Commissioning of the ATLAS Calorimetersat the Large Hadron Collider
and Prospects towards New Physics Search

Hideki Okawa
A thesis submitted to the Department of Physics
for the degree of Doctor of Philosophy
The University of Tokyo
February 2010

Abstract
The Large Hadron Collider (LHC) is the ﬁrst collider to probe the physics at theTeV-energy scale. At the TeV-energy scale, many models of new physics are expected, of
which the Supersymmetry (SUSY) and the Extra Dimensions are prospective examples.
In such models, model-independent searches with jets and large missing transverse energy(Missing ET ; ETmiss ) signatures are considered as eﬀective ways to probe the new physics
which may manifest itself at the TeV-energy scale. Jets and ETmiss are reconstructed
mainly from the calorimeter information, and thus it is of vast importance to understandthe performance of the ATLAS calorimeters with the real data even before the collisions.
The ATLAS liquid argon (LAr) and hadronic tile calorimeter (TileCal) systems havebeen undergoing an extensive period of in-situ commissioning, including a series of cosmic
ray runs which began in 2005 and continued in 2006-2008, and single beam runs that weretaken in September 2008. The data from these runs improve our understanding of thedetectors and also form a useful training ground for studying reconstruction algorithms
such as clusters, jets, and ETmiss in the calorimeter systems. In this thesis, commissioning
studies of the ATLAS calorimeter system are shown. The in-situ measurement of electronic noise in the calorimeters was performed for the ﬁrst time with the commissioning
data. The Topological Clustering (Topocluster) algorithm, which is one of the standard
calorimeter clustering algorithms used in ATLAS, was validated with the real data. The
updates on the algorithm were proposed and made in order to achieve the expected performance, considering the non-Gaussian feature of the electronic noise observed in the
TileCal. Jet and ETmiss reconstruction algorithms were validated with the data as well.
High energy deposits in the calorimeters were observed with the cosmic and the single
beam data. Those high energy phenomena were well reproduced and described by Monte
Carlo simulations. Such events become background to physics measurements during thecollision data taking, so should be considered and treated.
The quality of jet and ETmiss was investigated in detail, and the strategies to cope with
the non-collision background (i.e. hot and dead channels, cosmic muons, and beam halo
particles) were proposed. Here, so called the “cleaning” approach was adopted, whichis to reject the clusters, jets or even the events which contain large contamination of
the non-collision sources. The rejection power of the cleaning cuts were measured withthe real data, and the selection eﬃciency of the physics signals were investigated with
Monte Carlo simulation samples. Further investigations were performed with the “event
overlay” technique, which allows to overlap real data on Monte Carlo samples in orderto validate the cleaning cuts under more realistic conditions.
Finally, prospects towards new physics searches using “jet(s) + ETmiss ” event topologies were discussed. Strategies to search for the Large Extra Dimensions (LED) with amonojet signature, and R-parity conserving Supersymmetric Models with a “multi-jet +ETmiss ” event topology were investigated. Under the assumption of the integrated luminosity of 200 pb−1 at the center-of-mass energy of 10 TeV, the strategies were evaluatedwith realistic conditions.

To my mother, father, Anne,and my mentor in life, Dr. Daisaku Ikeda

Acknowledgments
I would like to thank my supervisor, Prof. Sachio Komamiya for many discussions
and suggestions that he has given me throughout these six years, and also letting me
participate in this great experiment, ATLAS. I appreciate Prof. Tomio Kobayashi for
accepting me in the ATLAS group at the International Center for Elementary Particle
Physics (ICEPP) at the University of Tokyo.
I appreciate Prof. Richard Teuscher for the discussions and feedback on the calorimeter commissioning and cosmic analyses. Dr. Robert Stanek, who was the former project
leader of the Tile Calorimeter group, accepted me as a member of the Tile Calorimeter
project. I would like to express my sincere gratitude for his kindness. I appreciate Dr.
Ana Henriques, who is the current Tile Calorimeter project leader, for letting me continue
my participation in the group. I thank Dr. Tancredi Carli for many fruitful discussions
related to jet and Missing ET studies using the commissioning data. I would like to thankDr. Michael Hauschild for becoming my supervisor at CERN. I enjoyed participating in
the Data Quality works and shifts at the ATLAS Control Room.
I would like to thank the professors, staﬀs, and students at ICEPP. I thank Prof. Shoji
Asai for the discussions on the studies related to the Supersymmetric Particle search andMissing ET studies. I thank Prof. Naoko Kanaya for the discussions on the studies ofMissing ET validation using the Minimum Bias and W events. I appreciate Prof. Ikuo
Ueda, Prof. Junichi Kanzaki, and Prof. Tatsuo Kawamoto for supporting me with mylife at CERN. I thank Prof. Masaya Ishino for the discussions and feedback on thetrigger conﬁgurations during the cosmic data takings. I would like to thank people in
the Japanese analysis team, Prof. Junichi Tanaka, Prof. Osamu Jinnouchi, Prof. Junichi
Kanzaki, Prof. Soshi Tsuno, Dr. Shimpei Yamamoto, Dr. Yousuke Kataoka, Dr. Koji
Terashi, Dr. Taiki Yamamura, Dr. Tatsuya Masubuchi, and Michiru Kaneda. I would
also like to thank Takayuki Sasaki and Kenta Oe for the discussions when I was new tothis experiment.
I appreciate the collaborators in the calorimeter commissioning. I would like to thankDr. Adam Gibson, Bin Guo, Gabe Rosenbaum, Dr. Bernhard Meirose, Dr. Adam
Yurkewitz, Dr. Andre Nepomuceno, Travis Bain, Luciano Filho, Louise Heelan and theexperts of the Tile and Liquid Argon Calorimeters for many discussions related to thecommissioning studies. It was a great pleasure to work with them.
I would like to thank all the ATLAS collaborators and the experts of the LHC, whom
I could not mention above, for making this tremendous experiment possible.
I also would like to express my thanks to the members of the Komamiya Laboratory.
I appreciate Prof. Yoshio Kamiya, Dr. Masakazu Kurata, Dr. Taiki Yamamura, Dr.
Taikan Suehara, Shinsuke Kawasaki, Shinya Sonoda, Tomoya Nakamura, Hakutaro Yoda,
Takashi Yamanaka, Masahiro Oroku, Go Ichikawa, Yohei Yamaguchi, and Yutaro Iiyama.
It was nice to have discussions and chatting with them.
I thank the secretaries at the Department of Physics and ICEPP, Ms. Kuniko Kono,
Ms. Masako Shiota, Ms. Ritsuko Anbiru, Ms. Naoko Kataoka, and Ms. Chieko Kurita
for their support.

I deeply appreciate my mother Toshiko, my father Takeshi, and Anne, for their eternallove and support. None of my accomplishments were possible without them. I would
like to thank my friends of the Soka Gakkai International (SGI) for the friendship andencouragement. Finally, I would like to express my sincere gratitude to my mentor in life,
Dr. Daisaku Ikeda, the president of the SGI for countless encouragement and guidance.
He has always been my role model, and I learned from him the importance of courage,
sincerity, patience, and endeavor without compromise.
I humbly would like to dedicate this thesis to my mother, father, Anne, and mymentor. Thank you so much.

Contributions of the Author to
the ATLAS Collaboration
This thesis is based on the research of the author in collaboration with the membersof the ATLAS Collaboration at the LHC.
The author has worked on developing methods for the data-driven estimation of theStandard Model background to Supersymmetric events, using all-hadronic “Multi-jet +ETmiss ” event topology (so called “No-lepton Search Mode” in ATLAS). This has been
the ﬁrst work done in ATLAS in regards to data-driven methods for the estimation of
background in Supersymmetric events. The author has also contributed to the development of the validation method of Missing Transverse Energy (Missing ET ; ETmiss ) at
the early stage of the experiment using the Minimum Bias and W events, and worked
on updating the Missing ET oﬄine monitor. The largest contribution of the author goesto the commissioning of the ATLAS calorimeters with the cosmic ray and single beamdata. The author was the ﬁrst member from the Japanese institutes to be involved in thecommissioning of the ATLAS calorimeters. The author has made a great contribution
to the international academic exchange. The author’s work was one of the ﬁrst works
that linked the gap between the detector performance and physics analysis studies withthe real data, such as the ﬁrst in-situ noise measurement of the calorimeter cells, theﬁrst validation of “Topological Clustering” algorithm (one of the major clustering algorithms in ATLAS) with the cosmic ray data, and the ﬁrst validation of jet algorithmwith cosmic and single beam data. The ﬁrst studies of jet and ETmiss quality with the real
data were done as well. Not only calorimeter information but also information from theInner Detector tracks and the muon spectrometer was investigated to to tag and reject
non-collision eﬀects. The impact of such non-collision background was investigated forvarious event topology searches using jets and ETmiss , with a strong emphasis on the
monojet signature expected from the Large Extra Dimensions and some SUSY models.
These works were presented at International Conferences, International Workshops,
the JPS (Japan Physics Society) conferences, the 21 COE (Center-of-Excellence) Symposium at the University of Tokyo, and 46 times at the ATLAS internal meetings (seeAppendix A for details).
The author contributed to shifts in the ATLAS Control Room for the Data Quality
Working Group (since the M5 Week, the 5th combined cosmic run in October 2007),
Tile Calorimeter Group, and Liquid Argon Calorimeter Group (since the M7 Week, the
7th combined cosmic run in May 2008). The author has experienced a referee for the
ATLAS public note related to the Tile Calorimeter studies. This vast contribution to thecollaboration is one of the characteristics of the author.

Contents
I

Introduction

23

1 Introduction
1.1 Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24
25

2 The Standard Model and Physics at the LHC
2.1 Construction of the Standard Model . . . . . . . . . . . . . . . . . . . .
2.2 Physics at the LHC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Higgs Particle Searches . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2 Supersymmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.3 Extra Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3 The Large Hadron Collider
3.1 Why the LHC? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 LHC Injector Complex . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 LHC Layout and Technology . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Experiments at the LHC . . . . . . . . . . . . . . . . . . . . . . . . . . .

40
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4 The ATLAS Detector
4.1 Inner Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Beam Conditions Monitor . . . . . . . . . . . . . . . . . . . . . .
4.1.2 Pixel Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.3 Semiconductor Tracker . . . . . . . . . . . . . . . . . . . . . . . .
4.1.4 Transition Radiation Tracker . . . . . . . . . . . . . . . . . . . .
4.2 Calorimeters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1 Electromagnetic Calorimeter . . . . . . . . . . . . . . . . . . . . .
4.2.2 Hadronic Calorimeter . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Muon Spectrometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.1 Muon Drift Tubes . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3.2 Cathode Strip Chamber . . . . . . . . . . . . . . . . . . . . . . .
4.3.3 Resistive Plate Chamber . . . . . . . . . . . . . . . . . . . . . . .
4.3.4 Thin-Gap Chambers . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Magnets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Forward Detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5.1 LUCID (LUminosity measurement using Cerenkov Integrating Detector) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

47
48
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7

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4.5.2 ALFA (Absolute Luminosity For ATLAS) Detector . . . . . . . .
4.5.3 Zero-Degree Calorimeter . . . . . . . . . . . . . . . . . . . . . . .
Trigger System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67
67
70

5 Energy Reconstruction in the ATLAS Calorimeters
5.1 Introduction: Physics of Calorimetry . . . . . . . . . . . . . . . . . . . .
5.1.1 Energy Loss of Charged Particles Through Matter . . . . . . . . .
5.1.2 Interactions of Electrons and Photons in Matter . . . . . . . . . .
5.1.3 Electromagnetic Cascade . . . . . . . . . . . . . . . . . . . . . . .
5.1.4 Hadronic Cascade . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Energy Reconstruction in LAr Calorimeters . . . . . . . . . . . . . . . .
5.2.1 Optimal Filtering Method and Coeﬃcients . . . . . . . . . . . . .
5.2.2 Ramp Factor: ADC → DAC . . . . . . . . . . . . . . . . . . . . .
5.2.3 The Mphys /Mcali Factor . . . . . . . . . . . . . . . . . . . . . . . .
5.2.4 The FDAC→μA Factor . . . . . . . . . . . . . . . . . . . . . . . . .
5.2.5 The FμA→MeV Factor . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 Energy Reconstruction in Tile Calorimeter . . . . . . . . . . . . . . . . .
5.3.1 Energy Calibration in Tile Calorimeter . . . . . . . . . . . . . . .
5.4 Hadronic Calibration in ATLAS Calorimeters . . . . . . . . . . . . . . .
5.4.1 H1-style Calibration . . . . . . . . . . . . . . . . . . . . . . . . .
5.4.2 Local Hadronic Calibration . . . . . . . . . . . . . . . . . . . . .

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II

91

4.6

Commissioning of the ATLAS Calorimeters

6 Cosmic and Single Beam Runs
6.1 Cosmic Runs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.2 Single Beam Runs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.3 LHC Accident . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.4 Data Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.4.1 Physics Streams during Cosmic Runs . . . . . . . . . . . . . . . .
6.4.2 Physics Streams during Single Beam Runs . . . . . . . . . . . . .
6.5 Runs Used in the Analyses . . . . . . . . . . . . . . . . . . . . . . . . . .

92
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96
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7 Calorimeter Measurement with Random Stream Data
7.1 Noise in the Calorimeters . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.1.1 Electronic Noise in TileCal . . . . . . . . . . . . . . . . . . . . . .
7.1.2 Electronic Noise in LAr . . . . . . . . . . . . . . . . . . . . . . .
7.2 Problematic Channels in the ATLAS Calorimeters . . . . . . . . . . . . .
7.2.1 TileCal Bad Channel Search . . . . . . . . . . . . . . . . . . . . .
7.3 Performance of Calorimeter Clusters . . . . . . . . . . . . . . . . . . . .
7.3.1 Sliding Window Algorithm . . . . . . . . . . . . . . . . . . . . . .
7.3.2 Topological Clustering . . . . . . . . . . . . . . . . . . . . . . . .
7.3.3 Performance of Topoclusters in Random Stream Data . . . . . . .
7.4 Performance of Missing ET in Random Stream Data . . . . . . . . . . . .

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7.4.1
7.4.2

Missing ET Algorithm in ATLAS . . . . . . . . . . . . . . . . . . 130
Performance of ETmiss in Data and Its Improvement . . . . . . . . 131

8 Calorimeter Measurement with Cosmic Ray Data
136
8.1 Cosmic Rays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
8.2 Cosmic Monte Carlo Simulation in ATLAS . . . . . . . . . . . . . . . . . 138
8.3 High Energy Events from Cosmics . . . . . . . . . . . . . . . . . . . . . . 140
8.4 Test of Object Reconstruction in the Calorimeters . . . . . . . . . . . . . 144
8.4.1 Topological Clusters in Cosmic Ray Data . . . . . . . . . . . . . . 144
8.4.2 Jet Reconstruction With Cosmic Ray Data . . . . . . . . . . . . . 148
8.4.3 Missing Transverse Energy Reconstruction Using Cosmic Ray Data 155
9 Quality of Calorimeter Objects
159
9.1 Quality Variables and Cleaning Cuts . . . . . . . . . . . . . . . . . . . . 160
9.1.1 Jet-level quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
9.1.2 Event-level Quality . . . . . . . . . . . . . . . . . . . . . . . . . . 175
9.1.3 Cleaning for Pure Cosmic Events . . . . . . . . . . . . . . . . . . 175
9.2 Event Overlay Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
9.2.1 Event Overlay Algorithm . . . . . . . . . . . . . . . . . . . . . . . 176
9.2.2 Investigations on Event Overlap of Cosmic Rays on Collision Events177

III

Prospects towards New Physics Search

182

10 Searches for New Physics with Jets and ETmiss Event Topologies
10.1 Jets & ETmiss Signatures from New Physics . . . . . . . . . . . . . . . . .
10.1.1 SUSY Search Modes in ATLAS . . . . . . . . . . . . . . . . . . .
10.2 Physics Objects and Algorithms . . . . . . . . . . . . . . . . . . . . . . .
10.2.1 Electrons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2.2 Muons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2.3 Jets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2.4 Missing ET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10.2.5 b-Tagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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11 Monojet Search for the Large Extra Dimensions
11.1 Monojet Signature from the Large Extra Dimensions . . . . . . . . . . .
11.1.1 Real KK-Graviton Emission in Large Extra Dimensions Model . .
11.2 Monte Carlo Simulation Samples and Data . . . . . . . . . . . . . . . . .
11.3 Event Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.3.1 Trigger Menus for the Monojet Search . . . . . . . . . . . . . . .
11.3.2 Absence of Reconstructed Leptons . . . . . . . . . . . . . . . . .
11.3.3 Selection Cuts on Jets . . . . . . . . . . . . . . . . . . . . . . . .
11.3.4 Non-collision Background Rejection . . . . . . . . . . . . . . . . .
11.3.5 Event Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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12 Multi-jet plus ETmiss Search for R-parity Conserving SUSY Models
12.1 Data-Driven Methods for Background Estimation . . . . . . . . . . . . .
12.2 Monte Carlo Simulation Samples and Data . . . . . . . . . . . . . . . . .
12.3 Trigger Menus for the Multi-jet Search . . . . . . . . . . . . . . . . . . .
12.4 Event Selection for No-lepton Search Mode . . . . . . . . . . . . . . . . .
12.5 Estimation of Background . . . . . . . . . . . . . . . . . . . . . . . . . .
12.5.1 Z(→ ν ν̄)+jets Background . . . . . . . . . . . . . . . . . . . . . .
12.5.2 W +jets Background . . . . . . . . . . . . . . . . . . . . . . . . .
12.5.3 tt̄+jets Background . . . . . . . . . . . . . . . . . . . . . . . . . .
12.5.4 QCD Multi-jet Background . . . . . . . . . . . . . . . . . . . . .
12.5.5 Non-collision Background . . . . . . . . . . . . . . . . . . . . . .
12.6 Systematic Uncertainties . . . . . . . . . . . . . . . . . . . . . . . . . . .
12.6.1 Systematics in Data-driven Methods . . . . . . . . . . . . . . . .
12.6.2 Systematics from Jet and ETmiss Performance . . . . . . . . . . . .
12.7 Background Subtraction and Signal Signiﬁcance . . . . . . . . . . . . . .
12.7.1 Eﬀective Mass Distributions . . . . . . . . . . . . . . . . . . . . .
12.7.2 Number Counting Approach . . . . . . . . . . . . . . . . . . . . .

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13 Conclusions

245

A Contributions in ATLAS Publications and Presentations
A.1 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.1.1 Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.1.2 ATLAS Note . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.1.3 Proceedings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.2 Public Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.3 Presentations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
A.3.1 International Conference Presentations . . . . . . . . . . . . . . .
A.3.2 ATLAS Workshop Presentations . . . . . . . . . . . . . . . . . . .
A.3.3 National Meetings and Symposiums . . . . . . . . . . . . . . . . .
A.3.4 ATLAS Internal Presentations . . . . . . . . . . . . . . . . . . . .

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B Optimal Filtering Method

253

C Missing ET Algorithm in ATLAS

257

D Functions in KK-Graviton Processes

260

E Monte Carlo Simulation Samples Used
261
E.1 Samples for Monojet Studies . . . . . . . . . . . . . . . . . . . . . . . . . 261
E.2 Samples for Multi-jet Studies . . . . . . . . . . . . . . . . . . . . . . . . 262
F Monte Carlo-based Estimation of Z(→ ν ν̄) BG for SUSY Searches

264List of Figures
2.1
2.2
2.3
2.4

2.6
2.7

Cross section and branching ratio of the Standard Model Higgs bosons .
Feynman diagrams of the Higgs productions . . . . . . . . . . . . . . . .
Sensitivity of ATLAS to the discovery of the Standard Model Higgs bosons
Expected Missing ET distributions of a benchmark Supersymmetric eventsand the Standard Model background . . . . . . . . . . . . . . . . . . . .
Experimental limits on the parameters of the modiﬁed Newtonian gravitational force . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Lower limits of MD in the Large Extra Dimension model . . . . . . . . .
Side-view of a simulated black hole event in ATLAS . . . . . . . . . . . .

37
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3.1
3.2
3.3
3.4

CERN and the LHC . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The LHC Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
LHC cryodipole in the arc section . . . . . . . . . . . . . . . . . . . . . .
LHC Cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41
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4.1 ATLAS Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Cross section of the ATLAS Detector . . . . . . . . . . . . . . . . . . . .
4.3 Inner Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Position of the Beam Conditions Monitor inside the inner detector . . . .
4.5 Semiconductor Tracker (SCT) module . . . . . . . . . . . . . . . . . . . .
4.6 ATLAS Calorimeter System . . . . . . . . . . . . . . . . . . . . . . . . .
4.7 EM Accordion Structure . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.8 Amount of dead material in front of the EM calorimeter . . . . . . . . .
4.9 Thickness of each accordion sampling layer in the EM calorimeter . . . .
4.10 TileCal Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.11 TileCal Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.12 Typical ﬁber bundles in TileCal . . . . . . . . . . . . . . . . . . . . . . .
4.13 HEC Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.14 FCAL Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.15 Muon Spectrometer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.16 Momentum resolution of the muon spectrometer . . . . . . . . . . . . . .
4.17 Muon Trigger System . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.18 Sideview of the muon spectrometer . . . . . . . . . . . . . . . . . . . . .
4.19 MDT Chamber . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.20 CSC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.21 RPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

48
49
51
51
52
53
55
56
56
58
58
59
60
60
61
62
62
63
64
64
65

2.5

11

33
33
34
35

4.22 TGC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.23 Schematic view of magnets . . . . . . . . . . . . . . . . . . . . . . . . . .
4.24 Forward Detectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.25 LUCID Detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.26 The layout of the ALFA Detector . . . . . . . . . . . . . . . . . . . . . .
4.27 Schematic view of the ZDC in the TAN . . . . . . . . . . . . . . . . . . .
4.28 The overview of the ATLAS trigger system . . . . . . . . . . . . . . . . .
4.29 Level-1 Trigger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

65
66
68
68
69
69
71
71

5.1
5.2

76

Stopping power for positive muons in copper . . . . . . . . . . . . . . . .
Fractional energy loss per radiation length in lead as a function of electron
or positron energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 Photon total cross sections as a function of energy in carbon and lead . .
5.4 Cell energy reconstruction formula in LAr . . . . . . . . . . . . . . . . .
5.5 Diagram of the Liquid Argon Calorimeter read-out electronics . . . . . .
5.6 Form of the ionization signal and the shaped ionization signal . . . . . .
5.7 ADC peaks and input DAC values . . . . . . . . . . . . . . . . . . . . . .
5.8 Pulse shape from calibration runs and physics runs . . . . . . . . . . . .
5.9 Diagram of the TileCal read-out electronics . . . . . . . . . . . . . . . .
5.10 Calibration in TileCal . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.11 Response to a CIS pulse of 2 pC and 560 pC . . . . . . . . . . . . . . . .
5.12 The cell response of electrons at 20◦ . . . . . . . . . . . . . . . . . . . . .

78
78
80
81
82
84
84
86
87
87
89

6.1
6.2
6.3

92
94
95

First Cosmic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Cosmic Data in Fall 2008 . . . . . . . . . . . . . . . . . . . . . . . . . . .
First Single Beam Data . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7.1 Contribution of electronic, pile-up and total noise . . . . . . . . . . . . . 102
7.2 Electronic and total noise of the calorimeters at the cell-level . . . . . . . 102
7.3 Deﬁnition of cells in the Tile calorimeter . . . . . . . . . . . . . . . . . . 103
7.4 Electronic noise in the TileCal Barrel at the cell-level in M5 run 29576 . 105
7.5 Energy distribution of TileCal cells . . . . . . . . . . . . . . . . . . . . . 105
7.6 Tile Calorimeter power distributions . . . . . . . . . . . . . . . . . . . . 106
7.7 Non Gaussian distribution of TileCal pedestal . . . . . . . . . . . . . . . 106
7.8 Correlation of electronic noise in TileCal LBA 16 . . . . . . . . . . . . . 108
7.9 Correlation of electronic noise among the drawers in TileCal PMT1 in LBA108
7.10 η-dependence of the electronic noise of the TileCal cells in Fall 2008 cosmic
data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.11 φ-dependence of the electronic noise of the TileCal cells in Fall 2008 cosmic
data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
7.12 Stability of the pedestals of the TileCal in Fall 2008 runs . . . . . . . . . 110
7.13 Stability of the Electronic noise of the TileCal in Fall 2008 runs . . . . . 111
7.14 Electronic noise of the LAr Calorimeter at the cell-level . . . . . . . . . . 112
7.15 Electronic noise and volume of calorimeter cells in LAr as a function of η 112
7.16 Stability of the pedestals of the Liquid Argon Calorimeter in Fall 2008 runs113

7.17 Stability of the electronic noise of the Liquid Argon Calorimeter in Fall2008 runs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
7.18 η-φ map of LAr and TileCal masked problematic channels . . . . . . . . 116
7.19 The distributions of EMTopoclusters in M5 run 29576 NIM0 stream . . . 117
7.20 Noise values of LBA PMT 6 for each drawer in M7 run 69373 from theRNDM0 stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
7.21 Mean energy of EBA PMT 29 in M7 run 69373 from the RNDM0 stream 121
7.22 Energy diﬀerence of PMT’s in the same cell (PMT 3 & 4 in LBA) in M7
run from the 69373 RNDM0 stream . . . . . . . . . . . . . . . . . . . . . 122
7.23 Energy distribution of PMT’s . . . . . . . . . . . . . . . . . . . . . . . . 122
7.24 Contributions of calorimeter cells to combined towers shown in schematicη − φ grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
7.25 Schematic view of Topological Clustering algorithm . . . . . . . . . . . . 125
7.26 Sensitivity of Topoclusters to noise database . . . . . . . . . . . . . . . . 126
7.27 Ratio of the RMS and the Gaussian σ . . . . . . . . . . . . . . . . . . . 127
7.28 Ratio between the conﬁdence level and the Gaussian sigma . . . . . . . . 127
7.29 Number of reconstructed Topoclusters . . . . . . . . . . . . . . . . . . . 128
7.30 η distributions of reconstructed Topoclusters . . . . . . . . . . . . . . . . 128
7.31 η − φ distributions in December 08 reprocessing data and new reconstruction129
7.32 “MET Base” Missing EX and EY from random stream data and toy Monte
Carlo samples from a Gaussian noise model . . . . . . . . . . . . . . . . 132
7.33 “MET Base” ETmiss from random stream data and toy Monte Carlo samples
from a Gaussian noise model . . . . . . . . . . . . . . . . . . . . . . . . . 132
7.34 “MET Topo” Missing EX and EY from random stream data and toy Monte
Carlo samples from a Gaussian noise model . . . . . . . . . . . . . . . . 134
7.35 “MET Topo” ETmiss from random stream data and toy Monte Carlo samples from a Gaussian noise model . . . . . . . . . . . . . . . . . . . . . . 134
7.36 “MET Topo” Missing EX and EY from random stream data with andwithout updated Topocluster algorithm and toy Monte Carlo samples froma Gaussian noise model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
7.37 “MET Topo” ETmiss from random stream data with and without updated
Topocluster algorithm and toy Monte Carlo samples from a Gaussian noisemodel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
8.1
8.2
8.3
8.4
8.5
8.6
8.7
8.8
8.9

Schematic view of air shower development . . . . . . . . . . . . . . . . .
Vertical ﬂuxes of cosmic rays in the atmosphere . . . . . . . . . . . . . .
Schematic view of cosmic Monte Carlo simulation . . . . . . . . . . . . .
Layout of ATLAS cavern at Point 1 . . . . . . . . . . . . . . . . . . . . .
Origin of cosmic muons at the surface in the Monte Carlo simulation . .
Impact point of cosmic muons on surface in cosmic ray data . . . . . . .
Momentum of generated muons that reached the ATLAS detector . . . .
Momentum distribution of stand-alone muon spectrometer tracks in cosmic
data and Monte Carlo . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Sum ET distribution from cosmic ray data and Monte Carlo simulation .

137
137
139
139
141
142
142
143
144

8.10 Sum ET distribution in each calorimeter partition in run 90272 L1Calostream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
8.11 A TeV event in run 90272 L1Calo stream . . . . . . . . . . . . . . . . . . 146
8.12 An air shower event in run 90272 L1Calo stream . . . . . . . . . . . . . . 146
8.13 The number of EMTopoclusters in M5 run 29576 NIM0 stream . . . . . . 147
8.14 Energy of Tile Topoclusters for M5 run 29576 . . . . . . . . . . . . . . . 148
8.15 Schematic overview of jet reconstruction in ATLAS . . . . . . . . . . . . 150
8.16 Number of reconstructed cone-algorithm jets (ET > 7 GeV) in cosmic data 151
8.17 Number of reconstructed cone-algorithm jets (ET > 20 GeV) in cosmic data151
8.18 Transverse energy of reconstructed jets in run 90272 . . . . . . . . . . . . 152
8.19 Reconstructed jets’ η-φ distribution for run 90272 CosmicMuons stream . 152
8.20 Reconstructed jets’ η-φ distribution for run 90272 L1Calo stream . . . . 153
8.21 Comparison of Jet distributions in run 90272 L1Calo stream with cosmic
Monte Carlo samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
8.22 Comparison of energy distributions of bremsstrahlung photons from cosmic
muons and reconstructed “fake” jets in cosmic Monte Carlo samples . . . 155
8.23 ETmiss from MET Base and MET Topo algorithm in run 90272 L1Calo stream157
8.24 Sum ET vs ETmiss as measured in M8 run 77585. . . . . . . . . . . . . . . 158
9.1 Jet EM fraction in cosmic data and QCD Monte Carlo . . . . . . . . . .
9.2 Jet EM fraction from Cone4H1TowerJets and Cone4H1TopoJets . . . . .
9.3 Correlation plots of transverse energy of jets and jet EM fraction . . . . .
9.4 Examples of events with mid-EM fraction . . . . . . . . . . . . . . . . .
9.5 Inner Detector event display from cosmic run 90270 . . . . . . . . . . . .
9.6 Number of Tracks in cosmic data and QCD dijet Monte Carlo . . . . . .
9.7 Numbers of tracks associated with jets . . . . . . . . . . . . . . . . . . .
9.8 Number of clusters in jets . . . . . . . . . . . . . . . . . . . . . . . . . .
9.9 Number of clusters in Cone4H1TopoJets . . . . . . . . . . . . . . . . . .
9.10 Number of cells in Cone4H1TopoJets from run 90272 L1Calo stream and
QCD di-jet Monte Carlo samples . . . . . . . . . . . . . . . . . . . . . .
9.11 Correlation of jet quality variables . . . . . . . . . . . . . . . . . . . . . .
9.12 Event quality from jet information . . . . . . . . . . . . . . . . . . . . .
9.13 Preliminary cleaning on Cone4H1TowerJets and Cone4H1TopoJets . . .
9.14 Jet EM fraction in overlaid samples . . . . . . . . . . . . . . . . . . . . .
9.15 Number of Topolusters in Jets in overlaid samples . . . . . . . . . . . . .
9.16 Number of associated tracks in jets in overlaid samples . . . . . . . . . .
9.17 Jet charge fraction in overlaid samples . . . . . . . . . . . . . . . . . . .

162
164
165
166
168
169
170
172
173
173
174
176
177
178
179
179
180

10.1 Distribution of jet b-tagging weight . . . . . . . . . . . . . . . . . . . . . 187
11.1 Leading order Feynman diagrams of KK-graviton emission with monojet
signature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
11.2 Cross section of LED monojet processes in SHERPA as a function of thenumber of extra dimensions . . . . . . . . . . . . . . . . . . . . . . . . . 191
11.3 Eﬃciency of jet triggers at the Event Filter level . . . . . . . . . . . . . . 193

11.4 Eﬃciency of ETmiss triggers at the Event Filter level . . . . . . . . . . . . 193
11.5 Number of reconstructed electrons from LED KK-graviton signals andbackground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
11.6 Number of reconstructed combined and MS-standalone muons from LED
KK-graviton signals and background . . . . . . . . . . . . . . . . . . . . 195
11.7 Leading jet distributions of LED KK-graviton signals and background . . 198
11.8 Second leading jet distributions of LED KK-graviton signals and background199
11.9 Number of jets for events with the leading jet pT > 100 GeV . . . . . . . 200
11.10EM fraction of the leading jet from LED KK-graviton signals and background202
11.11Charge fraction of the leading jet from LED KK-graviton signals and background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
11.12Expected ETmiss distributions in monojet signature with LED KK-Graviton
signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
12.1 ETmiss distributions of SUSY signals and Standard Model background after
selection cuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
12.2 ETmiss and eﬀective mass distributions after No Lepton Mode selection cuts 213
12.3 Invariant mass of dimuons . . . . . . . . . . . . . . . . . . . . . . . . . . 215
12.4 Trigger eﬃciency of reconstructed muons . . . . . . . . . . . . . . . . . . 217
12.5 Identiﬁcation eﬃciency of muons . . . . . . . . . . . . . . . . . . . . . . 218
12.6 Eﬃciency of Z reconstruction using dimuons as a function of pT . . . . . 219
12.7 Estimation of Z(→ ν ν̄) background using Z(→ μ+ μ− ) events . . . . . . . 220
12.8 Estimation of W (→ τ ντ ) background using one-lepton control samples . 223
12.9 Estimation of W (→ μνμ ) no-lepton background using W (→ μνμ ) onelepton control samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224
12.10Estimation of tt̄(→ bb̄τ ντ qq) background using tt̄(→ bb̄μνμ qq) one-lepton
control samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
12.11ETmiss distributions of SUSY signals and Standard Model background after
selection cuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
12.12Eﬀects of jet and ETmiss systematics on ETmiss distribution of SU3 . . . . . 237
12.13ETmiss and eﬀective mass distribution of SU3 signals and Standard Modelbackground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
12.14ETmiss and eﬀective mass distribution of SU4 signals and Standard Modelbackground . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240
12.15Eﬀective mass distribution of SU4 signals . . . . . . . . . . . . . . . . . . 241
12.16Signiﬁcance of the SUSY signal SU3 scanned over various ETmiss and Mef fthresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242
12.17Signiﬁcance of the SUSY signal SU4 scanned over various ETmiss and Mef fthresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243
F.1 Overview of quasi-date-driven estimation scheme for Z(→ ν ν̄) background 265
F.2 Normalization factor determined by comparing Z(→ l+ l− ) Monte Carloand pseudo-data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
F.3 Estimation of Z(→ ν ν̄) background using quasi-data-driven method . . . 266

List of Tables
2.1

Elementary Particles in the Standard Model . . . . . . . . . . . . . . . .

31

3.1

LHC Parameters for proton-proton collisions . . . . . . . . . . . . . . . .

45

4.1

Primary trigger menus proposed for the luminosity 1031 cm−2 s−1 at thecenter-of-mass energy 10 TeV . . . . . . . . . . . . . . . . . . . . . . . .
Primary trigger menus proposed for the luminosity 1031 cm−2 s−1 at thecenter-of-mass energy 10 TeV . . . . . . . . . . . . . . . . . . . . . . . .

73

6.1
6.2

ATLAS Cosmic Ray Milestone Weeks and Beyond. . . . . . . . . . . . .
Level-1 Calorimeter menus used in the cosmic run 90272 L1Calo stream

93
97

7.1
7.2
7.3
7.4

Numbers and percentages of masked problematic cells for each partition .
Categories of the problematic LAr channels . . . . . . . . . . . . . . . .
Categories of the problematic TileCal channels . . . . . . . . . . . . . .
Mean of reconstructed number of Topoclusters in each calorimeter region

116
118
119
127

8.1
8.2
8.3

Proﬁle of generated cosmic Monte Carlo simulation samples . . . . . . . 138
Triggered event rate of high energy events in run 90272 L1Calo stream . 147
Triggered event rate for numbers of TopoJets per event . . . . . . . . . . 151

9.1
9.2

Deﬁnition of QCD dijet Monte Carlo samples . . . . . . . . . . . . . . .
The breakdown of the ratio of jets remaining after diﬀerent cut values on
the EM fraction for Cone4H1TowerJets . . . . . . . . . . . . . . . . . . .
The breakdown of the ratio of jets remaining after diﬀerent cut values on
the EM fraction for Cone4H1TopoJets . . . . . . . . . . . . . . . . . . .
The breakdown of the ratio of jets remaining after diﬀerent cut values on
the number of associated tracks in jets . . . . . . . . . . . . . . . . . . .
The ratio of jets remaining after diﬀerent cut values on the jet size . . . .
Mean number of cells in jets . . . . . . . . . . . . . . . . . . . . . . . . .
The breakdown of the ratio of jets remaining after diﬀerent cut values on
the number of cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The breakdown of the ratio of jets remaining after diﬀerent cut values on
the EM fraction for Cone4H1TopoJets in overlaid samples . . . . . . . .
The breakdown of the ratio of jets remaining after diﬀerent cut values on
the number of clusters in Cone4H1TopoJets in overlaid samples . . . . .

4.2

9.3
9.4
9.5
9.6
9.7
9.8
9.9

16

74

161
163
163
169
171
171
171
178
178

9.10 The breakdown of the ratio of jets remaining after diﬀerent cut values on
the number of associated tracks for Cone4H1TopoJets in overlaid samples 179
9.11 The breakdown of the ratio of jets remaining after diﬀerent cut values on
the charge fraction for Cone4H1TopoJets in overlaid samples . . . . . . . 181
11.1 Cross section of LED processes in SHERPA . . . . . . . . . . . . . . . . 191
11.2 Number of events with no reconstructed electrons . . . . . . . . . . . . . 197
11.3 Number of events with no reconstructed muons . . . . . . . . . . . . . . 197
11.4 Number of events with second leading jet veto . . . . . . . . . . . . . . . 201
11.5 Number of events after EM fraction and charge fraction cuts . . . . . . . 206
11.6 Number of events remaining after each selection cut for background processes207
11.7 Number of events remaining after each selection cut for the LED signals . 207
12.1 Cross section and proﬁle of the benchmark SUSY mSUGRA samples . . 210
12.2 Particle mass spectrum for the benchmark mSUGRA points . . . . . . . 210
12.3 Breakdown of events after event selection of no-lepton search mode . . . 214
12.4 Breakdown of events after event selection of no-lepton search mode . . . 214
12.5 Number of events of Z(→ ν ν̄) background and estimation . . . . . . . . . 221
12.6 Origin of W background to no-lepton search mode . . . . . . . . . . . . . 221
12.7 Number of events of W (→ τ ντ ) background and estimation . . . . . . . . 222
12.8 Number of events of W (→ μντ ) no-lepton background and estimation . . 225
12.9 Origin of semileptonic tt̄ background to no-lepton search mode . . . . . . 226
12.10Number of events of tt̄ no-lepton background with τ inside the acceptanceand estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
12.11Breakdown of the Standard Model background and cosmic-QCD overlay
events after event selection of no-lepton search mode . . . . . . . . . . . 230
12.12Systematic uncertainties assumed for data-driven estimation of Z(→ ν ν̄)background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
12.13Systematic uncertainties assumed for data-driven estimation of W (→ τ ντ )background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
12.14Systematic uncertainties assumed for data-driven estimation of W (→ μνμ )background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
12.15Systematic uncertainties assumed for data-driven estimation of tt̄(→ bb̄τ ντ qq)
background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
12.16Breakdown of events after event selection of no-lepton search mode withconsiderations on jet & ETmiss systematics . . . . . . . . . . . . . . . . . . 235
12.17Uncertainty of selection eﬃciency from jet and ETmiss systematics for theStandard Model background . . . . . . . . . . . . . . . . . . . . . . . . . 235
12.18Breakdown of SUSY signal events after event selection of no-lepton searchmode with jet & ETmiss systematics . . . . . . . . . . . . . . . . . . . . . . 236
12.19Uncertainty of selection eﬃciency from jet and ETmiss systematics for the
SUSY signals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
12.20Signiﬁcance of mSUGRA benchmark signals . . . . . . . . . . . . . . . . 244
C.1 ETmiss quantities in ATLAS. . . . . . . . . . . . . . . . . . . . . . . . . 259

E.1
E.2
E.3
E.4

Cross section and proﬁle of Standard Model samples . . . . . . . . . . . . 261
Cross section and proﬁle of Monte Carlo samples for Z events . . . . . . 262
Cross section and proﬁle of Monte Carlo samples for Z events . . . . . . 263
Cross section and proﬁle of Monte Carlo samples for Top and QCD events 263

Acronyms and Terms• ADC: Analog to Digital Converter.
• ADD: The Large Extra Dimension model by Arkani-Hamed, Dimopoulos, andDvali.
• AOD: Analysis Object Data. These are the data ﬁles that contain information ofreconstructed physics quantities and are used for physics analyses in ATLAS.
• Athena: Software framework used in ATLAS.
• ATLAS: A Toroidal LHC Apparatus. One of the two general purpose detectors at
the LHC.
• BCID: Bunch Crossing Identiﬁer. It is the number that deﬁnes which bunch crossing an event belongs to.
• BCM: Beam Conditions Monitor.
• BH: Black Hole. In this thesis, the acronym indicates the extradimensional miniblack holes expected to be produced at the LHC in some extra dimensional models.
• BSM: Physics beyond the Standard Model.
• CIS: Charge Injection System of the Tile Calorimeter. It is the calibration system
to derive the calibration constants which convert the ADC counts to pC.
• Cone4H1TopoJets: Jets reconstructed by the cone algorithm with the cone sizeof 0.4 and the Topoclusters as the inputs for the reconstruction. H1-style hadroniccalibration was adopted for the energy reconstruction.
• Cone4H1TowerJets: Jets reconstructed by the cone algorithm with the cone
size of 0.4 and the combined towers as the inputs for the reconstruction. H1-style
hadronic calibration was adopted for the energy reconstruction.
• CRC: Cyclic Redundancy Check.
• CTP: Central Trigger Processor.
• DAC: Digital to Analog Converter.
• DAQ: Data Acquisition System.
• DCS: Detector Control System. It is the TDAQ system that controls the ATLAS
sub-detectors, their common interfaces, and communication with CERN services(cooling, ventilation, electricity distribution, safety, etc.) and LHC accelerator.

• DMU: Data Management Unit.
• DSP: Digital Signal Processor.
• EBA: A-side (positive η) of the Extended-Barrel of the Tile Calorimeter.
• EBC: C-side (negative η) of the Extended-Barrel of the Tile Calorimeter.
• EF: Event Filter. It is the highest-level trigger in ATLAS.
• EMB: Barrel electromagnetic calorimeter.
• EMEC: End-cap electromagnetic calorimeter.
• ESD: Event Summary Data. These are data ﬁles used in ATLAS with more information stored than the AOD’s. They contain suﬃcient information to re-run thereconstruction, and are also used for detailed detector commissioning analyses.
• FCAL: The forward calorimeter.
• H1 calibration: Cell energy calibration based on the energy density to account for
the non-linear energy response of calorimeters to hadrons. H1 is one of the HERA
(e-p collider) experiments at DESY.
• HEC: The hadronic end-cap calorimeter.
• HLT: High-Level Trigger; an inclusive name for the Level-2 trigger and Event
Filter.
• HV: High voltage.
• J0-J8: Monte Carlo simulation samples for QCD dijets. The samples are slicedby the transverse momentum of the leading parton. Nine simulation samples exist
from J0 to J8, where the numbers indicate the “n’th” slice. See Chapter 9 andAppendix E for more detail.
• KK: Kaluza-Klein. Particles excited due to the boundary conditions of the extra
dimensions.
• L1Calo: The Level-1 Calorimeter Trigger.
• LAr: The Liquid Argon Calorimeters in ATLAS. Inclusive term for the electromagnetic, the end-cap hadronic and the forward calorimeter using the Liquid Argon
technique.
• LED: The Large Extra Dimensions model proposed by N. Arkani-Hamed, S. Dimopoulos, G. Dvali.
• LBA: A-side (positive η) of the Long-Barrel of the Tile Calorimeter.
• LBC: C-side (negative η) of the Long-Barrel of the Tile Calorimeter.

• LHC: The Large Hadron Collider.
• LSP: The lightest Supersymmetric particles.
• LUCID: LUminosity measurement using Cerenkov Integrating Detector. It is thedetector that measures the relative luminosity.
• LV: Low voltage.
• M1-M8: Milestone Weeks. A series of cosmic ray runs in ATLAS motivated to
combine and operate all the ATLAS detector components available at the time ofrunning. M1 corresponds to the ﬁrst Milestone Week.
• MBTS: Minimum Bias Trigger Scintillator.
• Missing ET (ETmiss ): Missing transverse energy. The variable which quantiﬁes
the imbalance of the transverse momentum using the calorimeters and the muon
spectrometer.
• MET Base, MET Topo, MET RefFinal: Reconstruction schemes of Missing
ET . See Section 7.4.1.
• OFC: Optimal Filtering Coeﬃcients. The coeﬃcients used for energy reconstruction from the calorimeter signals with the Optimal Filtering Method.
• Pixel: Pixel Detector. It is one of the sensors of the inner detector in ATLAS,
placed closest to the interaction point.
• PMT: Photo-multiplier. A device to detect light with high gain and sensitivity. It
is used as the sensor in the ATLAS Tile Calorimeter.
• Point 1-8: Eight numerical names assigned to the underground sites along the
LHC ring with equal intervals. Point 1 is where ATLAS locates.
• PS, PSB, PSEC: Presampler, barrel presampler, and end-cap presampler.
• QCD: Quantum Chromodynamics. It is the quantum gauge ﬁeld theory that
describes the strong interaction.
• QED: Quantum Electrodynamics. It is the quantum gauge ﬁeld theory that describes the electromagnetic interaction.
• RNDM: Random-triggered event stream.
• RPC: Resistive Plate Chamber. It is the barrel muon trigger chamber.
• RS: The warped extra dimension model by L. Randall and R. Sundrum.
• SCT: Semiconductor Tracker. It is one of the sensors of the inner detector in ATLAS, and provide precision tracking information in addition to the Pixel Detector.

• SFO: Sub-Farm Output. The TDAQ system component which receives accepted
events from the Event Filter and send them to the permanent oﬄine event storage.
• SM: The Standard Model of particle physics.
• STACO: One of the standard muon reconstruction algorithms. It combines Inner Detector tracks and Muon Spectrometer tracks by considering their parameter
vectors, and calculating the χ2 of track matching quality.
• SUSY: Supersymmetry.
• TDAQ: ATLAS Trigger and DAQ (including DCS).
• Tier-0, Tier-1, Tier-2: Tier is the hierarchy of the Grid sites. Tier-0 is theinitial site located at CERN, responsible for storing the raw data from the experiments, and running the ﬁrst reconstruction of data. Tier-1 takes care of storing
reconstructed data, and also re-running the reconstruction from data. Tier-2 is
responsible for generating Monte Carlo simulation samples, and also for storingdata.
• TileCal: ATLAS Tile Calorimeter. It is the barrel hadronic calorimeter using theplastic scintillator tiles.
• TGC: Thin Gap Chamber. It is the end-cap muon trigger chamber.
• Topoclusters: Calorimeter clusters using the topological clustering algorithm. It
makes use of the signiﬁcance of the energy deposit in each calorimeter cell (seeChapter 6 for more detail).
• TRT: Transition Radiation Tracker. It is one of the sensors in the inner detector
in ATLAS, using the straw chamber technique.

22Part I
Introduction

23

Chapter 1
Introduction
The Large Hadron Collider (LHC) is the ﬁrst collider to probe the physics at the TeVenergy scale. Its primary motivations are to search for the last missing piece of theStandard Model, the Higgs boson, and to search for the physics beyond the Standard
Model.
At the TeV-energy scale, many models beyond the Standard Model are expected,of which the existence of the Supersymmetry (SUSY) and the Extra Dimensions areprospective examples. The lightest Supersymmetric particles (LSP’s) from SUSY models
and Kaluza-Klein particles from Extra Dimensions models are considered as candidatesof the dark matter in the Universe. Those particles are very likely to leave large Missing
Transverse Energy (Missing ET , ETmiss ), because they only interact weakly with theordinary matter. In a large part of such cases, jets with high transverse momentum (pT )are expected to accompany (e.g. through cascade decays of squarks and gluinos down to
the LSP in the R-parity conserving SUSY models).
In this thesis, the emphasis is laid on the searches for physics beyond the Standard
Model with jet and ETmiss signatures. For the reconstructions of jets and ETmiss , thecalorimeters play signiﬁcantly important roles. Calorimeters are essential tools for almost
any kind of physics measurement in collider physics, and we cannot emphasize too muchtheir importance.
In order to achieve successful searches for new physics with the early data, a largepart of this thesis is dedicated to the commissioning studies of the calorimeters in the
ATLAS (A Toroidal LHC Apparatus) detector. Here, the current understandings on
the performance of the ATLAS calorimeters were shown, and the improvements made inorder to undertake successful analyses were mentioned. The data from the cosmic rayruns were extensively used in this thesis. They do not only serve as probes to understandthe response of the calorimeters, but also allow us to validate the ATLAS software, andto understand and estimate the contribution of the cosmic muons themselves during thecollision data taking, because they will be non-negligible background in various physics
measurements. Strategies to cope with such non-collision background were established,
and their performance in some of the new physics searches were mentioned.
Strategies to search for the physics beyond the Standard Model with jet and ETmiss signatures were proposed at the ﬁnal part of the thesis. The emphasis was put on the un-24derstanding and solutions to cope with the non-collision sources during the search. The
methods to estimate the Standard Model background in data-driven ways were proposed
as well. These studies are especially essential for new physics searches with early data.

1.1

Outline of the Thesis

This thesis consists of three parts. The introductory part (this chapter to Chapter 5),
the commissioning part (Chapter 6 - 9), and the prospective part (Chapter 10 - 13).
In Chapter 2, a brief review of the Standard Model was given, and some of thecandidates of physics beyond the Standard Model were mentioned, which are important
physics motivations of the LHC experiments. Chapter 3 brieﬂy describes the LHC andthe accelerator complex. Chapter 4 gives an overview of the ATLAS detector and its
performance. Since a large fraction of this thesis is dedicated to the calorimeter studies
with the commissioning data, the energy and time reconstruction in the calorimeters is
described in a separate chapter (Chapter 5).
The proﬁle of the cosmic and single beam runs recorded since 2005 and up to the fall
and winter of 2008 is described in Chapter 6. Commissioning studies of calorimeters were
performed with random-triggered (random stream) and cosmic data, where the former
is presented in Chapter 7, and the latter in Chapter 8. The analyses of random stream
data allowed us to understand the detector conditions such as the electronic noise andproblematic channels such as so called the “dead” and “hot” channels. Observations ofhigh energy cosmic rays with the calorimeters allowed for the further validation of thedetector systems as well as to deepen our understandings towards the rate and eﬀects
from the high energy cosmic muons.
Chapter 9 presents our proposals on how to reject the fake energy components comingfrom non-collision background. The quality of reconstructed calorimeter objects wasinvestigated with the real and Monte Carlo cosmic data. A comparison with collision
Monte Carlo samples such as the QCD dijet, lead to the proposals of “cleaning cuts” tobe performed to reject non-collision background, still keeping high enough eﬃciency forthe collision events.
Chapter 10 overviews the motivations of the new physics searches with jet andETmiss event topologies considered in this thesis.
Chapter 11 shows the expected eﬀects of cosmic ray events on monojet event topology.
New physics searches with jets and Missing ET (ETmiss ) can be contaminated with noncollision background, but the use of cleaning cuts overcome the situation. Contributions
from the cosmic rays are estimated from the real data, and the rejection power of cleaning
cuts were shown.
Chapter 12 shows the strategy toward the multi-jet search for R-parity conserving
Supersymmetry. Data-driven methods to estimate the Standard Model background areproposed. An estimation of cosmic ray background to the search is done with the “overlay” of the real cosmic data and QCD multi-jet Monte Carlo simulation.
25

The closing chapter overviews the thesis and presents conclusions.

26

Chapter 2
The Standard Model and Physics at
the LHCIn this chapter, a brief overview of the Standard Model is given, followed by an introduction of some of the prospective physics to be searched at the LHC.

2.1

Construction of the Standard Model

The particle physics has experienced a tremendous success in explaining many parts ofthe fundamental aspects of nature.
There are four known fundamental interactions; the electromagnetic, strong, weak,
and gravitational interactions. All of the interactions except for the gravitational force
are described by the gauge theories of quantum ﬁelds (the gravitational force can also be
regarded as a gauge theory, but its quantization is not yet achieved). Those theories arecalled the Standard Model as a whole. In the Standard Model, leptons and quarks are
considered as the elementary particles that make up the matter around us, and gaugebosons as the mediators of the fundamental interactions (Table 2.1). The 20th century
has been a revolutionary era for the construction of the Standard Model. Here, the brief
overview of the construction of the Standard Model is given.
The ﬁrst attempt to describe a particle interaction by a boson exchange was done by
Yukawa [1], which he introduced a new particle (meson) to explain the strong interaction
between nucleons inside atoms,


1 ∂2
2
Δ − 2 2 − λ U(x) = 0 .
(2.1)
c ∂t
Here, U(x) is the meson ﬁeld, λ has the dimension of [length]−1 and related to the mass), and x is the space-time coordinate. The meson, now known asof the meson (m = λcthe pion (π) was discovered in cosmic rays after the discovery of the muon (which at ﬁrst
was expected as Yukawa’s meson) [2][3], and proved the validity of the theory. Though
the theory is now considered as an eﬀective theory and not fundamental, it opened up
a way to the subsequent works to describe the fundamental interaction through particle
27

exchanges, and thus the construction of the Standard Model. Nambu recalls that Yukawa
founded the modern formalism of particle physics in a theoretical way, whereas Lawrence
played the same role in the experimental realm through the construction of cyclotrons [4].
The ﬁrst success of renormalizable quantum gauge ﬁeld theories was accomplishedin describing the electromagnetic force, which is called the Quantum Electrodynamics(QED) [5]-[7]. QED has a Lagrangian invariant under U(1) local gauge transform,
ψ(x) → eiα(x) ψ(x) ,(2.2)

where ψ(x) is a Dirac ﬁeld, x is the space-time coordinate, and α(x) is the phase to bechosen arbitrarily. The Lagrangian density consists of the terms from the gauge ﬁeld Aμ ,and the Dirac ﬁeld ψ and interactions between the Dirac and gauge ﬁelds,1
L = − Fμν F μν + ψ̄(iγ μ Dμ − m)ψ ,4
where Fμν is the ﬁeld strength,Fμν = ∂μ Aν − ∂ν Aμ

(2.3)

(2.4)

and Dμ is the covariant derivative,
Dμ = ∂μ − ieAμ .

(2.5)

The mass term of the gauge ﬁeld M 2 Aμ Aμ (M: mass of the gauge boson) is prohibitedby the gauge invariance, and assures the massless feature of the photons. The theory
allows to describe and predict the electromagnetic interaction among particles. It has
been tested throughout a wide energy range, and demonstrated a powerful prediction
power.
A rich spectroscopy of hadron states led to the speculations to describe hadrons by
more fundamental particles: the quarks (only the three lightest quarks; u, d, and s were
proposed at ﬁrst) in an SU(3) group [8]. The quarks were considered to carry spin 1/2,
with fractional charges. The dilemma to explain a baryon state like Δ++ with spin 3/2
inspired an additional quantum number called the color, where the quarks are expected
to have three diﬀerent states (the name came from an analogy to the color of light) [9].
The hypothesis of the new quantum number was supported by the observed rate ofπ 0 → γγ and e+ e− annihilation into hadrons. In the deep-elastic electron-nucleon [10] andneutrino-nucleon [11] scattering experiment, the existence of point-like particles inside theprotons were conﬁrmed, which lead to the parton description [12]. Partons were invented
as constituents of the protons, independently from the quark description. The three-jet
events from e+ −e− annihilations observed in PETRA experiments showed the evidence ofthe gluons [13]. Partons are now considered as quarks and gluons, and their interactions
are described by a non-commutative gauge theory (Yang-Mills theory [14]) in an SU(3)group. It is called the Quantum Chromodynamics (QCD). The Lagrangian density of
QCD consists of terms from gluon gauge ﬁelds, Dirac ﬁelds of quarks (qi : quark ﬁeld; i:index for ﬂavors), and a term to ﬁx the gauge,
1 a aμν F+q̄i (iγ μ Dμ − mi )qi + Lgauge-fixing .
(2.6)
L = − Fμν
4
i
28

a
In equation (2.6), Fμν
is the ﬁeld strength of the gluons (Aaμ : gluon ﬁeld; a, b, c = 1 ∼ 8:
number of freedom from gluons; g: the strong coupling constant; f abc : the structure
constant of SU(3)),
Fμν = ∂μ Aaν − ∂ν Aaμ − gf abc Abμ Acν ,(2.7)

and Dμ is the covariant derivative,
Dμ = ∂μ − igAaμ T a ,(2.8)

where T a is the generator of SU(3), satisfying,[T a , T b ] = if abc T c .

(2.9)

The short range feature of the strong interaction was once an obstacle to adopt the
non-Abelian gauge theory, since the gauge bosons must be massless. The problem wasovercome by the notion of the asymptotic freedom [15] and conﬁnement, where the former
also allowed for the perturbative calculation of the strong interaction at high energy, andmade the QCD as a predictive theory. Though quarks and gluons are not detected independently due to the conﬁnement and only hadrons are observed, their existence is now
taken for granted due to the success of the theory in explaining numerous experimental
results.
Ascertaining the non-conservation of parity in the weak interaction [16] lead to thedescription of the “V-A” theory [17], where the weak current J μ consisted of vector andaxial-vector component, such as,
1
J μ (x) = u¯ν γ μ (1 − γ 5 )ue
2(2.10)

for example (uν , ue are the spinors of the electon neutrino and the electron). An eﬀective
theory was established to describe the weak force by a four-point Fermi interaction using
the V-A current,
4GF
M = √ J μ Jμ†(2.11)
2and allowed to calculate the amplitude M for known weak interactions such as the
β-decay of nucleons and the Michel decay of muons at the leading order. Higher order
calculations were not possible due to its non-renormalizability. The adoption of the YangMills theory in weak interaction seemed impossible at ﬁrst, because the gauge bosons in
the theory must be massless as mentioned above.
The breakthrough was achieved by introducing a spontaneous symmetry breaking
mechanism, and the uniﬁed description of the electromagnetic and weak interactions
by an SU(2)L × U(1)Y group, now called the the Glashow-Weinberg-Salam (GWS)
model [18]. For the origin of the electroweak symmetry breaking, the Higgs mechanism
was considered as the most probable [19][20]. The gauge term of the GWS Lagrangian29

density is described as follows,
1
Lgauge = − (∂μ Aaν − ∂ν Aaμ + gabc Abμ Acν )241− (∂μ Bν − ∂ν Bμ )2
4

2
|μ2 |
2
2
+|Dμ φ| − λ |φ| −,2λ1Dμ = ∂μ − igAaμ T a − i g Bμ ,2(2.12)(2.13)

where Aaν and Bν are respectively SU(2) and U(1) gauge ﬁeld, g and g  are coupling
constants, abc is the structure function of the SU(2) gauge group, φ is the Higgs ﬁeld
described as a doublet ofﬁeld, and λ, μ are related to the vacuum
 two complex scalar±
2
expectation value (v = −μ /λ). The W and Z bosons acquire mass by the spontaneous symmetry breaking of the vacuum and mixing of the SU(2) and U(1) gauge ﬁelds.
The gauge ﬁelds of W ± and Z bosons are described as
1v
Wμ± = √ (A1μ ∓ iA2μ ), mW = g
2
2

1
v
(gA3μ − g  Bμ ), mZ = g 2 + g 2Zμ0 = 2
g 2 + g 2

(2.14)(2.15)and the remaining vector ﬁeld orthogonal to Zμ0 is massless, describing the photon ﬁeld
in the QED,1(g  A3μ + gBμ ), mA = 0.
(2.16)
Aμ = 
g 2 + g 2
The conﬁrmation of renormalizability in the GWS model by ’t Hooft and Veltman [21]revealed the validity of the theory.
Through many experiments, the Standard Model has revealed its outstanding prediction power to describe particle interactions. No experimental result is observed up to
now that contradicts with the Standard Model (the neutrino oscillation is an exception,
but this does not change the fundamental structure of the theory).
Though the Standard Model has established its status as a promising theory, there
is one missing piece that is actually at the core of the theory: the Higgs bosons. As
mentioned above, the Higgs bosons are believed to be the origin of the spontaneous
symmetry breaking of the electroweak symmetry. The Higgs bosons are also expected tooriginate the mass of fermions as well. If the Higgs bosons exist, they are very likely to
be captured at the LHC experiments (see Section 2.2.1).

2.2

Physics at the LHC

Despite the success of the Standard Model, it is not believed to be the fundamental
theory, and rather regarded as a low-energy eﬀective theory. Some of the reasons are as
follows:
30

1st generation 2nd generation
Quarksucds
Leptonsνe
νμe
μ3rd generation
t
b
ντ
τ

electromagnetic force strong interaction
Gauge Bosonsγgweak interaction
W ±, Z 0(gravity)(G)

Table 2.1: Elementary Particles in the Standard Model: quarks, leptons, and gaugebosons.
• There are too many parameters in the Standard Model which cannot be determined
fundamentally from the theory (mass of elementary particles, three mixing angle
and one phase parameter in the CKM-matrix, similar missing angles for neutrinos,
three gauge coupling constants, QCD vacuum angle, and quadratic and self-coupling
constants of Higgs bosons).
• The gravity is not included in the theory (and cannot be included by the current
framework due to the non-renormalizability).
• Fundamental forces are believed to unify at high energy, but the precise measurements from the LEP (Large Electron Positron Collider) experiments suggests thatthe three fundamental forces (electromagnetic, weak, and strong forces) are unlikely
to do so within the Standard Model prediction.
• The extreme diﬀerence in the scales of quantum gravity (Planck scale: MP c2 ∼ 1019
GeV), Grand Uniﬁed Theory (GUT; MGU T ∼ 1015 GeV), and electroweak theory(MW ∼ 102 GeV). This is called the (gauge) hierarchy problem.
• The mass of the Higgs particles are unstable against radiative corrections within
the Standard Model, and requires ﬁne-tuning.
The Superstring Theory is a good candidate to answer the ﬁrst two points, where spin2 particles are naturally included in the theory. This allows for the quantum description of
the gravitons. The fundamental particles are believed to be explained as “vibrations” ofstrings, where no artiﬁcial parameters except for the string length are needed. However,
the excitation of strings leads to massive particles, which none of the Standard Modelparticles belong. Thus, the Standard Model particles are considered as the ground statesof the strings, and their mass is expected to originate from other mechanisms which are
not found yet.
The supersymmetry provides possible solutions to the remaining problems (it is important ingredient in the Superstring Theory as well). It is a symmetry between bosons
and fermions. The supersymmetry predicts the presence of supersymmetric particles
each corresponding to the Standard Model particles with the same quantum numbers(including the mass) except for the spin. If they exist, they contribute to the radiative31

corrections to the mass of the Higgs bosons, and cancel the contributions from the corresponding Standard Model particles, since they have the opposite spin. In such case,there is no need for any unnatural “ﬁne-tunings” due to the cancellation of loop diagram contributions. However, no supersymmetric particles with the same mass as their
Standard Model counterparts are observed in the previous experiments. Thus, if thesupersymmetry exists, it is expected to be broken at some energy scale (possibly aroundTeV), which may also allow for the three fundamental interactions to unite at the GUT
scale.
The existence of extra dimensions can provide an alternative explanation for the existence of hierarchy. Arkani-Hamed, Dimopoulos, and Dvali proposed what is called the“Large Extra-Dimensions (LED)” model [27] that there is not actually a hierarchy, but
all the scales (Planck, GUT, and electroweak scales) are close. The model insists that the“fake” hierarchy shows up when we observe at four dimensions. Randall and Sundrum
took another approach that the weak scale is generated from the Planck scale throughexponential hierarchy caused by the background metric coming from an extra dimension (warped extra dimensions), and thus providing a mechanism of the hierarchy [28].
There are other models exploiting the existence of extra dimensions, but they will not bementioned here.

2.2.1

Higgs Particle Searches

The ATLAS detector is designed to cover almost all the the possible decay channels of
the Higgs boson along its mass range (∼ 1 TeV is considered as the upper limit due tounitarity arguments).
The expected cross section of the Standard Model Higgs boson production at thecenter-of-mass energy of 14 TeV and its branching ratios are shown in Figure 2.1. The
production of the Higgs boson is possible through several mechanisms. They are• Gluon fusion (Figure 2.2 (a); the red line in the left ﬁgure of Figure 2.1): A Higgs
boson is produced by the loop of top quarks originating from gluon-gluon interaction. This process has the largest cross section at the LHC.
• Vector boson fusion (Figure 2.2 (b); the pink line in the left ﬁgure of Figure 2.1):
A Higgs boson is produced by the interaction of W/Z bosons radiated from thequarks. These events are accompanied by 2 forward jets originating from thosequarks.
• Associated production with W/Z (Figure 2.2 (c); the green and blue lines in the left
ﬁgure of Figure 2.1): One of the important production processes at the Tevatron,
since it collides protons and antiprotons.
• Associated production with t/b (Figure 2.2 (d); the black line in the left ﬁgure of
Figure 2.1): Two pair-produced top (or bottom) quarks interact to make a Higgs
boson.
Examples of channels are shown below that have considerably been investigated at
the ATLAS experiment [23].
32Figure 2.1: Cross section (left) and branching ratio (right) of the Standard Model Higgs
bosons at the center-of-mass energy 14 TeV [22].

g

g

q
t

q
W/Z

H

H

W/Z
q

q

(a)

q

(b)

g

H

t/b
W/Z
q

H
g

W/Z

t/b
(d)

(c)

Figure 2.2: Feynman diagrams of the Higgs productions: (a) gluon fusion, (b) vectorboson fusion, (c) associated production with W/Z, (d) associated production with top or
bottom quarks.

33

Figure 2.3: Sensitivity of ATLAS for the discovery of the Standard Model Higgs boson [24].
• pp → H → ZZ (∗) → 4l(l = e, μ)• pp → H → γγ
• pp → H → W + W − → l+ νl− ν, lνqq
• pp → qqH → qqτ + τ −• pp → tt̄H → tt̄bb̄
• pp → tt̄H → tt̄W + W − , pp → ZH → l+ l− W + W −Figure 2.3 shows the sensitivity of the ATLAS detector to the discovery of the Standard Model Higgs boson. From the gluon fusion Higgs productions, the H → ZZ (∗) → 4lchannel provides a prominent signature along a large mass range. H → γγ is an important channel for low mass Higgs (80 < mH < 150 GeV/c2 ). The degradation of γγ
invariant mass resolution due to conversions in the material in front of the calorimeter isthe major challenge at the ATLAS experiment.
Recently, the Tevatron experiments (CDF and D0) excluded at 95% C.L. the Standard
Model Higgs boson with the mass range of 163-166 GeV/c2 [25]. The exclusion slightly
decreased from 160-170 GeV/c2 (as of March 2009) due to the presence of signal-like34

Events / 1fb−1/ 50GeV

Events / 1fb−1/ 50GeV

104

SU3
SM BG
tt
W
Z
QCD
single top

103

2

10

ATLAS

102

10

SU3
SM BG
tt
W
Z
single top
ATLAS

1

10

1
0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

10−1
0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

Figure 2.4: Expected Missing ET distributions of a benchmark Supersymmetric events(named as SU3) and the Standard Model background in No Lepton (left) and One Lepton
Mode (right) at the center-of-mass energy of 14 TeV [23].
candidates after the increase in statistics, though the excess was less than one standard
deviation in respect to the background.

2.2.2

Supersymmetry

An exact Supersymmetry predicts the existence of at least one set of supersymmetricparticles accompanying each Standard Model particle with the same mass and a diﬀerent
spin. Such particles were not detected in the previous experiments, so the exact Supersymmetry is not adopted in nature. The only possibility for the Supersymmetry to exist
is that it is softly broken at an energy scale, and giving the supersymmetric particles
their mass which were not reachable by the previous high energy colliders.
Several mechanisms were proposed which break the supersymmetry. The gravitymediated, the Gauge-mediated (GMSB), and the Anomaly-mediated SUSY breakings(AMSB) are well-known mechanisms and have been investigated to quite an extent,
phenomenologically and experimentally.
In R-parity conserving Minimal supergravity (mSUGRA) models, where the universality of gaugino masses, scalar masses, and various A-parameters at the GUT (Grand Uniﬁcation Theory) scale is assumed, the neutralino is normally the lightest-supersymmetricparticle (LSP). The LHC tends to have copious productions of squarks and gluinos, andthose particles will initiate cascade decays to the LSP’s. Since the LSP’s are weaklyinteracting particles, they will create a large Missing ET (ETmiss ) with many jets originating from the cascades. Thus, “multi-jet and large ETmiss ” is one of the standard
event topologies that have also been extensively investigated at the Tevatron. ATLAS
has a discovery potential for large parameter space of mSUGRA, and such events canobviously show up as an excess in the ETmiss distribution as shown in Figure 2.4 (the left
ﬁgure shows the ETmiss distribution from all hadronic multi-jet plus ETmiss signature, andthe right from 1 lepton, multi-jet plus ETmiss signature).

35

2.2.3Extra Dimensions

The prominent features predicted by extra dimensions is the presence of Kaluza-Klein
particles and their emissions or decays (will be mentioned below), and productions ofmini-black holes.
In the LED model, on top of the normal four dimensional space-time, n extra dimensions are considered to exist, toroidally compactiﬁed with a radius, thus with the volume(2πr)n . All the Standard Model particles are localized to the four dimensions, and only
the gravitons can propagate in the extra dimensions. Considering the action in 4 + n
dimensions, and rewriting and factoring it out the higher dimensional action to match
the four dimensional action, we obtain22+n= MPl(4+n)(2πr)n ,MPl(2.17)

where MP l is the standard Planck scale, and MPl(4+n) (also often written as MD ) is theactual Planck scale expected in the higher dimensional case. This shows that the strengthof the gravitational force is not actually weak, but seems as so, due to the presence of
extra dimensions.
There has been a series of experimental tests on the deviation of the Newtonian
gravitational force. The potential V (r  ) of the modiﬁed Newtonian force is often simpliﬁed
and described as,
m1 m2 

1 + αe−r /λ ,(2.18)V (r ) = −GNrwhere GN is the standard gravitational constant, m1 , m2 the mass of two objects underconsideration, r  the distance between the two objects, α the strength of the unknown
short range force, and λ the range of the unknown force related to the mass of theexchanged bosons. The term of the unknown short range force is often called as the“ﬁfth force.” Current limits on the ﬁfth force from various experiments are summarized
in Figure 2.5. In the LED model, the number and size of the extra dimensions can be
expressed with the non-Newtonian force as [30],
λ = r
4
α =
(2n)
3

(2.19)(2.20)

Considering that
1
r=
2π

2
MPl
n+2
MPl(4+n)1/n,(2.21)

n+2
in case of MPl(4+n)=1 TeV, the size of extra dimensions is 1012 m for n = 1, 10−3 m
for n = 2, 10−8 m for n = 2, and etc. It is obvious that n = 1 is ruled out in case ofn+2=1 TeV, but the extra dimensions of a millimeter-size (n = 2) was not excludedMPl(4+n)
at the time of the proposal of the LED model. Previous collider experiments also imposed
further limits on the models, but the lower limits are still around 1 TeV (Figure 2.6).
The LHC provides an opportunity to search for the extra dimensions in the range never
having to be able to reach before.

36

Figure 2.5: Experimental limits on the parameters of the modiﬁed Newtonian gravitational force [29].

Figure 2.6: Lower limits of MD in the Large Extra Dimension model from the previous
collider experiments [39].

37The warped extra dimension is another well-known model (often called the RS model
where RS stands for Randall and Sundrum). In the original RS model, only one extra
dimension is assumed with non-trivial space-time metric, contrary to the LED model. In
the RS model, two 3-brane’s exist; one is at the TeV scale, and the other at the Planckscale. The branes are separated by a certain distance along the direction of the ﬁfth
dimension. The background space metric is described as,
ds2 = e−k|y| ημν dxμ dxν − dy 2,(2.22)

where y is the coordinate of the ﬁfth dimension, k is the AdS curvature of the ﬁfth
dimension. e−k|y| is called the “warp factor.”
In extra-dimensional models, Kaluza-Klein modes (or particles) occur due to theboundary conditions of the extra dimensions. Only certain eigenvalues of energy is allowed in the direction of the extra dimensions, and those quantized states appear as
massive particles from a three dimensional observer. A peculiar diﬀerence in KK modecan be seen between the LED and RS model. In LED model, the spacing between the KK
modes is ∼ 1/R per each extra dimension, whereas in the RS model, the mass spacing is
the same as the zero points of the Bessel function. This is due to the warped geometryof the extra dimensions. The detection of several KK particles and their mass spacings
will allow us to discriminate the models.
When the extra dimensions are visible at TeV scale, the Schwartzschild radius will
be large enough for the impact parameter of the colliding partons to pass it. In such asituation, a mini-black hole can be copiously produced at the LHC [40], and originate
striking event features like democratic particle decays. The use of dijet topology is also
proposed to investigate the eﬀect from quantum gravity [41].

38Figure 2.7: Side-view of a simulated black hole event in ATLAS. Inner detectors weremagniﬁed in the ﬁgure and not depicted to scale.

39

Chapter 3
The Large Hadron Collider
The Large Hadron Collider (LHC) is a proton-proton collider (and a Pb-Pb collider forheavy ion studies), located at the European Organization for Nuclear Research (CERN)
in Geneva, Switzerland [42]-[44]. It was operated at the center-of-mass energy 7 TeV atthe end of 2009 and will reach up to 14 TeV which will surpass the current center-ofmass energy from any other colliders around the world (by a signiﬁcant factor comparedto 1.96 TeV of the Tevatron). The design luminosity is 1034 cm−2 s−1 which is also an
unprecedented performance. Beam crossings are 25 ns, and there will be 23 interactions
per bunch crossing at the design luminosity. The LHC also has the world’s largest
circumference of 26.7 km which was succeeded from the Large Electron Positron Collider(LEP).

3.1

Why the LHC?

An accelerator facility that can surpass the current energy frontier and probe the physics
at the TeV scale has been aspired for a long time. The LHC is the ﬁrst collider to meetsuch requirements.
Although low background and clear experimental environment are essential advantages of experiments at electron-positron colliders, the major obstacle to achieve theacceleration up to such high energy in electron-positron colliders is the synchrotron radiation. The synchrotron radiations can be emitted during the transverse accelerations
by deﬂections, and can signiﬁcantly contribute to the energy loss. The power emitted by
the synchrotron radiation by a deﬂection is:P =1
e2 c E 46π0 (mc2 )4 ρ2(3.1)

where 0 is the permittivity of vacuum, e the electron charge, c the speed of light, m
the mass of the moving particle, E the energy of the moving particle, and ρ the bending
radius. As obvious from the equation, energy loss severely depends on the mass ofthe moving particle and the curvature of the path. In order to achieve high energy
acceleration, there are two ways of overcoming the obstacle. One is to use heavy particles
for acceleration (large m), and the other is to use a linear accelerator (ρ → ∞). LHC
40

Figure 3.1: CERN and the LHC. The ﬁgure is not to scale.

41

adopted the former solution, to use protons instead of electrons, and use the world-largest
LEP tunnel which further suppresses the synchrotron radiation due to its large radius.
For such a collider, the synchrotron radiation is rather small, thus the limitation on theacceleration comes from the maximum magnetic ﬁeld available to bend the protons.
Since the momentum distribution of partons (quarks and gluons) inside the colliding
protons is a steeply decreasing function, a high luminosity is also required on top ofhigh center-of-mass energy to search for rare events, in which a parton-parton system of
the fundamental processes has large invariant mass. In order to achieve high luminosity, proton-proton collisions were chosen, since it is not possible to reach the required
luminosity using protons and anti-protons.
Fulﬁlling all the requirements mentioned above, LHC will be the ﬁrst experiment thatmake it possible to probe the physics at the TeV-energy scale.

3.2

LHC Injector Complex

Many parts of the accelerator complex of the LHC are re-used from previous experiments
including the LHC tunnel which used to be called the LEP tunnel, and accelerators usedin the injector complex such as Linac, PS, and SPS (Figure 3.1). Those accelerators were
upgraded to meet the requirements from the LHC: high intensity proton bunches withsmall transverse and well-deﬁned longitudinal emittance.
Protons are provided from Linac2 using a duoplasmatron source, and accelerated up to
750 keV with radio-frequency quadrupole (RFQ). An upgrade from the original CockcroftWalton injector to RFQ in 1993 made a signiﬁcant increase in the beam intensity. Dueto the installation of the new injector and many improvements in the source, RF, optics,
and diagnostics of Linac2, currents of up to 183 mA was achieved.
Those protons are injected into Proton Synchrotron Booster (PSB; Booster in Figure 3.1). PSB was upgraded to increase the output energy from the original 1 GeV to
1.4 GeV. This lead to the increase of magnetic ﬁeld by about 26 % to 0.87 T. A new
RF system is tuned to the revolution frequency (RF harmonic h = 1), and supplemented
with a h = 2 system. It contributed to the reduction of space charge eﬀects observed in
the previous acceleration scheme by h = 5 and more stability of the coupled bunches.
This new RF system provides 1 bunch per PSB ring, with 190 ns bunch length.
These 1.4 GeV protons enter Proton Synchrotron (PS), and will reach 25 GeV. Six
bunches are delivered from the PSB in two batches, and are captured by harmonic h = 7
in the PS. These bunches will be split in three when the second batch is received. This
will lead to 18 bunches on h = 21, and are accelerated to 25 GeV on this harmonic.
By changing the procedure of splitting and using diﬀerent values of harmonic captures,
alternative bunch spacings such as 50 ns or 75 ns can be achieved as well. The bunch
spacing at the PS will be preserved up to the LHC.
The Super Proton Synchrotron (SPS) will be the ﬁnal link in the injector chain to the
LHC. Since the SPS is not optimized for the LHC, it can only accelerate about 4 × 1013
protons per cycle, which limits the number of PS bunches per SPS cycle up to four. In
order to ﬁt the small aperture of the LHC superconducting magnets and to achieve thehigh luminosity, the transverse emittance of the beams must be kept small. This is one
42Point 5

Point 8

Point 2
Point 1Figure 3.2: The LHC layout. ATLAS locates at Point 1 of the LHC ring.
of the major challenges for the SPS. The acceleration of proton beam up to 450 GeV isperformed with a 200 MHz traveling wave RF system. These protons are injected to the
LHC by the transfer lines.

3.3

LHC Layout and Technology

The LHC consists of eight arc sections and eight straight sections (Figure 3.2). Protons
will counter-rotate in two separate beam pipes and cross at four points, where there aredetectors. The four detectors from LHC experiments (will be mentioned in the next
section) are installed at the straight sections. Other straight regions are dedicated forthe beam cleaning, beam dumping system, RF and beam instrumentation. The RF andbeam feedback systems are concentrated at Point 4, and 400 MHz superconducting cavity
system will accelerate the protons up to several to 7 TeV.
In the arc sections, protons are deﬂected by dipole magnets (Figure 3.3). Quadrupole
magnets are used for focusing. Each LHC arc consists of 23 cells, where there are six
dipole magnets and two quadrupole magnets per cell. One cell is about 106.9 m long
(Figure 3.4). Ideally, these magnets would be enough to keep the particle trajectory stable, but sextupole, octupole, and decapole magnets are installed to correct for nonlinear
movements due to magnetic ﬁeld errors.

43

The main parameters of the LHC for proton-proton collisions are shown in Table 3.1.
As mentioned in the previous section, 450 GeV protons are injected into the LHC ringfrom the SPS, and are accelerated by the RF at Point 4. The designed maximum energyof the protons are 7 TeV.

3.4

Experiments at the LHC

The LHC has two detectors dedicated for high luminosity experiments, ATLAS (AToroidal LHC Apparatus) [45] and CMS (Compact Muon Solenoid) [46]. These are general purpose detectors designed for various physics motivations such as the discoveriesof the Higgs bosons, supersymmetric particles, extra dimensions, and precise measurements of the Standard Model processes. There are two low-luminosity experiments whichare LHCb (Large Hadron Collider beauty experiment) [47] for B-physics and TOTEM(TOTal Elastic and diﬀractive cross section Measurement) [49] for the total cross section
measurement. ALICE (A Large Ion Collider Experiment) [48] is dedicated to heavy ion
physics studies such as investigations on the quark-gluon plasma with the peak luminosity of 1027 cm−2 s−1 in lead-lead collisions. LHCf [50] is for the highest-energy cosmic-ray
studies, and will validate the models of cosmic hadronic interactions using the LHC data.

44

Figure 3.3: LHC cryodipole in the arc section.

Injection Energy
Maximum Energy
Dipole Field
Design Luminosity
Number of Particles per Bunch
Number of Bunches
Bunches Length
Bunch spread
Bunch Crossing

Design450 GeV
7 TeV
8.33 T1034 cm−2 s−1
1.15 × 1011
2808
1.06 ns0.22 ×10−3
24.95 ns

Table 3.1: LHC Parameters for proton-proton collisions.

45

Figure 3.4: LHC Cell. there exist six dipole magnets and two quadrupole magnets per
cell.

46

Chapter 4
The ATLAS Detector
The ATLAS (A Toroidal LHC Apparatus) detector (Figure 4.1) is a general purpose
detector designed to meet requirements for a wide range of physics measurements such as
searches for Higgs particles, Supersymmetry (SUSY), extra dimensions, and the precise
measurement of Top physics and other Standard Model processes. The detection of the
Higgs bosons was mainly used as the benchmark for the requirements of the detector
performance. The overall requirements for the detector performance can be summarized
as follows.
• Very good muon identiﬁcation and momentum measurement up to 1 TeV with noless than 10% precision. An adequate resolution even with a stand-alone reconstruction with the muon spectrometer to cope with the high luminosity.
• Very good electromagnetic calorimetry with high resolution of photon and electronenergy, position and direction measurement with a wide geometric coverage.
• Full coverage of hadron calorimetry for good jet energy and missing transverse
energy measurement.
• Inner tracking with very good momentum resolution and tolerance against highradiation doses.
• Triggering and measurement of low pT particles to cover most of the physics processes of interest.
The details on how each sub-detector fulﬁlled these requirements will be mentioned in
the following sections.
ATLAS is 44 m wide and 25 m tall, and is placed at Point 1 of the LHC tunnel, about
100 meters underground. Its total weight is 7000 tons. New technologies are used invarious parts of the detector, such as air-core toroidal magnets, the accordion-structure of
the electromagnetic (EM) liquid argon (LAr) calorimeter, the perpendicular conﬁgurationof the plastic tiles in the Tile calorimeter, and the hybrid hadron calorimeter system using
two diﬀerent technologies in the barrel (Tile) and the end-cap (Liquid Argon), to namea few.
47

Figure 4.1: ATLAS Detector
The ATLAS detector consists of the inner detector, EM and hadronic calorimeters,
muon spectrometer, one solenoidal magnet for momentum measurement of the charged
particles at the inner detector, three toroidal magnets for the muon momentum measurement with the muon spectrometer, and forward detectors [45][52][53]. The detector
system allows to measure various types of particles, such as electrons and photons by the
inner detector and EM calorimeter, hadrons by the hadronic calorimeters, and muons by
the outermost muon spectrometer (Figure 4.2). Even the weak-interacting particles suchas neutrinos can be measured by exploiting the transverse energy imbalance in the wholedetector.

4.1

Inner Detector

There are four sensors so called the inner detector as a whole [54] [55]. From the closest
to the beam pipe, they are the Beam Conditions Monitor (BCM) [56], Pixel Detector(Pixel) [57], Semiconductor Tracker (SCT), and Transition Radiation Tracker (TRT).
They are contained within a cylinder of 7 m length and a radius of 1.15 m. The innerdetector volume is restricted by the dimension of the cryostat containing the LAr EM
calorimeter in the radial direction and by the position of the end-cap calorimeters in the
beam direction. The inner detector are immersed in 2 T solenoidal magnetic ﬁeld. The
BCM is implemented to detect beam incidents and to measure the luminosity. Pixel and
SCT are used for the pattern recognition (charged track reconstruction), momentum and48

Figure 4.2: Cross section of the ATLAS detector with typical particles traversing throughit.

49

vertex measurement. The TRT uses straw tube technology, and oﬀers additional tracking
points to the ones from the Pixel and SCT, which greatly improves the momentum
resolution. The TRT can also detect transition radiations in the outer volume of innerdetectors which is useful for electron and pion separation.
Each component is brieﬂy reviewed below.

4.1.1

Beam Conditions Monitor

The Beam Conditions Monitor (BCM) is one of the newest detectors stationed at Point
1. It is designed to detect beam incidents such as several bunches hitting the collimators.
It also provides measurement of bunch-by-bunch luminosities.
The BCM is based on radiation-hard polycrystalline chemical vapor deposition (pCVD)
diamond sensors. It consists of two stations in the forward and backward directions.
Eight modules (four on each station) are installed on the pixel detector support frame(Figure 4.4).

4.1.2

Pixel Detector

The Pixel detector allows for a very high-granularity, high-precision measurements withthe closest position to the interaction point. It mostly determines the impact parameter
resolution.
The Pixel detector is one of the tracking detectors using the semiconductor technology,
which covers the region of |η| < 2.5. In the barrel region, pixels are arranged in threelayers around the beam axis in a concentric cylinders, whereas in the end-cap region,
they are placed in ﬁve layer disks perpendicular to the beam axis. The innermost layer(B-layer) in the barrel suﬀers greatly from the radiation damage, so the mechanical design
allows for the replacement of the B-layer after a few years of running, to maintain theperformance of b-tagging. All pixel sensors are identical and the minimum pixel size is
50 × 400 μm2 . The pixel layers are segmented in R − φ and z. Particles typically crossthree pixel layers. The position resolution of a single hit is 10 μm in R − φ and 115 μm
in z. There are approximately 80.4 million channels.

4.1.3

Semiconductor Tracker

The SCT uses the silicon microstrip detector technology, which has successfully been
adopted in the previous experiments such as the LEP and the Tevatron. The barrel
SCT provides 8 layers of silicon microstrip detectors in the intermediate radial range
in order to achieve precision in R − φ and z coordinates, and contribute to improvethe measurement of momentum, impact parameter, and vertex position. Each silicondetector is 6.36 × 6.40 cm2 in size with 768 readout strips of 80 μm pitch. In the barrel
region, the SCT uses 40 mrad stereo strips to measure both coordinates. The active
silicon area of the SCT is about 63 m2 , more than an order of magnitude larger than any
other silicon vertex detector used in the previous experiments. The resolution is 17 μm
in R − φ and 580 μm in z. The total number of readouts is approximately 6.3 million.

50

Figure 4.3: Inner Detector.

Figure 4.4: Position of the Beam Conditions Monitor inside the inner detector.

51

Figure 4.5: Semiconductor Tracker (SCT) module.

4.1.4

Transition Radiation Tracker

TRT uses 4 mm diameter straw tubes equipped with a 31 μm diameter gold-plated W
anode wire, which provides typically 36 hits per track. Tracks can be followed up to|η| = 2.0. TRT only provides R − φ information. It is operated with Xe-CO2 -O2 gas
mixture (70 %, 27 %, 3 %) under 5 - 10 mbar over-pressure, which allows to detect
transition radiation photons created in the radiator (a matrix of polypropylene ﬁbers
with a diameter of 19-μm) between the straws. The length of the straw tubes are 144 cm
in the barrel, and 37 cm in the end-caps. The barrel contains 52544 straws per module,and 122880 straws for each end-cap. Under the nominal operating conditions, the electron
collection time is about 48 ns, and the drift-time accuracy is around 130 μm. The total
number of readout channels is approximately 351,000. The continuous tracking by the
TRT improves the pattern recognition performance. The straw spacing was optimized
for the electron identiﬁcation. The detection of transition radiation photons provides
discrimination between electrons and hadrons.

4.2

Calorimeters

Calorimeters [58] in the ATLAS detector consist of two types of calorimeters, electromagnetic (EM) calorimeter and hadronic calorimeter (Figure 4.6). EM calorimeter uses the
LAr technology [59], whereas hadronic calorimeter uses two technologies; Tile technology
in the barrel [61], and LAr in the end-cap. Here, overall detector proﬁle of the calorimeters and their performance is described (details of the readout, energy reconstruction andcalibration schemes will be mentioned in Chapter 5).

4.2.1

Electromagnetic Calorimeter

The main physics requirements on the EM calorimeter can be summarized as follows:• Large acceptance in η. In order to observe rare physics events such as H → γγor H → 4e decays with the largest possible acceptance, it is important to cover52

Figure 4.6: ATLAS Liquid Argon and Tile Calorimeters. The calorimeter system has adiameter of 4.25 m and a length of about 13 m.
as much large rapidity as possible. The upper bound comes from the radiation
tolerance of the inner detector in the forward region.
• Very good energy resolution and ﬁne segmentation. The mass measurementof the Higgs boson from H→ γγ and H→ 4e impose tight requirements on theperformance of the EM calorimeter. Energy resolution ofσE
10%= √ ⊕ 0.7%
EE
is required for the energy resolution. Fine segmentation of the EM calorimeter allows for good angular resolution of e, γ and also e, γ/π 0 separation. High granularity
also allows for low electronic noise as will be mentioned below.
• Wide dynamic range (∼ 1 GeV to 5 TeV). ATLAS will measure electrons andphotons of wide energy range. The lower bound comes from the need to reconstructelectrons originating from the semileptonic decays of b-quarks to achieve good btagging eﬃciency. TeV-energy electrons may originate from new physics phenomena
such as the leptonic decays of Z  and W  bosons.
• Good time resolution. Precise time measurement provides a power tool to reject
non-collision background such as the cosmic rays, beam gas, and beam halos. It is53

also important for identifying non-prompt decays coming from long-lived particles
from new physics. Identiﬁcation of bunch crossing is needed for the trigger purpose.
Thus, the time resolution of a few ns is required in the EM calorimeter.
• Low electronic and pile-up noise. The aim to achieve very good mass resolution for H → γγ and H → 4e channels requires low noise of various types. High
granularity of the calorimeter allows for the low incoherent electronic noise. Fast
detector response and electronics are required to suppress the pile-up noise. Dueto the large number of channels in the calorimeter, coherent noise can largely spoil
the resolution of ETmiss as well, which is important for the mass measurement of the
Higgs boson in H→ τ τ channel, for example.
The EM calorimeter is located in two separate regions, the barrel and the end-cap.
The barrel EM calorimeter covers up to |η| =1.475, and the end-cap EM calorimetercovers 1.375 < |η| < 3.2. Each of them is housed in a separate cryostat. In order toreduce the material before the calorimeter, the central solenoid and the LAr calorimeter
share a common vacuum vessel. The barrel calorimeter consists of two identical halfbarrels, separated at z=0 with a 4 mm gap. Each end-cap EM calorimeter consists of
two mechanical coaxial wheels, which the outer wheel covers 1.375 < |η| < 2.5 and theinner wheel covers 2.5 < |η| < 3.2.
For the active material, LAr is used, and lead (Pb) as the absorber.
In the region up to |η| < 1.8, the presampler is located in front of the EM calorimeterto the measure the energy lost in the dead material in front of the EM calorimeter(Figure 4.8). The EM calorimeter consists of three sampling layers (Figure 4.9). Due tothe accordion structure [60] (Figure 4.7), the readout from front and back is possible andallows for the complete φ symmetry without azimuthal cracks. The accordion waves in thebarrel are axial and run in φ, and the folding angles vary with radius to achieve constantliquid-argon gap of 2.1 mm. This gap width corresponds to 450 ns of total drift time
for operating voltage of 2000 V. In the end-cap, the accordion waves are parallel to theradial direction and run in the axial direction. The LAr gap in the end-cap increases withradius, and the wave amplitude and the folding angle of the absorber and the electrodes
vary with radius to achieve uniform performance. The total thickness of the modules is
at least 22 radiation lengths (X0 ) in the barrel, and greater than 24 X0 in the end-cap(except for |η| < 1.475). The ﬁne granularity of the EM calorimeter matched with theinner detector allows for the precise measurement of electrons and photons.

4.2.2

Hadronic Calorimeter

ATLAS hadronic calorimeter uses hybrid technology as mentioned above, the Tile technology in the barrel part, and the LAr technology in the end- cap and forward region.
The LAr forward calorimeter expands the coverage up to η = 5.0 to improve the ETmiss resolution.
The major role of the hadronic calorimeter is to reconstruct jets and contribute to themeasurement of ETmiss . The requirements on the performance of the hadronic calorimeterare summarized as follows:54

Cells in Layer 3
Δϕ×Δη = 0.0245×0.05
Trigger ToweΔη = 0 r
.1

2X0

47

0m
m

η=0

16X0
TriggeTow r
Δϕ = 0er
.0982

15
00
m
m

4.3X0
1.7X0
Δϕ=0.0245x36.8m 4mx
=147.3 4mm

Square cells in
Layer 2Δϕ = 0.0245

ϕΔη = 0.025
m/8 =4.69 mm
Δη = 0.0031
Strip cells in Layer 1

37.5m

η

Figure 4.7: Sketch of a barrel module in EM LAr calorimeter• Good rapidity coverage. The extension of the rapidity coverage up to |η| ∼5 allows for an eﬃcient tagging of the forward jets expected to associate to the
production of heavy Higgs bosons. A large rapidity coverage also improve theresolution of ETmiss .
• Adequate granularity. A successful reconstruction of dijets originating fromhigh-pT W bosons impose the most stringent requirement on the granularity. Granularity of Δη × Δφ = 0.1 × 0.1 is needed in the region of |η| < 3, and Δη × Δφ =0.2 × 0.2 is suﬃcient at larger η regions.
• Energy resolution. The required energy resolution performance in the end-cap
and the forward region is as follows:
σE
E
σE
E

50%
√ ⊕ 3% (|η| < 3)E100%
= √ ⊕ 10% (3 < |η| < 5)
E
=

55

X0

12
Before accordion

10

Before presampler

8
6
4
2
0
0

0.5

1

1.5

2

2.5
3
Pseudorapidity

X0

X0

Figure 4.8: Amount of dead material in front of the EM calorimeter.

40
35

Layer 3Layer 2
Layer 1
Before accordion

45
40
35

30

30

25

25

20

20

15

15

10

10

5

5

0
0

Layer 3Layer 2
Layer 1
Before accordion

0.2

0.4

0.6

0.8

1

1.2
1.4
Pseudorapidity

0

1.6

1.8

2

2.2

2.4

2.6

2.8
3
3.2
Pseudorapidity

Figure 4.9: Thickness of each accordion sampling layer in the EM calorimeter in thebarrel (left) and the end-cap (right).

56

The resolution is suﬃcient for the jet and ETmiss measurement.
Tile Hadronic Barrel Calorimeter
Tile Calorimeter (TileCal) consists of three cylindrical sections. The Long-Barrel (LB:|η|<1.0), 5.8 m in length, is contained in a single cylinder with separate partitions for
positive and negative η. Two partitions of the Extended-Barrel (EB: 0.8<|η|<1.7), eachof them 2.6 m in length, are respectively contained in a cylinder. The Tilecal has the inner
radius of 2.28 m and the outer radius of 4.25 m. The four partitions are named as LBA,
LBC, EBA, and EBC, where A corresponds to the positive η (Anti-clockwise direction of
the beam-line) and C the negative η (Clockwise direction). The radial depth of TileCal
is about 7.4 interaction lengths (λ). TileCal has three sampling layers (sampling A,
BC, D; Figure 4.11). Scintillating tiles are used as active material, and steel is used as
an absorber. Tiles are placed perpendicular to the beam axis and radially staggered in
depth. It allows for a simpler readout and tile conﬁguration in order to combine all thesignals from towers [62](Figure 4.10). Signals are read out by photomultipliers (PMT’s)with wavelength shifting (WLS) ﬁbers connected to both side of the tiles (Figure 4.12).
Hamamatsu R7877 8-stage metal channel dynode PMT’s are used. WLS ﬁbers have adiameter of 1mm (Kuraray Y11(200)MSJ double-clad ﬁbers), and their emission peakis at 476 nm with a decay time of ∼6 ns. The photomultiplier tubes and front-end
electronics are mounted in “drawers,” which are aluminum structures of 3 meter length
placed in a girder frame along the outer edge of each module. TileCal has good time
resolution, around 1 ns, and adequate granularity (η × φ = 0.1 × 0.1, and 0.2 × 0.1 forthe last layer) to achieve good enough jet energy and missing transverse energy resolution.
LAr Hadronic End-Cap Calorimeter
For the hadronic end-cap calorimeter (HEC), liquid argon is used as the active materialand copper (Cu) as the absorber. It consists of 2 independent wheels per end-cap andcovers 1.5 < |η| < 3.2. Each wheel is divided into 2 layers, thus composing 4 layers foreach end-cap. The extention of HEC up to |η| = 3.2 which overlaps with FCAL, wasdone to avoid material reduction in the transition region of HEC and FCAL. The copper
plates are interleaved with LAr gap of 8.5 mm (Figure 4.13). The typical drift time ofelectrons is 430 ns under the nominal voltage, 1800 V.
LAr Forward Calorimeter
Forward Calorimeter (FCAL) [63] covers the η range of 3.1 to 4.9. It is approximately
4.7 m away from the interaction point, 10 interaction lengths deep, and consists of threemodules which the ﬁrst layer is for electromagnetic measurement whereas the second
and the third modules is for hadronic measurement (Figure 4.14). It contributes to theuniformity of the calorimeter coverage. A shielding plug was placed behind the thirdlayer to reduce background in the muon spectrometer. The ﬁrst module uses copper for
absorber, and the second and the third modules use tungsten. Since, FCAL is exposed
to high ﬂuxes of particles, the liquid argon gaps were kept very small, with the electrode57

Photomultiplier

Wavelength-shifting fibre
Scintillator

Steel

Sourcetubes

Figure 4.10: TileCal Module

Figure 4.11: Segmentation in depth and η of the tile calorimeter modules in the longbarrel and extended-barrel

58

Figure 4.12: Typical ﬁber bundles in TileCal [61]. Each bundle is read out by a single
PMT.
structure of small-diameter rods, centered in tubes. These tubes were placed parallel tothe beam direction. In the ﬁrst FCAL layer, the triangular current pulse at the electrodehas a total drift time of 60 ns.

4.3

Muon Spectrometer

Muon spectrometers [64] consist of 4 detectors, 2 dedicated for track reconstruction (Monitored Drift Tubes and Cathode Strip Chambers) , and the others for trigger purposes(Resistive-Plate Chambers and Thin-Gap Chambers; Figure 4.15).
The muon momenta are measured with the magnetic deﬂection of tracks by the large
superconducting air-core toroid magnets. They are designed to measure tracks up to|η| < 2.7, and to trigger on particles in the region of |η| < 2.4. For the range |η| < 1.4,
tracks are bent with the barrel toroidal magnetic ﬁeld, whereas in 1.6 < |η| < 2.7, themagnetic bending is provided by the small end-cap toroid magnets placed on the bothends of the barrel toroid. The region of 1.4 < |η| < 1.6 is called the transition region
where the magnetic deﬂection is provided by both the barrel and end-cap toroids. The
performance goal is to measure 1 TeV tracks with a resolution of 10% with a standalone momentum reconstruction. The stand-alone measurements still provide adequate
resolution and charge identiﬁcation up to a few TeV (∼3 TeV), and down to a few GeV(∼3 GeV). Various contributions to the momentum resolution of the Muon Spectormeter
tracks are shown in Figure 4.16.
The trigger chambers provide fast information from muon tracks. They are required
to discriminate the muon momentum, to indentify the bunch-crossing, and to provide fastand coarse tracking informaton to be used for the high-level triggers (Level-2 and Event
Filter; see Section 4.6). They also provide φ hits to complement the MDT measurement.
The resolution requirement greatly diﬀers in the barrel and the end-cap region, whichlead to the separate trigger chamber systems. With RPC and TGC, muon triggers can
59

Figure 4.13: Schematic view of HEC module1111111111111111111111
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1111111111111111111111
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1111111111111111111111
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0
10000000000000000000000
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0
10000000000000000000000
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01(EM)0000000000000000000000
1111111111111111111111
0
10000000000000000000000
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0
10000000000000000000000
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0
10000000000000000000000
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Moderator shielding
0000000000000000000000
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0
10000000000000000000000
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0
10000000000000000000000
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0Pump
10000000000000000000000
1111111111111111111111
0
1
0
1

HEC

30
20
10

FCal 1R

40(back)

EMEC

r

50

1111
0000
0000
1111
1111
0000
0000
1111
0000
1111
0000
1111
0000
1111
0000
1111
0000
1111
0000
1111
0000
1111
0000
1111
0000
1111

HEC

te

60

FCal 2

FCal 3

(Had)

(Had)

sh LA
ie r
ld C
in al
g or
pl im
ug e

R (cm)

LAr gap

Beampipe
Warm
wall

0
350

400

450

500

550

600

650

z (cm)

Superinsulation
Cold
wall

Figure 4.14: Schematic view of FCAL partitions and module
cover the low and high pT muons in wide η regions (Figure 4.17).

4.3.1

Muon Drift Tubes

Muon Drift Tubes (MDT) are one of the two precision-tracking chambers dedicated for thedetermination of the coordinate of the track in the bending plane. MDT has a projective
geometry, which means that the layer dimension and chamber size increase proportionallyto their distance from the interaction point. It is used both in the barrel and the end-cap,
and covers the region of |η| < 2.7, but the innermost layer in 2 < |η| < 2.7 is coveredby the CSC (Figure 4.18). It consists of pressurised drift tubes made of aluminum(diameter 29.970 mm), ﬁlled with Ar/CO2 gas (mixed with the ratio of 93% and 7%)
at 3 bar (Figure 4.19). The electrons produced from ionization are collected at thetungsten-rhenium wire (diameter 50 μm) with the potential of 3080 V. The gas gain is
2 × 104 . There are several advantages for using individual tubes with single wires. The
stiﬀness of the tubes provides mechanical precision and strength. Also, a high operational
performance is expected, since the failure from a single tube will not aﬀect most of the60Figure 4.15: Overview of the Muon Spectrometer
other tubes. A disadvantage comes from the long drift time, which the maximum is about
700 ns when furthest from the tangental point. The average resolution per tubes is about
80 μm.

4.3.2

Cathode Strip Chamber

The Cathode Strip Chamber (CSC) is a multi-proportional chambers with radiallyoriented wires. Counting rates are expected to be higher in the end-cap region, andwill exceed the safe operation limit of MDT (150 Hz/cm2 ) in the ﬁrst layer at |η| > 2. Insuch regions, the MDT is replaced with the CSC (Figure 4.18). Its safe operation limit
is up to the counting rate of about 1000 Hz/cm2 , which is suﬃcient up to |η| = 2.7. It issegmented in φ with large and small chambers (8 for each). The anode wire diameter is
30 μm with 1900 V applied. The CSC is ﬁlled with Ar/CO2 (80/20). The gas gain is 6
×104 . The position of tracks is obtained by interpolating the charges induced on cathodestrips. The signals are not read out from the CSC wires. The CSC is suitable for thehigh particle density region due to its good two-track resolution, short electron drift time(less than 40 ns), and low neutron sensitivity.

4.3.3

Resistive Plate Chamber

Resistive Plate Chamber (RPC) is the muon trigger system in the barrel region. If
consists of three concentric cylindrical layers. It is a gaseous parallel electrode-plate
detector without any wires. The resistive plates are made of phenolic- melaminic plastic
laminate. The nominal operating voltage is 9.8 kV, and the gas gap is 2 mm. The gas
61

Contribution to resolution (%)

12

10

8

6

Wire resolution and autocalibration
Chamber alignment
Multiple scattering
Energy loss fluctuations
Total|η| < 1.5

4

2

0
10

10

2

10

3

pT (GeV)Figure 4.16: Momentum resolution of the muon spectrometer

Figure 4.17: Schematic view of the Muon Trigger System
62

MDT chambers

12 m
Resistive plate chambers
10
Barrel toroid coil
8

Thin gapchambers

64End-cap
toroid2
Radiation shield

Cathode strip
chambers
0

20

18

16

14

12

10

8

6

4

2m

Figure 4.18: Sideview of the muon spectrometer.
ﬁlled in the gap is a mixture of C2 H2 F4 /Iso-C4 H10 /SF6 (94.7/5/0.3), which allows for
relatively low operating voltage, non-ﬂammability and low cost. The detection eﬃciency
per layer is larger than 98.5 %.

4.3.4

Thin-Gap Chambers

Thin Gap Chambers (TGC) are the trigger systems in the end-cap region. They also
provide the second azimuthal coordinate to complement the MDT measurement. The
TGC’s are multi-wire proportional chambers. The distance of wires to the cathodes is
1.4 mm, and it is smaller than the wire pitch of 1.8 mm. The anode wire diameter is
50 μm with 2900 V applied. They are operated with the gas mixture of CO2 /n-C5 H12(n-pentane). The gas gain is 3 ×105 .

4.4

Magnets

As mentioned above, ATLAS exploits of one solenoidal magnet and three toroidal magnets(Figure 4.23). This is a unique hybrid technology of combining four superconducting
magnets. The overall size of the magnet system is 22 m in diameter and 26 m in length.
It will store energy of 1.6 GJ. The system will provide the magnetic ﬁeld over about
12,000 m3 .
The central solenoid is placed before the LAr EM calorimeter as mentioned before. It
provides 2 T axial ﬁeld. The single-layer coil is made of a Al-stabilised NbTi conductor.
It is 5.8 m long, and the inner and outer diameters of the solenoid are 2.46 m and 2.56
m. It stores the energy of 40 MJ. The cold mass can absorb the stored energy in case ofa quench, with temperature increase up to 120 K at maximum which is a safe value. It
takes about 30 minutes to charge or discharge the solenoid. Its re-cooling to 4.5 K canbe achieved within a day.
63

Figure 4.19: Mechanical view of MDT chamber

Figure 4.20: Structure of CSC

64

Figure 4.21: Cross section of RPC

Figure 4.22: Cross section of TGC

65

Figure 4.23: Schematic view of magnets. Eight barrel toroidal magnets and two setsof eight end-cap toroidal magnets are depicted as red windings. The cylinder with four
layers indicate the Tile Calorimeter and a return yoke. The soleinoidal magnet is drawn
as a red cylinder inside the Tile Calorimeter.
The barrel toroid provides the magnetic ﬁeld in the cylindrical volume surrounding
the calorimeters and both of the end-cap toroids. It consists of eight coils individuallycontained in the stainless-steel vacuum vessels. The overall size of the toroid is 25.3 m inlength with inner and outer diameters of 9.4 and 20.1 m. The large size of the toroidal
magnets allows for good momentum measurement of high energy muons. Basically, thesame conductor and coil-winding technology is used in the barrel and the end-cap toroids.
The technology exploits the winding of a pure Al-stabilised Nb/Ti/Cu conductor intopancake-shaped coils. It will provide approximately 0.5 T in the central region. It storesthe energy of 1.1 GJ, and in case of a quench, the energy will be absorbed by the coldmass enthalpy followed by the activation of four quench heaters per coil which forces theentire magnet to the normal conducting state within less than two seconds.
The end-cap toroids are placed at both side after the end-cap and forward calorimeters. They provide magnetic ﬁeld for the end-cap muon momentum measurement. They
consist of eight coils each with a single cold mass on each side.

4.5

Forward Detectors

There are three detectors so called the forward detectors [65]. The one closest to theinteraction point is the LUCID detector (LUminosity measurement using Cerenkov Integrating Detector), and the Zero-Degree Calorimeter (ZDC) and the ALFA (Absolute
Luminosity For ATLAS) detector follow (Figure 4.24).

66

4.5.1

LUCID (LUminosity measurement using Cerenkov Integrating Detector)

The LUCID measures the relative luminosity. There are several detectors than can measure the luminosity, but the LUCID is unique in the sense that it is primarily dedicatedto the online luminosity monitoring. In order to provide the integrated luminosity andthe online monitoring of the instantaneous luminosity and beam conditions, the inelastic
p-p scattering is detected. It can also be used for the diﬀractive studies, to apply arapidity-gap veto or tag diﬀractive signals.
The concept of the detector to use the array of Cerenkov tubes was originally developed by the CDF collaboration [66]. The LUCID consists of 20 aluminium tubes
set around the beam pipe pointing toward the interaction point (Figure 4.25). The 1.5
m long mechanically polished tubes (diameter of 15 mm) are placed in a light-weight
aluminium gas vessel. The tubes are ﬁlled with C4 F10 at the pressure of 1.2-1.4 bar,which provide a Cerenkov threshold of 2.8 GeV for pions and 10 MeV for electrons. The
LUCID detectors are installed about 17 m away from the interaction point, surrounding
the beam pipe with 10 cm radial distance (|η| ∼ 5.8). The Cerenkov light emitted by theparticles passing the detector will be read out by photomultiplier tubes (PMT’s). The
signal amplitude of the PMT’s provide the numbers of particles per tube, and the fast
timing response allows for the identiﬁcation of the individual bunch crossings.

4.5.2

ALFA (Absolute Luminosity For ATLAS) Detector

ATLAS adopted the traditional technology to measure the absolute luminosity using theelastic scattering at small angles with the ALFA detector. The luminosity can be derived
from the total cross section calculated from the elastic scattering amplitude at forward
regions by the optical theorem. In order to measure at the very small angles (3 μrad),the detector will be located at ± 240 m from the interaction point. The Roman Pot
technique [67] is adopted for the measurement. The technique exploits cylindrical vessels(the pots) that are separated from the vacuum of the accelerator, but equipped withbellows that connects them to the beam-pipe (Figure 4.26). The Roman Pots can reachas close as 1 mm to the beam. Scintillating ﬁber trackers are placed in the Roman Potsto perform position sensitive measurement.

4.5.3

Zero-Degree Calorimeter

The ZDC will measure forward neutrons with |η| > 8.3 in heavy-ion collisions. Such measurement allows to determine the centrality of the collisions, since it strongly correlateswith the number of forward neutrons. During the proton-proton data taking, the ZCD
will contribute as the additional acceptance to the other ATLAS detectors for diﬀractive
processes and can also act as an additional minimum bias trigger. The beam gas andbeam halo background can largely be rejected by requiring a tight coincidence from thetwo arms of the ZDC. It is housed in a slot of the Target Absorber Neutral (TAN) locatedat ±140 m from the interaction point (Figure 4.27), where the beam pipes are separated

67

Figure 4.24: Forward detectors

a)

b)
0
150

310 mm

c)

d)

c
on
nst
i
W

252 mmone

mm
15

170 mmFigure 4.25: LUCID Detector

68

mm

d le
un
b
re
F ib
i er
ipl
t
l
mu
oto
Ph

Front-end boards
Multi-anodephotomultiplierFibre connectors

Upper and lower
Roman pots

10 x u-v fibres

Beam axis

Figure 4.26: The layout of the ALFA Detector. The red line shows the beam axis.

Side view Top view Rear view

Photomultipliers
Multi-anodephotomultipliers

Rod holder

Light guide

Steel

Straight
quartz rods
Beam

Tungsten

Beam

Bent
quartz rods

Figure 4.27: (Left) Schematic view of the ZDC in the TAN. (Right) The electromagnetc
ZDC module.

69

into the two independent beam pipes. The ZDC consists of 1 electromagnetic (EM) module with about 29 radiation lengths and 3 hadronic modules with about 1.14 radiation
lengths each. The EM modules consist of 11 tangsten plates with 91.4 mm wide, 180 mm
high and 10 mm thick. Steel plates are extending above for 290 mm. Quartz rods with
1.0 mm in diameter penetrate the tangsten plates. They are bent vertically by 90◦ andare read out by multi-anode phototubes (MAPMT), which allows to observe Cerenkov
light from shower products from the incident particles. Quartz strips with 1.5 thickness
are also placed between the tungsten and steel plates, and are viewed by photomultipliertubes from above. The strips are placed in order to improve the energy measurement
performed by the position-sensitive rods. The hadronic modules have similar setups. The
main diﬀerences are the way the rods are read out by the MAPMT (4 rods onto 1 pixel
instead of one to one in the EM modules), and not all hadronic modules have position
sensitive rods.

4.6

Trigger System

The trigger system in ATLAS consists of three levels of event selection, which are Level-1(L1), Level-2 (L2), and event ﬁlter [68][69][70]. The Level-2 and the event ﬁlter make up
the High-Level Trigger. The L1 tigger operates with custom-made electronics, whereas
the HLT runs at the software level with commercially available computers and networking
hardware. The overview of the trigger system is shown in Figure 4.28. The L1 trigger
reduces the rate to 75 kHz (can be upgraded to handle in 100 kHz) in less than 2.5 μs,
and it is further reduces down to about 200 Hz by the higher level triggers.
The L1 trigger exploits the RPC, TGC, and all the calorimeter sub-systems with reduced granularity information (Figure 4.29) to search for high pT muons, electrons/photons,
jets, τ -leptons decaying into hadrons, large missing transverse energy (ETmiss ) and totaltransverse energy. Results from the L1 trigger are passed to the central trigger processor where trigger selections are performed based on the “trigger menus” implemented
there. The L1 trigger also provides the information on the Regions-of-Interest (RoI’s):
the η −φ coordinates of the detector regions that the selection process identiﬁed as having
interesting features. The RoI information is passed to the L2.
The L2 selection is seeded by the RoI information received from the L1. The L2
uses the detector information within the RoI’s with full-granlarity and precision. The
L2 trigger reduces the event rate below 3.5 kHz within an event processing time of
approximately 40 ms in average.
The event ﬁlter further reduces the event rate down to about 200 Hz with an average
processing time of order of seconds. The event ﬁlter is based on the oﬄine analysis
procedure to reduce the event rate to be recorded for the oﬄine analysis.
Trigger menus planned for early data with the center-of-mass energy of 10 TeV and theluminosity of 1031 cm−2 s−1 are shown in Table 4.1 and 4.2. For minimum bias studies,“mbSpTrk” starts from random triggered data, and “mbMbts 2,” uses the Minimum
Bias Trigger Scintillator (MBTS). There are various triggers for each physics object suchas electrons, photons, muons, tau leptons, jets, ETmiss , and Sum ET (scalar sum oftransverse energy). In Table 4.1 and 4.2, the numbers before the objects shows the
70

3

200

Figure 4.28: The overview of the ATLAS trigger system.

Calorimeters

Muon detectors

L1 trigger
Calorimeter triggers

EM



Jet

Muon trigger

Emiss
T

μ

ET

Central trigger
processor

Timing, trigger andcontrol distribution

Regionsof-Interest

Detector front-ends

L2 trigger

DAQ

Figure 4.29: The block diagram of the Level-1 trigger.

71required multiplicity, and the numbers after the objects corresponds to the transverse
energy threshold (e.g. “3j20” means three jets with transverse energy large than 20
GeV).

72

Table 4.1: Primary trigger menus proposed for the luminosity 1031 cm−2 s−1 at the centerof-mass energy 10 TeV [71]. Prescales are respectively shown for the Level-1, Level-2,
and the Event Filter. For “mbSpTrk,” prescale at the Level-1 is applied on top of therandom trigger.
Type
Minbias
Minbias
e/γ
e/γ
e/γ
e/γ
e/γ
e/γ+X
μ
μ
μ
τ
τ
τ
τ +X
τ +X
τ +X
τ +X
τ +X
τ +X
τ +X
B
B
B
B
B
B
B
B
B
B

Name
Rate [Hz]mbSpTrk
3.85
mbMbts 2
4.00
e10 medium
26.6
2e5 medium
1.85
e20 loose
6.17
em105 passHLT
1.03
g20 loose
8.02
g25 loose xe30
0.1
mu10
21.8
2mu4
15.4
mu20 MSonly—
tau50 loose
22tau 20i loose
1.3
2tau 29 loose1
tau12 loose e10 loose
1.95
tau16 loose xe25
∼ 10
tau16i loose EFxe30
3.6tau16i loose mu10
1.34
tau16i loose 3j40
2.8
tau16i loose j120
1.65
tau16i loose 2b23
<0.01
MU4 DiMu FS
0.62
MU4 Jpsimumu FS
0.1
MU4 Upsimumu FS
0.51
MU4 Bmumu FS—
2mu4 DiMu
0.62
2mu4 Jpsimumu∼0
2mu4 Upsimumu
0.10
2mu4 Bmumu—
MU4 Jpsie5e3 FS
13.3
mu4 DsiPhiPi FS
8.54

73

PrescalePhysics/Motivations
5∗ ,9,1
Minbias184000,1,1
Minbias
1,1,1
SM, BSM
1,1,1
SM
1,1,1
SM
1,1,1
check for problems
1,1,1
SM, GMSB
1,1,1
W, Zγ, LED
1,1,1
B,W,Z,Top
1,1,1
B,W,Z
1,1,1
1,1,1
BSM, Higgs
1,1,1
Higgs
1,1,1
Higgs
1,1,1
Z,Top,Higgs,SUSY
1,1,1
W(→ τ ν)1,1,1
W,Z,Top,Higgs
1,1,1
Z,Top,Higgs
1,1,1
SUSY
1,1,1
SUSY
10000,1,1
SUSY,Higgs
1,1,1
B,low-mass Drell Yan
1,1,1
J/ψ,B xsec
1,1,1
Υ
1,1,1
B,low-mass Drell Yan
1,1,1
B,low-mass Drell Yan
1,1,1
J/ψ,B xsec
1,1,1
Υ xsec
1,1,1
J/ψ,B xsec
1,1,1
J/ψ(→ ee)
1,1,1
B with DS decays

Table 4.2: Primary trigger menus proposed for the luminosity 1031 cm−2 s−1 at the centerof-mass energy 10 TeV [71]. Prescales are respectively shown for the Level-1, Level-2,
and the Event Filter.
Type
Jet
Jet
Jet
Jet
Jet
Jet
Jet
Jet
Jet
Jet
Jet
Jet
Jet
Jet
Jet+X
Jet+X
b-jet
b-jet
b-jet
b-jet
b-jet
b-jet
ETmissETmissSum ET

Name
Rate [Hz]
j10v3
0.79
j20v2
0.79
j40
0.99
j80v2
1.37
j140v21.37
j200v2
3.73
j260
4.04
3j20
0.2
3j40
0.14
3j60v2
0.51
4j20
0.28
4j30
0.22
4j40
0.39
2fj18
0.07
j80 xe30—
2j40 xe30—
2b20 3L1J20
1.5
3b20 4L1J20
0.001
1b40 2b20 3L1J10—
1b40 2b20 3L1J20—
2b40 3L1J20—
3b20 4L1J10—xe30—
xe35 tight
—
te360
0.3

74

Prescale Physics/Motivations
2000,100,1
QCD
1000,100,1
QCD
50,100,1
QCD
3,100,1QCD
1,50,1
Monojet, BH
1,10,1
Monojet, BH
1,1,1
Monojet, BH
200,100,1
QCD, BSM
1,10,1
QCD, BSM
1,1,1
QCD, BSM
20,100,1
QCD, BSM
20,1,1
QCD, BSM
1,1,1
QCD, BSM
100,1,1
Diﬀraction
1,1,1
Top, SUSY
1,1,1
Top, SUSY
100,1,1
Hadronic Top
1,1,1
Higgs
20000,1,1
Debugging
100,1,1
Debugging
100,1,1
Debugging
4000,1,1
Debugging—
SM, SUSY—
SM, SUSY
40,1,1
Black HolesChapter 5
Energy Reconstruction in the
ATLAS Calorimeters
In this chapter, the physics of calorimetery is brieﬂy reviewed in Section 5.1. In Section 5.2 and 5.3, the energy reconstruction schemes in the ATLAS Liquid Argon and Tile
Calorimeter are mentioned.

5.1

Introduction: Physics of Calorimetry

Calorimetry is one of the major detection methods widely used in particle physics tomeasure the energy and direction of incoming particles [72][73]. Calorimeters make useof large amounts of materials in which particles traversing them deposit energy by electromagnetic or hadronic showers. Even weakly interacting particles such as neutrinos canbe indirectly detected by measuring the energy imbalance in the whole detector (in caseof hadron colliders, only the imbalance in the transverse momentum carry information,
due to the fact that the energy of interacting partons is unknown).
Calorimeters can be grouped into electromagnetic calorimeters and hadronic calorimeters. Electromagnetic calorimeters mainly measure electrons and photons by capturing
their electromagnetic showers. Hadronic calorimeters measure energy of hadrons by detecting the outcome of the strong interactions, though the processes are more complicatedthan the electromagnetic cascade and makes the detectors more diﬃcult to optimize.
The calorimeters can further be grouped into sampling calorimeters and homogenious
calorimeters. Sampling calorimeters consist of consecutive layers of two kinds of materials,
the absorber and the active medium. The absorber is made up of a dense material in orderto develop electromagnetic or hadronic showers. The active medium provides signals tobe detected. Homogenius calorimeters are made up of only one type of material thatsimultaneously oﬀers shower development and signal generation.

5.1.1

Energy Loss of Charged Particles Through Matter

For moderately relativistic charged particles except for electrons, ionization and atomic
excitation are the major physics processes which those particles lose their energy. The
75

Figure 5.1: Stopping power for positive muons in copper (PDG, 2008)
mean rate of the energy loss is described by the Bethe-Bloch formula [74].
−Z 1 1 2me c2 β 2 γ 2 TmaxΔ(βγ)
dE
= Kz 2ln− β2 −
2
2
dx
Aβ 2
I
2

(5.1)

In the equation, K stands for 4πNA re2 me c2 (NA : Avogadro’s number, re : classical electron
radius, me : electron mass). Z is the atomic number of the absorber material in which
the particles are traversing, A is the atomic number of the absorber, β and γ follow thestandard meaning in relativity, Tmax is the maximum energy that can be given to a freeelectron in a collision, and Δ(βγ) is the density eﬀect correction to ionization energy loss
as a function of βγ.
At lower energy, various corrections have to made [74] as is obviously seen in Figure 5.1. At higher energy, the eﬀects of radiative losses become prominent, such as
bremsstrahlung, direct pair production, and photo-nuclear interaction.
Muons with a momentum of a few hundred MeV/c to a few hundred GeV/c have amean energy loss very close to the minimum ionization and are called minimum ionizing
particles (MIP’s). However, MIP’s are hypothetical ideal particles, and muons are not
MIP’s in a strict sense [73], since in the high energy region even muons undergo radiative
losses, and also in the low energy region the energy loss is far greater than the minimum
ionization energy loss. These subtle diﬀerences have to be considered when muons areused for calibration purposes.

765.1.2

Interactions of Electrons and Photons in Matter

The fractional energy loss of electrons and positrons (will be mentioned unitedly as“electrons” in the following description unless explicitly mentioned) in lead is shown in
Figure 5.2 [74]. When the incident electrons have small energy, the main physics processthat account for the energy loss is the ionization. There are also small contributions from
Møller scattering for electrons, and Bhabha scattering for positrons. Positrons can alsoannihilate with the electrons in the matter. Since electrons have a small mass, the main
source of energy loss is bremsstrahlung for incident energy larger than ∼ 10 MeV.
Figure 5.3 shows the cross section of photons in matter. At low energy, the largest
contribution comes from the photoelectric eﬀect, which an atom in the matter absorbs aphoton and emits an electron. The excited atom returns to the ground state and emits Xrays or Auger electrons. Rayleigh scattering also has a large cross section for low energy
photons, though the photons are simply deﬂected by an atomic nucleus and does not leadto an energy loss. Compton scattering is the most probable process in a few hundred
keV to ∼ 5 MeV energy range. In this process, a photon scatters with an electron in
an atom, and enough energy is transferred to the electron so that it becomes unbound.
Around a few tens of MeV in Lead, photonuclear interactions contribute to the energy
loss. In such processes, the nucleus is broken up. At high energy, electron-positron pair
productions by nuclear or atomic electron ﬁeld become dominant.

5.1.3

Electromagnetic Cascade

As mentioned in the previous section, electrons and photons with suﬃciently high energy
will create secondary photons by bremsstrahlung, or originate secondary electrons andpositrons by pair productions. These secondary particles will initiate the same process
and thus form a cascade of particles. Such a phenomenon is called the electromagnetic
cascade or the electromagnetic shower. The number of particles increase until the electrons lose enough energy so that the energy can only be degraded through ionizations.
Critical energy  indicates the energy when the main cause of the energy loss changes fromradiative losses to ionization and excitation. There are two standard deﬁnitions of thecritical energy; one is deﬁned as the energy when the radiative energy losses equals thosefrom ionization, and the other is the energy when the energy losses from ionization perradiation length (explained below) equals the traversing electron energy (the deﬁnition
used in [74]).
The longitudinal and lateral size of electromagnetic showers can be described by aparameter so called the radiation length X0 . It represents the average distance that anelectron or positron traverses in matter to reduce the energy down to 1/e of the original
energy E0 . Thus, the mean energy of electrons after traveling the distance x is describedasE(x) = E0 e−x/X0 .
(5.2)
Its value is material-dependent and can be expressed as
X0716A√ [g/cm2 ]Z(Z + 1)ln(287/ Z)
77

(5.3)

Figure 5.2: Fractional energy loss per radiation length in lead as a function of electron
or positron energy [74]Figure 5.3: Photon total cross sections as a function of energy in carbon and lead [74]78

where Z is the atomic number of the material and A is the atomic weight.
Similarly, the average intensity of photons I(x) as a function of the traveling distanceis,I(x) = I0 e−(7/9)(x/X0 ) ,(5.4)
where I0 is the original intensity. The factor 7/9 comes from the diﬀerence in the crosssections of electron’s bremsstrahlung and photon’s pair creation. Intensity was used todescribe the attenuation of photons, because unlike the charged particles, a single photon
is either absorbed completely or scattered by a large angle by an interaction.
The transverse spread of electromagnetic showers mainly ascribes to the multiple
scatterings of electrons and positrons. Bremsstrahlung photons and their secondary particles also contribute to further spread, due to their non-zero angles of the emission. The
Molière radius RM gives a transverse size deﬁned from measurements over the full shower
depth, and can approximately described as
RM

21

X0
[g/cm2 ]

(5.5)

About 90 % of the energy is contained within a cylinder of Molière radius RM , andprovides an indication of segmentations of calorimeters.

5.1.4

Hadronic Cascade

The energy loss of hadrons occurs through the sequential strong interactions as electromagnetic cascade does through electromagnetic interactions, though the process is far
more complicated than the electromagnetic cascade. Characteristic processes in hadronic
cascade are production of secondary hadrons by the strong interaction within a mean freepath (so called an “interaction length λ”), thus the mean energy of high energy hadrons
after traveling the distance x is described as
E(x) = E0 e−x/λ ,(5.6)

where E0 is the original energy of incident hadrons.
A large component of secondary hadrons are pions, and about one third of them
are neutral pions decaying into two photons and depositing all of their energy in thecalorimeter (so called the electromagnetic component). The fraction of electromagnetic
component in the hadronic shower is energy-dependent and increases with the incident
hadron energy. Thus, neutral pions (and also other neutral mesons such as η) do notcontribute to the development of the cascade, but play dominant roles in the measurementof energy by depositing all their energy by the photons. The remaining charged hadrons
deposits energy by ionization and may also expand the cascade further by sequential
strong interactions. The visible energy deposits from hadrons occur through the neutralmeson decays into photons, ionization by charged hadrons, and interaction of neutrons
with matter (e.g. elastic and inelastic scattering, neutron capture, (n, α) reactions). Theenergy deposit from the ionization of charged hadrons, kinetic energy evaporation ofneutrons, and invisible energy (will be explained below) is called the non-electromagnetic
component of the hadronic cascade.
79A peculiar aspect of hadronic cascade is the presence of “invisible energy,” whichindicates that a certain fraction of the hadronic cascade is fundamentally undetectable,in contrast to the electromagnetic cascade where all the energy is deposited and detectedin the calorimeter. When, incident hadrons strike a nucleus in the absorbing material,
the spallation process may occur (and a creation of hadrons may accompany in case ofhigh energy incident, which lead to the development of cascade as mentioned above). In
the spallation process, some nucleons inside the nucleus are excited. When the excitednucleus goes through de-excitation, it is broken up and emit some particles, mainly freenucleons, until the excitation energy is less than the binding energy of a nucleon. In thisprocess, some fractions of energy are used to release the nucleons from the nucleus tosurmount the binding energy and are lost. This is the source of the “invisible energy.” Thefraction of this invisible energy has a large ﬂuctuation, due to the diversity of processes
taking place in the hadronic cascade. The contribution of the invisible energy can be
zero in the extreme case (e.g. π + n → π 0 p), or may use up 60 % of the energy during
the spallation process. On average, 30 % - 40 % of the non-electromagnetic component
disappear as the invisible component.

5.2

Energy Reconstruction in LAr Calorimeters

An overview of the LAr readout electronics is shown in Figure 5.5 [75]. LAr ionizationelectrons drifting in the LAr gap with electric ﬁeld applied by the high voltage are collected by the electrode in each cell. The raw signals are passed through the cryostat
feed-throughs and mapped on to the Front End Board (FEB) inputs. Then, the preampliﬁer ampliﬁes the signals, and passed onto the shaper where the signals are split into
four. Three of the signals are shaped in three gains (high, medium, and low) and theremaining signal of the four is used for the Level-1 calorimeter trigger. This multi-gain
system is adopted in order to cover the large dynamic range (∼ 1 GeV to 5 TeV) required from the physics motivations (mentioned in Section 4.2.1). The three linear gains
are in the ratio of 1/10/100. Then, a bipolar CR − (RC)2 analogue ﬁlter is applied to
optimize the signal-to-noise ratio. The time constant of 15 ns was chosen to minimize
the overall noise (the electronic noise and the pile-up noise; see Figure 7.1). When an
event is triggered, the signals are subsequently digitized by a 12 bit ADC (Analog to
Digital Converter) every 25 ns (Figure 5.6). The pedestal is around 1000 ADC counts to

Figure 5.4: Cell energy reconstruction formula in LAr.

80

Figure 5.5: Diagram of the Liquid Argon Calorimeter read-out electronics.
accommodate the undershoot of the shaper. During the collision data taking, the pulse
will be sampled and digitized in ﬁve points every 25 ns, but for the calibration and othercommissioning purposes, more than 5 samplings were recorded in order to investigate thepulse shape in detail (32 samplings at most). For the Tier-1 reprocessed cosmic and single
beam data (the Tier-0 grid site runs the ﬁrst reconstruction of data, and Tier-1 takes
care of re-running’s of the reconstruction with updated database and software setups;
see “Acronyms and Terms” and Chapter 6), only the ﬁrst 5 samplings were used for theenergy reconstruction, regardless of the actual recorded number of samples, in order toreconstruct the signals in the same condition as the collision data taking, where only theﬁrst ﬁve samples are recorded.
The formula in Figure 5.4 describes the electronic calibration chain of the LAr cell
energy. Optimal Filtering Method [76] (described in the next section and B) is used forthe energy and time reconstruction in LAr [77], and the energy is reconstructed from
Nsamplesthe pulse height in ADC counts (corresponds to j=1
aj (sj − p); aj is the optimalﬁltering coeﬃcients (OFC), sj is the ADC count in each sampling, p is the ADC countof the pedestals, and Nsamples is the number of samplings). The energy is converted fromthe ADC counts to DAC (Digital to Analog Converter) values by so called the Ramp
factor Ri derived from calibration pulses. The subtle diﬀerence in the signal heights inthe calibration and physics runs are corrected by Mphys /Mcali factor (∼ 1.05) in order toperform precise energy reconstruction. The DAC value is further converted to the current
value determined by the characteristics of calibration board. The current value is ﬁnallyconverted to MeV values considering the sampling fraction and various parameters of theelectrodes.
The details of each constant are mentioned below.

81Figure 5.6: Form of the ionization signal and the shaped ionization signal in the LAr EM
calorimeter

5.2.1

Optimal Filtering Method and CoeﬃcientsThe Optimal Filtering method is a sophisticated method to compute the peak of the
shaped signals with reduced sensitivity to channel-to-channel variations, electronic andpile-up noise.
The amplitude of the signal (A) and the time oﬀset from the digitizer output (τ ) arewhat we want to measure. They are derived from the weighted sum of the digital samples
as below.
NsamplesA =aj (sj − p)

(5.7)

bj (sj − p)

(5.8)

j=1



Nsamples

Aτ =

j=1

Here, the ADC count-values from each sampling sj are the only inputs from the measurement. The OFC’s (aj , bj ) and the average pedestal value for each channel (p) aredetermined beforehand by series of calibration runs. Nsamples is the number of samplings
used for the energy reconstruction, and ﬁve samplings are used during the collision datataking as mentioned before. The details on the mechanism of the Optimal Filtering
Method is described in Appendix B.

82

5.2.2

Ramp Factor: ADC → DAC

The Ramp factor Ri converts ADC counts to DAC values. It is measured regularly by
the electronic calibration system.
In the calibration runs, each cell is pulsed around 100 times with a given input currents
typically ranging from 0 to 1500 DAC values [77]. Figure 5.7 shows the ADC peak values
as a function of input DAC values in the ﬁrst sampling layer of the electromagnetic
calorimeter. The Ramp factors are extracted by ﬁtting the DAC-ADC relation in the
non-saturating region with a polynomial function. Currently, the conversion is treated
only linearly (thus only the ﬁrst order polynomial is considered; only R1 in Figure 5.4),
but non-linearity of the electronic chain can be taken into account by including higher
polynomial terms (i ≥ 2).

5.2.3

The Mphys /Mcali Factor

The Mphys /Mcali factor corrects the Ramp factor considering the diﬀerence of the signal
heights between the calibration and the physics runs (Figure 5.8). The diﬀerence in thepulse shape arises because in the actual physics runs, the signals initiate inside the capacitors, whereas in the calibration runs, the signals are injected outside the capacitor.
This correction factor is needed to achieve the required energy resolution in the electromagnetic and hadronic end-cap calorimeter. However, the Forward Calorimeter does
not predict the ionization pulse shape from the calibration runs, since the performancerequirement is more relaxed compared to the EM and hadronic end-cap calorimeters. In
those calorimeters, Mphys /Mcali are around 1.05 as mentioned before, but set to 1 andnot considered for the Forward Calorimeter.

5.2.4

The FDAC→µA Factor

The FDAC→μA factor converts the DAC value to the current (μA). This factor is determined by the characteristics of the calibration board and the injector resistor on themotherboard as shown below.
79.295 μV
FDAC→μA =
.
(5.9)
Rinj
Rinj is the injection resistor.

5.2.5

The FµA→MeV Factor

This factor converts the current value to the energy at the MeV scale. It consists of theenergy-to-current conversion factor E/I and the sampling fraction factor fsamp .
1.
f
E sampFμA→MeV = I(5.10)The energy to current conversion factor is described aseEI=
frecomb (E)Vd (E) ,
E
W0
U
83

(5.11)

g(t) (a.u.)

Figure 5.7: ADC peaks and input DAC values in the ﬁrst sampling layer of the electromagnetic calorimeter [77]1calibration pulse

0.8

ionisation pulse

0.6
0.4
0.2
0
-0.2
0

100

200

300

400

500

600

700

800
t (ns)

Figure 5.8: Pulse shape from calibration runs and physics runs (black line is the pulse
shape from calibration runs, and the red line is from the physics runs) [78]84

where e is the unit electric charge (=1.6×10−19 C), W0 (=23.6 eV) is the ionization
potential of Liquid Argon, E is the electric ﬁeld in the channel, frecomb is the correction
factor for recombination eﬀects (∼ a few %), Vd (E) is the drift velocity, and U is thevoltage applied between the electrodes (∼ 2000 V).
The sampling fraction is the ratio between the energy deposited in the active layer
and the total energy deposit. It can be estimated by considering the energy deposited bya MIP in active material and the absorber, plus a correction factor e/mip to take care ofadditional energy deposit by electrons’ radiation.
e(dE/dx)mip
activefsamp =
×mip
mip (dE/dx)active + (dE/dx)mip
absorber

5.3(5.12)

Energy Reconstruction in Tile Calorimeter

The TileCal is also a sampling calorimeter. The scintillation light in the tile producedby the impinging particles is propagated by the WLS ﬁbers and read out by the PMT’s.
The PMT block consists of a light mixer, a photomultiplier tube, a voltage divider,and 3-in-1 card (Figure 5.9). The light mixer is an optical plastic insert and it mixes
the light from WLS ﬁbers to allow for uniform illumination of the photo-cathode of the
PMT. The 3-in-1 card is used for three purposes as expressed in its name. First of all, a
unipolar signal shaping is performed with a time constant of 50 ns, and two outputs witha relative gain of 64 (so called the low and high gain) are given. This bi-gain scheme wasadopted to cover the large dynamic range (∼ 20 MeV to ∼ 1.3 TeV) just as in the case of
LAr’s tri-gain system mentioned in Section 5.2. Secondly, the charge-averaging ampliﬁers
on the 3-in-1 card are used for used for the cell calibration and monitoring. Thirdly, the
3-in-1 card also provides analogue signals to be used for the Level-1 Calorimeter triggers.
The bi-gain outputs are then passed onto the digitizer and digitized every 25 ns using
two 10 bit ADC’s. Seven digitized samples are used for the energy reconstruction (ninesamples can be recorded and used at most).
The Optimal Filtering method (explained in the previous section) was used since theTier-1 reprocessing in December 2008, but the “Fit Method” [79] [95] has been used fora long time during the Tier-0 reconstruction in the cosmic and single beam runs of Fall,
2008.
Fit Method uses the knowledge of the pulse shape of the signal from the front-end
electronics to extract energy and timing. For each channel, ﬁt is done with the function,f (t) = Ag(t − τ ) + p(5.13)

where, f (t) is the function, A is the amplitude of the signal, g is the normalized pulse
shape previously determined from calibration runs, τ is the timing, and p is the pedestal.
The χ2 -minimization is used for the ﬁt.

85

Calorimeter
Tiles
Bulk LVPS

Bulk HVPS

Particle energy

FibresCesiumFinger
LVPS

CANbus

PMT blockLight mixer
HVmicro−processor

HVopto−coupler

Laser

Photomultiplier
Divider
3−in−1 Card

ProbesMother−board
Digitiser

Charge injection
systemADC

Mezzanine

CANbusAdder

Pipeline

Integrator

Interface

Current calibration

ROD

Charge or energy

Muon
L1 trigger

TTC

CalorimeterL1 trigger

Figure 5.9: Diagram of the TileCal read-out electronics

5.3.1

Energy Calibration in Tile Calorimeter

For the accurate measurement of the energy in each channel, TileCal has sophisticatedcalibration systems to take care of all the signal paths in the calorimeter (Figure 5.9,5.10). The systems consist of the electronic Charge Injection System (CIS) [99][100]solely for monitoring the readout, the pulsed laser system for monitoring the PMT andreadout response, a movable Cesium (137 Cs) radioactive source for monitoring the scintillator and PMT response, and a Minimum Bias (MB) monitoring system which will use
Minimum Bias events to monitor the calorimeter response during physics runs TileCal
has an independent readout (monitor system electronics) for the Cs and MB monitoring
systems in addition to the fast readout used for the laser, CIS calibration systems, andfor events from the physics runs.
The energy reconstruction of TileCal can be summarized as follows [81],Ecell = [FGeV→pC ]−1 × [FpC→ADC ]−1 × FLAS × FCs × AOFC [ADC](5.14)

where Ecell is the reconstructed cell energy at the GeV scale, FGeV→pC is the EM scale
factor determined from the test-beam analyses, FpC→ADC is the ADC/pC conversion
factor determined by the Charge Injection System, FLAS is the correction factor for nonlinearity measured by the Laser Calibration system, FCs is the correction factor fromthe Cesium Calibration system to apply to achieve better uniformity among the TileCalcells, and AOFC [ADC] is the amplitude of the pulse determined by the Fit Method or the
Optimal Filtering Method.
The characteristics of each calibration system will be brieﬂy mentioned below.

86

CIS

Figure 5.10: Conceptual diagram of calibration in TileCal; L(E,S,O) is light response(E=energy, S=sampling fraction, O: response of optical component (scintillator and WLS
ﬁbers)). Light pulse is converted into a charge Q(n,G,L) at PMT’s (n=quantum eﬃciency,
G=gain of the PMT). Figure revised from [61].

Figure 5.11: Response to a CIS pulse of 2 pC in the high gain (left) and 560 pC in the
low gain (right)[95].
Charge Injection System: ADC → pC
In between physics runs, pedestal, laser, and CIS runs will be taken. The CIS is a system
which injects well deﬁned charge into fast bi-gain electronics. This system provides
ADC/pC conversion for both gains. In Figure 5.11, the left ﬁgure is for the high gain
and the right for the low gain. The histograms show the the ADC samples, the dottedlines show the contributions from the capacitance of the switch, and the solid lines show
the overall ﬁt. It also provides an oﬄine correction for nonlinearity in the low-gain.
Laser Calibration: Non-linearity Correction
Laser runs will also be taken in between physics runs to understand PMT responsechanges in regards to linearity and gains. Laser pulses are sent to a set of photodiodes
and their response is monitored. The laser system can measure the absolute gains of each87

PMT as well, and is also used for time calibration of the readout digitizers.
Cesium Calibration: Uniformity Correction
The Cs scans are also done outside the beam periods. The 137 Cs γ-source (Eγ = 0.662
MeV, half-life t1/2 = 30.2 y) is used. The source ﬂows through the holes in scintillatingtiles (Figure 4.10) with a uniform speed, and monitors the response for each channel.
They allow for the cell intercalibration to equalize the signal response from all the cells,
and also to monitor the cell response in time. The EM scale factor (pCb/GeV) is deﬁned
from testbeam analyses as mentioned below, and the same factor is applied to all thecells. Online cell intercalibration is done by adjusting the high voltage (HV) of the PMT’s
based on a fast analysis of the Cs scanned data.
Minimum Bias Monitoring System
The Minimum Bias monitoring system plays complementary roles to the Cs scans, due tothe advantage that it can operate during the collision data taking, making use of the soft
inelastic proton-proton collisions called the Minimum Bias events. Since the Minimum
Bias events are uniform in φ with a moderate dependence in η, and occur with very highrate, they induce quasi-DC currents in the PMT’s. Such feature of the Minimum Bias
events allows to monitor the response of each channel in time.
EM Calibration from the Combined Test Beam Runs
As mentioned in the Cs section, the EM scale factor is determined from the testbeam
runs using electrons with the energy from 20 GeV to 180 GeV. The measurement was
performed with data from 3 long-barrel and 5 extended-barrel modules. The responses
from about 200 cells are shown in Figure 5.12. The mean value for the energy response
was 1.050 ±0.003 pC/GeV. The RMS of the distribution was 2.4 ± 0.1 %.

5.4

Hadronic Calibration in ATLAS Calorimeters

The hadronic energy calibration in ATLAS is applied on top of energy at the EM scale(mentioned in Section 5.2 and 5.3), in order to successfully reconstruct the actual energyof impinging hadrons. Such a procedure is needed due to the fact that the responseof the calorimeters to the EM component and the non-EM component of the hadronic
cascade diﬀers (this relation is called non-compensation). So, the oﬄine reconstruction
software recovers the compensation. Two standard algorithms exist in ATLAS for the
purpose [23]; one is the H1-style calibration which was used in the H1 experiment at
the electron-proton collider HERA (the Hadron Electron Ring Accelerator) located at
DESY (Deutsches Elektronen-Synchrotron) [83][84] and the other is the Local Hadronic
Calibration [85][86].

88Figure 5.12: The cell response of electrons at 20◦

5.4.1

H1-style Calibration

The H1-style calibration is a global hadronic calibration method, which the correction isapplied at the cell-level in regards to the energy density of the cells. It makes use of thefact that the EM component of the hadronic cascade makes a highly dense energy deposit,
but the non-EM component deposit its energy in more broad and less dense ways. In
ATLAS, jets are reconstructed from calorimeter clusters at the EM energy scale, and H1
calibration factors are later applied to cells inside the jets to reconstruct the energy E at
the hadronic scale,
N
cell

E=
wiEiEM .
(5.15)i

Here, i corresponds to the cells inside the jet, wi is the H1 calibration factor to be applied
to the cell, and EiEM is the cell energy at the EM scale. These correction factors dependon the calorimeter region and the cell volume. The weights are deﬁned as,
Np −1

w

k,j

=



akm logm

m=0
(k)

 
E
V j

(5.16)

where, Np is the number of parameters, am is the parameters to be deﬁned from a ﬁt with
Monte Carlo expectation, k is the index for the calorimeter region (total of 17 regions),
89

and j is the categorization of the energy density into 16 ranges deﬁned as,⎞⎛ E EMln V icell
i+ 26⎠ .
j = int ⎝ln 2(5.17)

Thus, one of the 17 × 16 weights are applied to the cells inside jets considering theirlocation in the calorimeter and energy density. The set of parameters akm are extracted
from ﬁts using QCD dijet Monte Carlo simulation samples to recover the actual hadronicenergy.

5.4.2

Local Hadronic Calibration

The Local Hadronic Calibration takes diﬀerent strategy compared to the H1 calibration.
Here, hadronic calibration is not performed after the jet reconstruction from clusters at
the EM energy scale, but at the calorimeter cluster level before the jet reconstruction.
So called the Topological Clusters (or Topoclusters; see Section 7.3.2) are used. The
Topoclusters are categorized into “electromagnetic-like” or “hadron-like” using various
cluster variables, such as lateral and longitudinal spread of the clusters, energy density,
energy fraction in the EM calorimeter, the energy fraction of the most energetic cell, etc.
and their moments. The hadronic calibration weights to be applied are derived fromGeant4 simulations of single charged pions to recover the actual energy.

90Part II
Commissioning of the ATLAS
Calorimeters

91Chapter 6
Cosmic and Single Beam Runs
6.1

Cosmic Runs

Figure 6.1: First “data” in the LHC recorded by the ATLAS Tile Calorimeter [89].
The ATLAS detector has been undergoing an extensive period of in-situ commissioning, including a series of cosmic ray runs which began in June 21, 2005 [87] [88], andcontinued in 2006-2008, in preparation for the ﬁrst LHC beam in September 2008. TileCal was the ﬁrst sub-detector to be included in ATLAS, and took the “ﬁrst data” in the
LHC [89] (Figure 6.1).
The data from these runs allowed us to understand and to have an overall view ofthe detector conditions, and also formed a useful training ground to validate the ATLAS
oﬄine software algorithms and study reconstructed objects such as calorimeter cells,
clusters, jets, ETmiss , and tracks from inner detector and muon spectrometers.
Since December 2006, combined cosmic runs so called “Milestone Weeks” began which
ran with various sub-detectors included in the data-taking. The objective of the Milestone92

Table 6.1: ATLAS Cosmic Ray Milestone Weeks and Beyond.
Period
M1
M2
M3
M4
M5
M6
M7
M8
Fall 2008

Dates
Dec. 11 - Dec. 19, 2006
Feb. 28 - Mar. 13, 2007
Jun. 4 - Jun. 18, 2007
Aug. 22 - Sep. 2, 2007
Oct. 22 - Nov. 5, 2007
Mar. 5 - Mar. 11, 2008
May 13 - Jun. 3, 2008
Jul. 11 - Jul. 20, 2008
Sep. 13 - Dec. 2, 2008

Weeks was to operate the ATLAS detector as a whole, using the sub-detector systems
available during the period. Eight Milestone Weeks have undergone since then, andthe number of sub-detectors were included during the data-taking has been increasing“week by week”. The last Milestone week (M8 Week) experienced the complete combined
operation which all the sub-detectors were included and successfully received data. The
period of Milestone Weeks is shown in Table 6.1. Even in between Milestone Week period,
the data-taking has been on-going, but emphasis was on the stand-alone data taking ofeach sub-detector for pedestal measurement, calibration studies, and etc.
After the ﬁnal Milestone week, the largest set of combined cosmic runs were recorded.
Most of the analysis results shown in this thesis are from the selected runs in such
“post-M8” data taking period. The integrated statistics of recorded cosmic data since
September 13, 2008 is shown in Figure 6.2. In some runs, LAr was taken out, whichallowed higher trigger accept rates of about 500 Hz (rises seen in Figure 6.2). Morethan 500 million events were recorded by ATLAS in 2008, which correspond to raw
data of about 1.2 PB and derived data of about 700 TB. Runs with fairly long datataking time (more than 500,000 events) and good detector conditions were selected from
September 13 to December 1, 2008, and those 127 selected cosmic runs (from run 88425
to 96982) were reconstructed from the raw data by the ATLAS production team using
the Tier-1 computing facility. Of the 127 runs, 39 runs were without solenoidal andtoroidal magnetic ﬁeld (categorized as “data08 cos”), 21 runs were Inner Detector speciﬁc
commissioning runs (“data08 idcomm”), and remaining 67 runs were combined cosmic
runs with magnetic ﬁeld (“data08 cosmag”). The central reprocessings occurred twice (asof May 2009). One in December 2008 and the other in March 2009. The understandingsof the ATLAS detector were improved with the ﬁrst reprocessed data, and the knowledge
was reﬂected in the reconstruction algorithms, which led to the second reprocessing.

93

Figure 6.2: Cosmic Data recorded in the Fall of 2008.

6.2

Single Beam Runs

The historical ﬁrst beam of the LHC has circulated the ring on September 10, 2008. The
circulation of beams in both directions has been achieved, and many beam halo particles
were recorded by the ATLAS detector. Figure 6.3 shows the ﬁrst single beam data witha large “beam splash” (the proton bunch hitting the collimator before the ATLAS andoriginating huge numbers of particles). The single beam data taking took place from
September 10 to 13. Ten single beam runs with fairly long data taking time and good
detector conditions were selected and have been included in the central reprocessing
mentioned in the previous section.
Due to the accident in the LHC on September 19, 2008, no more beam circulation
was possible, which is mentioned in the next section. Further single beam data taking
will occur in 2009, which will be followed by beam collision data takings possibly at theend of 2009.

6.3

LHC Accident

The malfunction was initially caused by a faulty electrical connection between two of theaccelerator’s magnets [90]. It lead to the mechanical damage and released helium fromthe magnet cold mass into the LHC tunnel. No one was injured during the accident, due
to the proper safety procedures.
94

Figure 6.3: First single beam data recorded in ATLAS on September 10, 2008.

6.4

Data Streams

During the collision data takings, data will be collected in 4 event streams based on thetrigger decisions. The event streaming determines the ﬁnal ﬁle destination of an event,and the main motivation of the streaming is to decouple the HLT from DAQ. A single
event can belong to several streams.
• Physics stream: data used for the physics analyses• Calibration stream: data speciﬁcally used for detector calibration purposes• Express stream: the full events with the fast reconstruction for monitoring anddata quality purposes, only containing partial information• Debug stream: events which the trigger could not make a decision due to failuresin the online system
Calibration and express streams were not operating during the cosmic and single beamdata taking in 2008. In this thesis, we will look into the commissioning data belong to
the physics streams.
95

Many trigger menus which will be used during the collision data taking were validated
during the cosmic commissioning. On top of the trigger validation, in order to eﬃciently
record the cosmic muons reaching the detector, some trigger settings have changed thethreshold or a part of their criteria from the standard collision settings, and there werealso speciﬁc triggers for tagging cosmic muons.

6.4.1

Physics Streams during Cosmic Runs

There were 11 physics streams that operated during the post-M8 cosmic data taking:L1Calo, L1CaloEM, RPCwBeam, TGCwBeam, CosmicDownwardMuons, CosmicMuons,
IDCosmic, TRTCosmic, MBTS BCM LUCID, RNDM and LateEvents stream. Eachphysics stream consists of a set of particular trigger menus (L1Calo stream consists ofevents triggered by the Level-1 Calorimeter, for example). There are overlaps of the same
events among the physics streams.
The same physics stream names were used for the cosmic and single beam runs, but
their names do not always describe the precise trigger settings actually used.
L1Calo
The L1Calo stream contains events triggered by the Level-1 Calorimeter (L1Calo; Table 6.2). The events are stored by taking the “OR” of all the L1Calo trigger bits.
“L1 EM3” triggers on events with EM trigger towers passing the energy threshold of
3 GeV.
L1CaloEM
The L1CaloEM stream is a complete duplicate of the subset of the L1Calo stream, which
the EM trigger towers passed the threshold.
RPCwBeam
The RPCwBeam stream stores events triggered by the RPC in ﬁlled bunches. “Beam”
was meant for the coincidence with the BPTX, which was originally planned for thecollision data taking, but this trigger requirement was not used during the cosmic runs.
TGCwBeam
The TGCwBeam stream contains events triggered by the TGC in-time items. The coincidence with the BPTX was not required during the cosmic runs as the RPCwBeam.
MBTS BCM LUCID
Only the Minimum Bias Trigger Scintillator (MBTS) is used for the cosmic runs. Events
triggered by the MBTS is stored in the stream.

96

Table 6.2: Level-1 Calorimeter menus used in the cosmic run 90272 L1Calo stream
Trigger Menu
L1 EM3
L1 EM7
L1 EM10
L1 EM13
L1 EM13I
L1 EM18
L1 2EM3
L1 2EM7
L1 2EM10
L1 TAU5
L1 TAU6
L1 TAU9
L1 TAU9I
L1 TAU11I
L1 TAU16
L1 TAU16I
L1 TAU40
L1 2TAU6
L1 2TAU9I
L1 EM3 BPTXL1 TAU5 BPTX
L1 EM4
L1 EM7I

Prescale
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2000
1
1
1

# of Events
71832
10436
6116
4150
3887
2469
179
42
19
195921
149898
86862
86735
65378
38187
38067
9808
2000
856
38
195921
26602
10114

97

Trigger Menu
L1 2EM4
L1 2EM7I
L1 J5
L1 J10L1 J18L1 J23
L1 J35
L1 J42L1 J70
L1 2J5
L1 2J10
L1 2J18
L1 2TAU5
L1 2TAU9
L1 FJ18
L1 FJ3
L1 2FJ18
L1 2FJ3
L1 3J10L1 XE20
L1 JE120
L1 JE340

Prescale
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

# of Events
111
30
212983
80734
34414
23954
12884
9698
4238
2215
535
142
2945
868
3
2002
0
0
14
29321
0
0

RNDM
RNDM stream exploits the random trigger which ﬁres in accordance with the Central
Trigger Processor (CTP) clock. It was called RNDM0 stream during Milestone Weeks.
The CTP clock can be synchronized to the LHC clock, or otherwise to an internal sourcein ATLAS. The random trigger rate can be parameterized from 40 MHz or less. During
the cosmic data taking, the events were randomly triggered in about 1 Hz with a prescaleof about 78400.
CosmicMuons
Events triggered by the RPC and TGC Level-1 logic with looser requirements on the
coincidence than the conﬁgurations used for the collision data taking. No coincidence isrequired with the beam speciﬁc triggers such as BPTX.
CosmicDownwardMuons
This stream has events streamed with level-1 RPC or TGC RoI in the bottom sector ofthe detector.
IDCosmic
Inner Detector tracking is performed at the Level-2 trigger level on all the physics Level-1
triggered events. Only Level-1 physics triggered events with ID segments are recorded inthis stream. The implementation of inner detector HLT allowed for storing cosmic events
passing the inner detector with high statistics.
TRTCosmic
This stream exploits the Transition Radiation Tracker (TRT) Level-1 trigger [91] speciﬁcally developed for the cosmic data taking. The trigger was used at the end of November
2008 for the ﬁrst time. It makes use of the TRT high threshold (HT) hits. This trigger
operate only during the commissioning, and during the collision data taking, it will be
exploited for other purposes.
LateEvents
The LateEvents stream was recorded only for run 89507 among the Tier-1 reprocessed
data. This stream stores events which arrived at the SFO’s after the luminosity block isclosed.

6.4.2

Physics Streams during Single Beam Runs

There were 4 physics trigger streams that operated during the single beam data taking:
BPTX, L1Calo, MBTS BCM LUCID, and RNDM stream.

98

BPTX
Events triggered by beam pick-up items.
L1CaloThe same as in the cosmic runs.
MBTS BCM LUCID
Events triggered by the MBTS, BCM or LUCID. A huge fraction of the events are comingfrom the MBTS.
RNDM
The same conﬁguration as the cosmic RNDM stream.

6.5

Runs Used in the Analyses

Out of various physics streams mentioned in the previous section, analyses were mainly
concentrated on RNDM and L1Calo stream. The analyses with RNDM stream data
allow us to understand the electronic noise and problematic channels, and are mentioned
in the next chapter. All the 90 runs from Fall’08 data with the RNDM stream were usedin the analyses. Chapter 8 shows results of cosmic measurement with the calorimeters.
The L1Calo stream records cosmic events with large energy deposit in the calorimeters,
and allow us to validate the detector systems, and also to understand the rate and eﬀects
from the high energy cosmic muons.

99

Chapter 7
Calorimeter Measurement with
Random Stream Data
By measuring the energy distributions of calorimeter cells in dedicated pedestal runs
and random stream data, and calculating the Root-Mean-Square (RMS) of the energy
distribution, we can determine the electronic noise at the cell-level (and also at the PMT
channel-level for TileCal). The electronic noise will set the minimum detectable signal
and the limitation on the energy and time resolution.
Due to the large dynamic range and the adoption of multi-gain systems in thecalorimeters (i.e. each signal is split into three gain scales in the ratio of 1/10/100
for LAr, and two gain scales in the ratio of 1/64 for TileCal; see Section 5.2 and 5.3),
the electronic noise must be well understood and kept under control, especially in the
LAr EM calorimeter, to meet the high precision requirements from Higgs analyses using
electrons (H → ZZ → 4e) and photons (H → γγ). The noise-level in the calorimeters
is considered and treated during the reconstruction of calorimeter objects (e.g. clusters,
jets, and ETmiss ), thus it must be correctly measured and stored in the ATLAS database.
Any mis-consideration of the noise-level in the database can deteriorate the noise suppression and performance of the calorimeter objects; especially the energy resolution.
The noise measurement has previously been performed with the testbeam data in
2000-2003 [93]-[95], but here, the ﬁrst in-situ noise measurement of calorimeter cells
using the random stream data is shown [A.3.4.15] [96] ([A.3.4.15] indicates the 15th
presentation mentioned in Appendix A.3.4).
On top of the noise measurements, eﬀects of problematic channels in the calorimeters
were investigated. A phenomenological approach to search the bad channels were proposed for the TileCal. It is of vast important to identify the bad channels, understandtheir eﬀects on the performance, and to develop strategies to cope with the eﬀects.
At the ﬁnal sections, the performance of calorimeter clusters and ETmiss is validatedwith the data, and proposals on the updates of cluster algorithm to take care of theactual noise feature and the improvements observed after adopting the new algorithm
are shown.
Investigations of random-stream data, and validation of oﬄine reconstruction withthe data will be an important ﬁrst step towards the physics analyses with the collision100

data to come.

7.1

Noise in the Calorimeters

For the energy reconstruction in the ATLAS calorimeters, several kinds of noise arise.
Electronic noise is due to the ﬂuctuation of the baseline. Basically, two mechanisms contribute to the electronic noise; velocity and number ﬂuctuations of electric
currents [102]. The former eﬀect originates from the thermal motion. Each electron has
a random velocity ﬂuctuation around the average drift velocity due to thermal excitation.
The number ﬂuctuations can occur due to a potential barrier for the emission of carriers,
or by the trapping of carriers during the current ﬂow. Due to the large dynamic range(and thus multiple gains used in the energy reconstruction) of the ATLAS calorimeters(from a few tens of MeV to about 3 TeV), the electronic noise should be kept as low as
possible.
Other than the noise originating from such detector characteristics, there is anothercontribution which must be considered during the high luminosity operations. It is thepile-up noise coming from the overlaps of soft proton-proton collision products (Minimum Bias events) during such high-luminosity data takings. At the design luminosity of1034 cm−2 s−1 and the center-of-mass energy of 14 TeV, an average number of 23 Minimum
Bias events are expected in a single bunch crossing (every 25 ns).
To suppress electronic noise, longer shaping time of signals are preferred, but on theother hand, shorter shaping time is preferred to decrease the contamination of pile-ups(Figure 7.1). The time constant is chosen to compromise the eﬀects and minimize theoverall noise (e.g. 15 ns for the LAr calorimeters and 50 ns for the TileCal). The expected
electronic noise and total noise during the design luminosity data taking are shown in
Figure 7.2.
With the current commissioning data, only the electronic noise can be measured,
since there were no collisions (as of September 2009; the ﬁrst collision took place on
November 23, 2009). Minimum Bias events in the early collision running will provide
useful information to estimate the pile-up noise during the collision data-taking.

7.1.1

Electronic Noise in TileCal

The Tile Calorimeter (TileCal) is instrumented with approximately 10,000 PMT’s (channels), with two PMT’s reading out each cell, giving about 5000 cells. The TileCal is
divided into a Long-Barrel (LB) cylinder and two Extended-Barrel (EB) cylinders as
mentioned in Chapter 4, and each cylinder consists of 64 modules in φ, with three longitudinal sampling depths (A, BC, D). The long barrel is divided into two logical partitions(LBA, LBC), and the front-end electronics in each module are referred to as “drawers”.
Figure 7.3 shows the geometry and deﬁnitions of cells and PMT’s in a drawer from eachpartition. In order to cover a large dynamic range (∼ 20 MeV to ∼ 1.3 TeV in a singlecell), bi-gain system is adopted. Those gains are called the low and high gain, with therelative ampliﬁcation diﬀerence of a factor 64. The transition from the high to low gainis around 20 GeV in a PMT.
101

Figure 7.1: Contribution of electronic, pile-up and total noise to an electromagnetic
shower at η = 0 at low and high luminosity [51].

10cm-2 s-1 (MeV)

3
PS
EM1

10

EM2
EM3
Tile1

34

Tile2
Tile3

2
FCal1
FCal2
FCal3

PS

4

EM1
EM2
EM3
Tile1

3

Tile2
Tile3

FCal1

102

HEC1
HEC2
HEC3
HEC4

0 0.5

10

Total noise at 1× 10

Electronic noise at zero luminosity (MeV)10

1 1.5 2 2.5 3 3.5 4 4.5 5
|η|

FCal2
FCal3
HEC1
HEC2
HEC3
HEC4

0 0.5

1 1.5 2 2.5 3 3.5 4 4.5 5|η|Figure 7.2: Electronic noise of the calorimeters at the cell-level (left) and total noisewith pile-up eﬀects at the design luminosity of 1034 cm−2 s−1 (right) as a function ofpseudorapidity (η) [92].

102

EBA

EBC

Extended-BarrelLong-Barrel

Cell NamePMT UPPMT DOWN

Figure 7.3: Deﬁnition of cells in the Tile calorimeter. The rectangles indicate the cells,
cell names are shown inside the region (e.g. A-1), and two numbers are the PMT numbers(e.g. 2, 3).

103

The Milestone Weeks were the transition era of the data format from the TileCalspeciﬁc ntuples to the ones from the central reconstruction software (“RecExCommission”
package, afterward merged with “RecExCommon” package in the Athena framework).
Measurement of the random stream data were preceded by the validation of the reconstruction software and the ATLAS database [A.3.4.18]. The plots shown here are fromthe condition after the update was made.
The electronic noise in TileCal consists of contributions from the electronics and thepower supplies. The intensity of dark current in the PMT’s is low (lower than 100 pA at
the nominal gain [61]) and has negligible eﬀects in the energy reconstruction. The main
contributions from the electronics are from the front-end electronics. The noise from thelow voltage power supplies also has perceptible eﬀects, and will be mentioned again in
the following text.
Figure 7.4 shows the electronic noise in the TileCal Long-Barrel (0 < |η| < 1) at thecell-level as a function of pseudorapidity (η) and sampling depth. This was the ﬁrst insitu measurement of the electronic noise of the TileCal[A.3.4.15]-[A.3.4.28]. The left plot
indicates the noise levels used in the current Monte Carlo simulation, while the centerand right plots give the noise values measured from a cosmic run (run 29576 during theM5 Week in the fall of 2007), averaged over all the TileCal modules in φ. The ExtendedBarrels (0.8 < |η| < 1.7) were not operating during the M5 Week. In the center plot,noise values are derived from the width of a Gaussian, ﬁtted iteratively over a 2 σ range.
The right plot shows the noise values deﬁned as the Root Mean Square (RMS) of the cell
energy distributions. Two features were observed, which were not expected and treatedin the Monte Carlo simulation. One is the η-dependence of the electronic noise. The
other feature is the non-Gaussian structure of the noise, which obviously shows up in theenergy distributions of cells especially in larger pseudorapidity regions (Figure 7.5) andalso in the diﬀerence between the σ from a Gaussian ﬁt and the Root-Mean-Square (RMS)of the cell energy distribution. These features have continuously been observed in-situ
since the M5 Week, throughout the following Milestone Weeks and Fall 2008 cosmic data.
Although these eﬀects are at the level of only 10 - 30 MeV per cell compared to a ﬂatdistribution, and are not problematic as long as the correct noise values are stored in the
ATLAS database, we further investigated to nail down the cause of such phenomenon.
We will also mention the current status of the TileCal detector.
The main origin of the non-Gaussian tail can be ascribed to the low voltage power
supply (Figure 7.6). The low voltage power supply is contained in a small closed volume
known as a “ﬁnger”. A signiﬁcant increase of non-Gaussian tails in the pedestals wereobserved in January 2006 [98] when the low voltage power supply was changed fromcommercial power supply to the ﬁnger power supply (Figure 7.7). The migration of the
low voltage power supply was mandate in order to operate the TileCal at Point 1, wherevery limited space is available.
In order to suppress common mode noise coming from the power supply, chokes were
implemented on the DC/DC converter outputs. Sizable chokes will mostly eliminate suchcommon mode, but only limited space is available in the ﬁnger power supply. In order tocope with the space limitation, ferrite chokes were adopted and used. The non-Gaussian104

Tile A
Tile BC
Tile D

60
50
40

70
Tile A
Tile BC
Tile D60
50
40Electronics Noise [MeV]Electronics Noise [MeV]Electronics Noise [MeV]

70

70
60
50
40

30

30

30

20

20

20

-1 -0.8 -0.6-0.4 -0.2 0 0.2 0.4 0.6 0.8 1
η

-1 -0.8 -0.6-0.4 -0.2 0 0.2 0.4 0.6 0.8 1
ηTile A
Tile B/C
Tile D

-1 -0.8 -0.6-0.4 -0.2 0 0.2 0.4 0.6 0.8 1
ηFigure 7.4: Electronic noise in the TileCal Barrel at the cell-level as a function of pseudorapidity (η) and sampling depth (A, BC, D). The left plot gives the noise levels in
Monte Carlo simulation, while the center and right plots give the noise values measured
from a cosmic ray run in 2008 (M5 run 29576) from a Gaussian ﬁt (center) and the RMS(right).

TileCellE_LBA_A_Module3_Tower1

Number of Events

4
3
10
10
18000

16000

LBA Module 3η =0.15Mean

0.1117

RMS

29.8

TileCellE_LBA_A_Module13_Tower3

4
3
10
10
18000

16000

3
14000
10

Entries 55245

20000

Number of Events

Entries 55245

20000

LBA Module 13η =0.35

10000
102

10000
102

8000
10
6000
10
4000

8000
10
6000
10
4000

2000
1
1
−200
−400

2000
1
1
−200
−400

−300
−150

−100
−200

−100
−50

0

100
50

200
100
300
150
400
200
Cell Energy [MeV]

−300
−150

−100
−200

−100
−50

0

100
50

TileCellE_LBA_BC_Module12_Tower7

LBA Module 12η =0.75

Mean

0.9588RMS

38.78

TileCellE_LBA_A_Module63_Tower9

4
3
10
10
18000

16000

3
14000
10

200
100
300
150
400
200
Cell Energy [MeV]

Entries 55245

20000

Number of Events

Entries 55245

20000

Number of Events

31.12

102
12000

10
12000

16000

0.1331

RMS

3
14000
10

2

4
3
10
10
18000

Mean

LBA Module 63η =0.95

Mean

1.407

RMS

62.32

3
14000
10

2

102
12000

10
12000
10000
102

10000
102

8000
10
6000
10
4000

8000
10
6000
10
4000

2000
1
1
−200
−400

2000
1
1
−200
−400

−300
−150

−100
−200

−100
−50

0

100
50

200
100
300
150
400
200
Cell Energy [MeV]

−300
−150

−100
−200

−100
−50

0

100
50

200
100
300
150
400
200
Cell Energy [MeV]

Figure 7.5: Energy distribution of TileCal cells. Non-Gaussian tails are obvious from thecell distribution in high η.

105

Figure 7.6: Tile Calorimeter power distributions. The left ﬁgure shows the location of
the ﬁnger low voltage power supply (the red box indicates a single TileCal module andthe light blue box indicates the ﬁnger), and the right ﬁgure shows the overall scheme ofthe power distribution [97].

Figure 7.7: Non Gaussian distribution of TileCal pedestal after migrating into ﬁnger
low voltage power supply (top) and before the migration using commercial power supply(bottom) [97].

106tails were fairly reduced [97], but the still-remaining feature was what we observed during
the in-situ measurement.
Recently, large component of the non-Gaussian tails were found to be related to thenoise correlation among channels in the same drawer [104][105]. Figure 7.8 shows thecorrelation coeﬃcients of PMT noise in LBA Drawer 16. The correlation coeﬃcients of
two PMT’s ci,j (i, j: PMT numbers) were calculated from the digitized outputs (sevensamplings; digitized every 25 ns) of the PMT’s (Ai , Aj ) bycov(Ai, Aj )σAi σAj
cov(Ai , Aj ),= 
(Ai − Ai )2(Aj − Aj )2

ci,j =(7.1)

where cov(Ai , Aj ) is the covariance of the two variables (Ai , Aj ),cov(Ai , Aj ) = Ai Aj − Ai Aj ,

(7.2)

and
Ai Aj
Ai

=

=

 

Nsample Nevent

1
Nsample Nevent

s=1

(s,n)

Aj

(s,n)

.

Ai

(s,n)

,

(7.3)

n=1

 

Nsample Nevent

1
Nsample Nevent

s=1

Ai

(7.4)

n=1

where Nsample is the number of samplings (=7 for TileCal), Nevent is the number of events(s,n)
in the data, and Ai
indicates the output of PMT i in the s-th sampling and n-th event.
Local structures were observed in the correlation, which fairly match with the motherboard and digitizer conﬁguration; 12 (6) out of 48 channels in a drawer share the same
motherboard (digitizer). The amount of coherence and the feature of local structuresslightly vary from drawer to drawer, but basically has the same tendency.
The non-Gaussian tails of the channel energy distribution can be reduced with oﬄine
energy correction using linear functions to ravel and reduce the correlation eﬀects [104] orcommon-mode suppression [105]. However, the non-negligible amount of non-Gaussian
tails remain, and can aﬀect the performance of the calorimeter objects (clusters, jets
and ETmiss ). We will further look into this issue and propose a strategy to cope it in
Section 7.3.3 and 7.4.
The correlation of electronic noise among the diﬀerent drawers were shown in Figure 7.9. The correlation coeﬃcients are calculated as the same way as equation (7.1),
except that i and j are now drawer numbers (1∼64). The correlation was calculated
between the PMT’s with the same number (the same η) in two drawers. The correlation of noise between the drawers was very small and almost negligible. However, smallinter-drawer correlations were observed for some PMT’s (especially in the low gain).
The overall performance from RNDM stream data taken in the fall of 2008 is shownin Figure 7.10 and 7.11.
107

0.6

30

0.8

40
35

0.6

30

0.4

25

1
45

0.4

25

20

0.2

20

0.2

15

0

15

0

10

10

−0.2

5
5

10

15

20

25

30

35

40

45
PMT

−0.2

5

−0.4

Correlation

35

PMT

0.8

40

Correlation

PMT

1
45

5

10

15

20

25

30

35

40

45
PMT

−0.4

40

0.6

1
60
0.8
50

0.6

0.4

40

0.4

30

0.2

30

0.2

20

0

20

0

−0.2

10

−0.2

10

−0.4
10

20

30

40

50

60
Drawer

−0.4
10

20

30

40

50

60
DrawerFigure 7.9: Correlation of electronic noise among the drawers in TileCal PMT1 in LBA
for the high gain (left) and low gain (right).

The stability of the electronic noise throughout the commissioning runs in the Fall2008 was also investigated (Figure 7.12 and 7.13). Figure 7.12 shows the mean energy(black circle) and the mean of the Gaussian ﬁtted iteratively between ±2σ range (black
inverted triangle) of the TileCal cell energy distribution for each partition. The discrepancy of the two “means” is due to the asymmetric non-Gaussian tails of the cell energy
distribution, and thus the mean of the Gaussian better represent the pedestal values.
The pedestal shifts were below 0.2 MeV (about 0.01 ADC counts), and its stability wasvery good over a few months of cosmic data taking. Figure 7.13 shows the stability ofelectronic noise of cells for each partition. The stability of the electronic noise was basically within 1 %, which assures the use of ﬁxed noise database throughout some period oftime. Of course, the electronic noise level is monitored throughout the whole data taking,and any variation in the performance will be identiﬁed and considered in the oﬄine data
analyses.

108

Correlation

0.8
50

Drawer

1
60

Correlation

DrawerFigure 7.8: Correlation of electronic noise in TileCal LBA Drawer 16 for the high gain
(left) and low gain (right).

Electronics Noise of TileCal Cells [MeV]

70
60
50
40
30
20

Tile Sample A
Tile Sample BC
Tile Sample D

ATLAS
10
−1.5

−1

−0.5

0
η

0.5

1

1.5

LBA

60
50
40
30
20

Tile Sample A
Tile Sample BC
Tile Sample D

ATLAS
10
−3

Electronics Noise of TileCal Cells [MeV]Electronics Noise of TileCal Cells [MeV]

70

−2

−1

0
φ

1

2

70
EBA

60
50
40
30
20

Tile Sample A
Tile Sample BC
Tile Sample D

ATLAS
10
−3

−2

−1

0
φ

1

2

70

3

LBC

60
50
40
30
20

Tile Sample A
Tile Sample BC
Tile Sample D

ATLAS
10

3

−3

Electronics Noise of TileCal Cells [MeV]Electronics Noise of TileCal Cells [MeV]

Figure 7.10: Electronic noise of the TileCal cells from Fall 2008 run 90272 RNDM stream
data.

−2

−1

0
φ

1

2

3

70
EBC

60
50
40
30
20

Tile Sample A
Tile Sample BC
Tile Sample D

ATLAS
10
−3

−2

−1

0
φ

1

2

3

Figure 7.11: Electronic noise of the TileCal cells in each partition from Fall 2008 run
90272 RNDM stream data.

109

Mean Energy [MeV]

0.8
0.7

LBA

Tile A

Tile BC

Tile D

Mean

Mean of Gaussian Fit

0.6
0.5
0.4
0.3
0.2
0.1
0

Mean Energy [MeV]

88000

88500

89000

89500

90000

90500

91000

91500

92000
Run Number

1
LBC

Tile A

Tile BC

Tile D

Mean

Mean of Gaussian Fit

0.8
0.6
0.4
0.2
0

Mean Energy [MeV]

88000

88500

89000

89500

90000

90500

91000

91500

92000
Run Number

0.6
0.5

EBA

Tile A

Tile BC

Tile D

Mean

Mean of Gaussian Fit

0.4
0.3
0.2
0.1
0
−0.1

Mean Energy [MeV]

−0.2

88000

88500

89000

89500

90000

90500

91000

91500

92000
Run Number

0.6
0.5

EBC

Tile A

Tile BC

Tile D

Mean

Mean of Gaussian Fit

0.4
0.3
0.2
0.1
0
−0.1
−0.2

88000

88500

89000

89500

90000

90500

91000

91500

92000
Run Number

Figure 7.12: Stability of the pedestals of the TileCal in Fall 2008 runs. The pedestal
stability is shown for each sampling layer in each partition. Open circular points indicate
the mean of the cell energy distributions, and the solid circular points indicate the meanfrom the Gaussian ﬁts.

110

TileCal Electronics Noise [MeV]

40
39

LBA

Tile A

Tile BC

Tile D

38
37
36
35
34
33
32
31

TileCal Electronics Noise [MeV]

30

88000

88500

89000

89500

90000

90500

91000

91500

92000
Run Number

42
41

LBC

Tile A

Tile BC

Tile D

40
39
38
37
36
35
34

TileCal Electronics Noise [MeV]

33

88000

88500

89000

89500

90000

90500

91000

91500

92000
Run Number

41.5
41

EBA

Tile A

Tile BC

Tile D

40.5
40
39.5
39
38.5
38
37.5

TileCal Electronics Noise [MeV]

37

88000

88500

89000

89500

90000

90500

91000

91500

92000
Run Number

45
EBC

Tile A

Tile BC

Tile D

44.5
44
43.5
43
42.5
42

88000

88500

89000

89500

90000

90500

91000

91500

92000
Run Number

Figure 7.13: Stability of the Electronic noise of the TileCal in Fall 2008 runs. The noise
stability is shown for each sampling layer in each partition. Lines indicate ±1% from themean values.

111

1000
103

Electronic Noise [MeV]Electronic Noise [MeV]

50

EMB 1
EMB 2
EMB 3

45
40
35
30
25
20

900
800
700
102
600
500

15

400
10
300

10

200

5

100

0
-1.5

-1

-0.5

0

0.5

1

1
-4

1.5
η

HEC 0
HEC 1
HEC 2
HEC 3

EMEC PSEMEC 1EMEC 2EMEC 3

-3

-2

-1

0

1

2

3

4
η

3

Cell volume (mm )Figure 7.14: Electronic noise of the LAr Calorimeter at the cell-level as a function of η
for the LAr barrel (left) and LAr end-cap (right), measured in-situ from M5 run 29576.
109
8

10

7

10

PS

FCal1

EM1

FCal2

EM2

FCal3

EM3

HEC1

Tile1

HEC2

Tile2

HEC3

Tile3

HEC4

6

10

105

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5
|η|

Figure 7.15: Electronic noise in Fall 2008 [106] (left) and volume of calorimeter cells in
LAr as a function of η. Volume of calorimeter cells as a function of η [92](right).

7.1.2

Electronic Noise in LAr

The LAr EM calorimeter has the most stringent requirement on the electronic noise,since the energy resolution of electrons and photons directly aﬀect the reconstruction of
the invariant mass of Higgs bosons decaying into electrons (H → ZZ → 4e) or photons(H → γγ). The coherent noise should also be suppressed as much as possible, especially
due to the fact that signiﬁcant number of channels exist in the LAr EM calorimeter(about 180,000 channels). Even a small amount of coherence can severely deteriorate theperformance of ETmiss , thus it is required to be less than 3 MeV per channel [58]. This
also sets a limit to the tolerable level of pedestal shifts per channel.
Electronic noise in the Liquid Argon calorimeter (LAr) was measured in-situ for theﬁrst time with M5 run 29576 [A.3.4.19]. Figure 7.14 gives the values of the electronicnoise in LAr at the cell level. The electronic noise in LAr is roughly proportional tothe cell volume as previously mentioned, as shown in Figure 7.14 and 7.15 [112]. The
electronic noise simulated in Monte Carlo for LAr (Figure 7.15) [111] is basically found112

Mean Energy [MeV]

0.8
EMB

PSB

EMB 1

EMB 2

EMB 3

91500

92000
Run Number

EMEC 2EMEC 3

91500

92000
Run Number

HEC 3

HEC 4

91500

92000
Run Number

0.6
0.4
0.2
0
−0.2

Mean Energy [MeV]

88000

88500

89000

89500

90000

90500

91000

2
EMEC

PSEC

EMEC 1

1.5

1

0.5

0

Mean Energy [MeV]

88000

6

88500

89000

89500

90000

90500

HEC

91000

HEC 1

HEC 2

4
2
0
−2
−4

Mean Energy [MeV]

88000

6

88500

89000

89500

90000

90500

FCAL

91000

FCAL 1

FCAL 2

FCAL 3

4
2
0
−2
−4
−6
88000

88500

89000

89500

90000

90500

91000

91500

92000
Run Number

Figure 7.16: Stability of the pedestals of the Liquid Argon Calorimeter in Fall 2008 runs
is shown for each sampling layer in each region.

113

Electronic Noise [MeV]

45
EMB

PSB

EMB 1

EMB 2

EMB 3

91500

92000
Run Number

EMEC 2EMEC 3

91500

92000
Run Number

HEC 3

HEC 4

91500

92000
Run Number

40
35
30
25
20
15
10

Electronic Noise [MeV]

88000

88500

89000

89500

90000

90500

91000

140
EMEC

PSEC

EMEC 1

120
100
80
60
40
20

Electronic Noise [MeV]

88000

88500

89000

89500

90000

90500

91000

450
HEC

HEC 1

HEC 2

400
350
300
250
200

Electronic Noise [MeV]

88000

400

88500

89000

89500

90000

90500

FCAL

91000

FCAL 1

FCAL 2

FCAL 3

350
300
250
200
150

88000

88500

89000

89500

90000

90500

91000

91500

92000
Run Number

Figure 7.17: Stability of the electronic noise of the Liquid Argon Calorimeter in Fall 2008
runs is shown for each sampling layer in each region. Lines indicate ±1% from the meanvalues.

114

to be consistent with that measured in cosmic ray runs.
There have been extensive investigations and measurements on the performance of
the LAr calorimeters up to now, fully exploiting the commissioning data. Here, we will
brieﬂy mention the current status of the detector.
The adoption of the Liquid Argon technique requires the calorimeters to be housedin cryostats as previously mentioned, and temperature variations directly inﬂuences thereadout signals through the eﬀects on the drift velocity of ionization electrons and also
on the eﬀects of the liquid density. In order to keep the constant term of the energy
resolution below 0.2%, the uniformity of the temperature is required to be within 100mK [107]. The measurement of the temperature stability was performed for ten daysaround the winter break in 2008, when the detector installation activities in the ATLAS
cavern were not ongoing. The temperature dispersion was 1.6 mK in average, and 5 mK
in maximum [106], and was well below the physics requirement. The purity of the Liquid
Argon was also stable and well below the requirement (less than 300 ppb from July 2007
to July 2009; better puriﬁcation is need when larger than 1000 ppb).
The stability of the pedestals and electronic noise in Fall 2008 cosmic runs is shown in
Figure 7.16 and 7.17. Shifts in the pedestals can make similar eﬀects as the coherent noise,and should be kept as small as possible (shifts by 3 MeV per channel in the EM calorimetercan be problematic, due to the presence of signiﬁcant number of channels). The pedestals
in the EM calorimeter were stable below 1 MeV. The presamplers had problems withcoherent noise during the 2008 data taking, but were ﬁxed in 2009. The stability of theelectronic noise was stable within ±1 %, and showed good enough performance to meetthe requirements from the physics analyses.

7.2

Problematic Channels in the ATLAS Calorimeters

Searches for problematic channels in the ATLAS calorimeters have been undergoing sincelong time using calorimeter speciﬁc calibration run and combined commissioning run
data. So called the “dead” (not responding) and “hot” (noisy) channels must be identiﬁed
to perform any meaningful measurements using the calorimeters. The “dead” channels
form “holes” in the detectors, and underestimate the energy when particles penetrate
through the regions. They can also lead to large fake ETmiss when a bunch of high energy
particles like jets accidentally pass the dead region. On the other hand, “hot” channels
can obviously initiate fake high energy signals in the calorimeters, which will also disturb
the triggers on top of energy measurements. Thus, the “hot” channels must be masked(their energy is forcibly set to zero or another ﬁxed value as a ﬂag). Figure 7.18 showsthe η-φ map of masked problematic channels in LAr and TileCal. The numbers andpercentages of masked cells in each partition is shown in Table 7.1.
The impact of hot channels on calorimeter clusters is shown in Figure 7.19 (the details
on the cluster algorithm will be mentioned in the following section). A signiﬁcant numberof clusters were reconstructed, since hot channels can easily pass the energy thresholddeﬁned by the clustering algorithm. Their eﬀects are too obvious in the energy distribu-

115

φ

φ

3

3

2

2

1

1

0

0

−1

−1

Tile Masked

LAr Masked
−2
−3
−5

−2

−4

−3

−2

−1

0

1

2

3

4

5
η

−3
−5

−4

−3

−2

−1

0

1

2

3

4

5
η

Figure 7.18: η-φ map of LAr (left) and TileCal (right) masked problematic channels.
Table 7.1: Numbers and percentages of masked problematic cells for each partition during
2008 Fall cosmic data taking period.

All cells
MaskedEM
Tile
1639685248
325 (0.2 %) 66 (1.3%)

HEC
5632
12 (0.19 %)

FCAL
3524
10 (0.3 %)tions as well. In the calorimeter reconstruction, so called the “masking” procedure can be
performed on the identiﬁed problematic channels. For problematic LAr channels, the cell
energy is overwritten as zero when the masking is turned on. For TileCal channels, themasking can take two diﬀerent correction procedures in regards to the situation. When
only one of the two PMT’s in a cell is identiﬁed as “bad”, only the remaining “good”
PMT will be used for the energy reconstruction, namely its channel energy is multipliedby two to calculate the cell energy. If both PMT’s in a cell are problematic, then the cell
energy is set to 1 MeV. This masking procedure will remove disastrous contributions fromnoisy channels, but further procedures are needed to recover the energy resolution degraded by the “holes” in the calorimeters. One of the straightforward ways to recover theenergy lost in the dead (or masked) channels are to estimate the energy of the channels
by taking the mean energy density from the neighboring cells [108]. Another method was
proposed to make use of the Inner Detector tracks pointing to the dead channels [109].
The problematic channels were identiﬁed, categorized for their problematic issues,and stored in the ATLAS database. The list has been checked and updated throughout
the data taking.
For the LAr channels, the bad channels are grouped into 15 categories (Tab. 7.2)[110]in regards to their problematic phenomena. They are also broadly grouped in three.
The “deadPhys” and “deadReadout” channels are called the “Dead” cells. The “highNoiseHG(MG,LG),” “unstabelNoiseHG(MG,LG),” “sporadicBurstNoise” channels aregrouped as “Noisy” cells. The remaining bad channels are called the “Aﬀected” cells.
In TileCal, the problematic channels are categorized as shown in Table 7.3.

116

5000

# of events# of events

40000

4500
3
10
4000

35000
104
30000

Before Masking
After Masking

3500
3000
102
2500

20000
102
15000

2000
10
1500

10
10000

1000

5000
1

500
1
0

10

20

30

40
50
60
70
Number of EM Topoclusters-10000

Before Masking
After Masking

05000
10000 15000 20000
EM Topocluster Energy [MeV]Before Masking
After Masking

16000
3

10
14000

35000
3
10
30000

12000

25000
102
20000

10000
102

15000
10
10000

6000
10
4000

5000
1

2000
1

8000

−4

−3

−2

−1

0

Normalized number of clusters

−5

-5000

20000
104
18000

# of clusters# of clusters

50000
45000
104
40000

Before Masking
After Masking

3

10
25000

1

2
3
4
5
EM Topocluster η

−3

−2

−1

0

1

2
3
EM Topocluster φ

−1
0.1
10

0.09

Before Masking
After Masking

0.08
0.07
10−2
0.06
0.05
−3
0.04
10
0.03

0.02
−4
0.01
10

0

20

40

60

80

100 120 140 160 180 200# of cells in EM Topocluster

Figure 7.19: The distributions of EMTopoclusters in M5 run 29576 NIM0 stream. The
reconstructed number of clusters (top left), the cluster energy (top right), pseudorapidityof the clusters (middle left), the φ of clusters (middle right), and the number of cells
inside the clusters (bottom) are shown.

117

Table 7.2: Categories of the problematic LAr channels
Category
Dead
Dead
Noisy

Flag Name
deadReadout
deadPhys
highNoiseHG(MG,LG)

Noisy
Noisy

unstabelNoiseHG(MG,LG)
sporadicBurstNoiseAﬀecteddeadCalibAﬀectedAﬀecteddistorted
lowNoiseHG(MG,LG)

AﬀectedAﬀectedshort
peculiarCalibrationLine

AﬀectedmissingFEB

Deﬁnitiondead channels at the readout level
dead channels at the detector levelsigniﬁcantly noisy channels in high gain
(medium, low gain)
they should be considered for masking
channels with instability respect to timechannels with sporadic bursts of noise
observed during the cosmic runs
channels with signiﬁcantly distorted
calibration pulse
wave distortions of various types
noisy channels in high gain (medium, low)
more than about 5 standard deviations
from the expected noise
not so noisy as to mask themchannels with shorts
channels with calibration problems(signals leaking in other channels)
only in the low gain
FEB was missing in the readout
for the channels

118

Table 7.3: Categories of the problematic TileCal channels
Level
ADC
ADC

Flag Name
GeneralMaskAdc
ADCDead

ADC

StuckBit

ADC

SevereStuckBit

ADC

DataCorruption

ADC
ADC
ADC
ADC
ADC
ADC
ADC
ADC
ADC
ChannelChannelChannelChannelChannelChannelChannelSevereDataCorruption
VeryLargeHfNoise
NoDataWrongDspConﬁg
LargeHfNoise
CorrelatedNoise
LargeLfNoise
NoCis
BadCis
Unspeciﬁed
NoPmt
NoHV
WrongHV
NoLaser
BadLaser
NoCesium

ChannelChannelChannel
Trigger
Trigger

BadCesium
NoTiming
BadTiming
TriggerGeneralMask
TriggerNoGain

Trigger

TriggerHalfGain

Trigger

TriggerNoisy

DSP

IgnoreInDsp

DSP

IgnoredInHlt

Deﬁnition
Unspeciﬁed ADC problems
ADC is deadA bit is stuck (ﬁxed at 1 or 0), in the ADC output,
but no signiﬁcant eﬀect on the performance.
A bit is stuck (ﬁxed at 1 or 0), in the ADC output,
manifesting signiﬁcant eﬀects on the performance.
Digital data is corrupted(CRC, BCID, DMU memory parity errors, etc.)
Digital data is corrupted(CRC, BCID, DMU memory parity errors, etc.)
The channels have to be masked.
The RMS of the ADC 7 sample distribution is very large
No data comes out of the ADC
The online DSP conﬁguration was not correct.
The RMS of the ADC 7 sample distribution is very large
Correlation is observed among the ADC noise
The RMS of the ﬁrst sample distribution is large
Problems identiﬁed in the long-term CIS calibration runs
Channels with 5.2 pF capacitor problems.
Unspeciﬁed channel problems
The PMT is not read out or dead
HV not applied to the PMT
A wrong HV value applied to the PMT
The PMT not receiving laser light
Laser light is too low or bright to perform good calibration
The PMT is not responding to the cesium scan
The response of the PMT to the cesium scan is
too low or highNot possible to set the dskew timing delay for the PMT
The PMT not well timed-in with the physics trigger
Unspeciﬁed trigger problems
The PMT of the 3-in-1 card is deadOne of the two diﬀerential signals is broken
so the channel is at 50% gain
The channel is too noisy, and must be disabledat the hardware-level
Data from the channels are not to be processed by the DSP
when masked, but this ﬂag does not save computing timeand so not used
Channels to be masked for the HLT decision
to avoid biasing events119In the next section, the strategy to identify the problematic TileCal channels is proposed and investigated.

7.2.1

TileCal Bad Channel Search

In order to identify problematic channels in TileCal, searches were performed for theindividual PMT’s with noise levels exceeding the average distributions measured in theprevious section. The criteria were developed to identify possible problematic channels [A.3.4.20], which are listed below.
• Noise values signiﬁcantly deviating from the average• Mean energy of a PMT signiﬁcantly diﬀerent from zero
• Large energy diﬀerence between PMT’s in the same cell
• Large energy diﬀerence ratio of PMT’s. This ratio is deﬁned as the diﬀerence ofthe energies of 2 PMT’s in a cell divided by the total cell energy. This quantity
provides additional information when studying high energy deposits.
• Time diﬀerence of PMT’s in the same cell (not considered here, but provides additional information, see Reference [122])• Shape of the energy distribution signiﬁcantly deviating from a Gaussian distribution
Examples are shown in Figure 7.20, 7.21, 7.22, 7.23 from M7 Week cosmic ray run
69373.
Figure 7.20 gives the measured noise of PMT 6 for all 64 Long-Barrel A (LBA)
drawers. The correspondence of PMT numbers and the cells are shown in Figure 7.3.
The PMT’s with the same ID number are compared, because the electronic noise has
η-dependence but basically uniform in φ as mentioned before (PMT number corresponds
to η, and a drawer is a set of PMT’s with the same φ values). Two channels are obviously
noisier than average, and one exhibits a below-average noise. From the left plot, we cansee the mean noise value and the spread of values for PMT 6 averaged over the LBA
partition. By ﬁtting this distribution with a Gaussian, we can deﬁne criteria to identify
bad channels (e.g. channels deviating from the mean value by 5 σ can be tracked in a
potential bad channel list). The right plot shows the PMT noise values versus the drawer
number, and this plot makes it easier to spot the problematic channels visually.
Figure 7.21 shows the mean energy of PMT 29 in the extended barrel A (EBA).
The left plot shows the energy distributions of two PMT’s, one of which is obviously
problematic. Again, by plotting the mean energy per drawer number one can easily spot
bad channels visually.
Figure 7.22 shows the energy diﬀerence of two PMT’s in the same cell (PMT 3 and 4
in LBA) compared to those over all the drawers. The left plot shows two examples of the
PMT energy diﬀerence, in which one has an unusual distribution with two peaks. The
right plot provides a better visualization by correlating the PMT energy diﬀerence with120

Figure 7.20: Noise values of LBA PMT 6 for each drawer in M7 run 69373 from theRNDM0 stream. The left plot shows the electronic noise distribution of PMT 6 for eachdrawer, while the right plot gives the electronic noise for PMT 6 versus drawer number.

Figure 7.21: Mean energy of EBA PMT 29 in M7 run 69373 from the RNDM0 stream.
The left plot gives examples of energy distributions for PMT 29 from 2 drawers, while
the right plot shows the mean energy for PMT 29 versus drawer number.

121

Figure 7.22: Energy diﬀerence of PMT’s in the same cell (PMT 3 & 4 in LBA) in M7
run from the 69373 RNDM0 stream. The left plot gives examples of the energy diﬀerence
between PMT 3 and 4, while the right plot shows the energy diﬀerence of PMT 3 and 4
versus drawer number

Figure 7.23: Energy distribution of PMT’s. The upper right and lower right plots are for
known bad channels.

122

drawer number. From this plot, we can only spot the “pairs” of problematic channels, but
we need additional information (such as that mentioned above) to identify the individual
bad channels.
Finally, Figure 7.23 shows the energy distribution for each PMT. The upper and lower
left plots are good channels, (the lower left plot has large non-Gaussian tails, but someof the channels in |η| ∼ 1 have this feature, which results in the increase in noise values
in Figure 7.4). The upper right and lower right plots are for known bad channels.
By adopting the criteria mentioned above, we can identify potential bad channels.
However, these criteria are not sensitive to coherent noise, or to channels which become
noisy for a short period of time (noise bursts). For those cases, we need to develop acomplementary strategy, such as using the ﬁt quality of the signal pulse shape.

7.3

Performance of Calorimeter Clusters

There are two major calorimeter clustering algorithms in ATLAS [111]. They are referred
to as a sliding window and a topological clustering (Topocluster) algorithm. Both typesof clusters are standard inputs for jet reconstruction.
The sliding window algorithm exploits ﬁxed size clusters, and shows robust performance. The topological clustering algorithm has better noise suppression, but relies
heavily on the ATLAS noise database in order to consider the signal-to-noise ratio of
each cell energy output. We will brieﬂy review the methods of the both algorithms
below.

7.3.1

Sliding Window Algorithm

There are two kinds of sliding window clusters used in ATLAS; electromagnetic (EM)clusters used for electron and photon reconstruction, and EM-hadronic combined clusters
used for the tau lepton reconstruction and jet ﬁnding.
The ATLAS calorimeters are divided in the η − φ grid by a ﬁxed size of Δη × Δφ.
The EM tower building starts from a size of Δη ×Δφ = 0.025×0.025, and they extend
up to |η| = 2.5, which means that the EM calorimeter is divided into 51,200 grids. They
will go through further procedures to be used for electron or photon reconstruction [111],
but the EM towers are not considered in this thesis.
The combined towers exploits larger conﬁguration size of Δη × Δφ = 0.1 × 0.1,
extending up to |η| = 5.0, thus 100 × 64 = 6, 400 towers in total. Projective cells which
completely ﬁt inside the tower, are simply added to the tower energy. For projective cellswith bin size larger than the tower (e.g. the D-layer cells of TileCal) and non-projective
cells (e.g. cells in the Forward Calorimeter), only some fraction of the cells contributeto the tower (Figure 7.24). In such cases, weight is applied to each cell considering thefraction of volume inside the tower. During the tower building, no further corrections orcalibrations are applied.

123

Figure 7.24: Contributions of calorimeter cells to combined towers shown in schematicη − φ grid.

7.3.2

Topological Clustering

The main idea of the topological clustering algorithm is to group cells in three-dimensions
and over various calorimeter partitions, based on the signiﬁcance of their energy deposits
in each cell and its relationship among the adjacent cells [112]. In the topological clustering algorithm, cells are classiﬁed as “seeds”, “neighbors”, and “others” with respect totheir signiﬁcance above the expected noise levels. Cells whose absolute energy pass thenseed σ (σ is noise value of the cell) are deﬁned as “seeds”. Cells adjacent to “seed” cells
are called “neighbors” when their energy is larger than nneighbor σ. “Neighbors” themselves
can behave as new “seeds”, expanding the clusters by including their adjacent cells with
|E|/σneighbor > nneighbor . Cells bound to “neighbor” cells are deﬁned as “others” when
their energy exceeds nothers σ. Detailed criteria for merging and splitting of the clusters
are explained in [112].
There are several types of Topological Clusters (Topoclusters). EMTopoclusters arereconstructed solely from cells in the EM calorimeter. “AllCalo” Topoclusters are reconstructed from all the calorimeter cells among EM and hadronic calorimeter, and are used
as inputs for jet reconstruction and the ETmiss calculation. Topoclusters reconstructed
solely from TileCal were also used for the commissioning studies up to the Tier-0 reconstruction of the Fall 2008 data ( Tier-0 is the initial Grid site located at CERN, responsible
for storing the raw data from the experiments, and running the ﬁrst reconstruction ofdata. See Acronyms and Terms for more details). The settings (nseed , nneighbor , nothers ) =(4, 2, 0) were used as the default for all types of Topoclusters in commissioning runs (andan additional ET > 0.1 GeV cut was applied to EM Topocluster), though the threshold for

124

φ

>2σ
>2σ >2σ>2σ >4σ >2σ>2σηFigure 7.25: Schematic view of Topological Clustering algorithm. The red box indicatesthe “seed” cell, the orange boxes are “neighbors”, and the yellow boxes are “others” cells.
Note that the actual clustering takes place in three dimensional directions.
EM Topoclusters were updated to (nseed , nneighbor , nothers ) = (4, 3, 0) with ET > 0.3 GeV
cut. Here, investigations were extensively done for Topoclusters using all the calorimeterswith (nseed , nneighbor , nothers ) = (4, 2, 0) threshold.

7.3.3

Performance of Topoclusters in Random Stream Data

Topological clustering has a sophisticated noise suppression algorithm, but on the other
hand, it is sensitive to the noise description in the database. Figure 7.26 shows thenumber of reconstructed TileCal Topoclusters in M5 run 29576 NIM0 stream in the Fallof 2007 (NIM0 stream consists of events triggered by the TileCal towers with coincidence
in the top and bottom drawer, thus include many cosmic events). The blue line shows thenumber of reconstructed clusters with the noise values stored in the database. At thattime, the noise values assumed in the Monte Carlo simulation were stored in the database
and was also used for the real cosmic data (see the left ﬁgure in Figure 7.4). The red line
and black lines show reconstructed number of clusters with the noise values measured
from the same run (Gaussian σ used for the red line, and the RMS for the black line).

125

# of events

10000
104
9000
8000
103
7000

Noise RMS

6000
102
5000

Noise Sigma
CaloNoiseTool

4000
10
3000
2000
1000
1
0

10

20

30

40

50
60
70
# of clusters per event

Figure 7.26: Sensitivity of Topoclusters to noise database. Number of reconstructed
TileCal Topoclusters in M5 run 29576 NIM0 stream is shown.
From the penetration of a cosmic muon, two clusters should be reconstructed in average,
even though there can be splitting of clusters due to geometrical eﬀects. However, themean number of reconstructed TileCal Topoclusters was 5.2 even when considering theRMS of the noise, which is more than twice the expectation. Such phenomena have been
continuously observed in the commissioning data also for the combined Topoclusters (andwill be mentioned again below).
As mentioned above, TileCal has been observing more reconstructed clusters thanthe expectation. Since the noisy channels are mostly identiﬁed and masked during thereconstruction, the source of extra clusters must have some other origin. It was foundthat those extra clusters were reconstructed due to the non-Gaussian tails in the TileCalcell energy distributions (Figure 7.27) [A.3.4.29].
The Topological Clustering algorithm makes use of the threshold considering the
conﬁdence level of the energy in each cell. The default algorithm extracts the noise values
from the database, and simply multiply by the nseed , nneighbor , nother to set the threshold.
Due to the non-Gaussian tails in the TileCal cells, this linear relationship between the
conﬁdence level and n× RMS does not hold, underestimating the seed threshold by
about 60 % at most (the left ﬁgure of Figure 7.28). Such eﬀects can aﬀect the cluster
performance to a great extent, and can also degrade the resolution of ETmiss as will bementioned in the next section. In order to reconstruct the topological clusters consistent
with the expectation, the correct values for nseed σ, nneighbor σ, nother σ must be oﬀered inregards to the real conﬁdence levels.
Here, the default Topological Clustering algorithm was updated to extract the correct
values of noise threshold, and was compared with the default reconstruction [A.3.4.29]. In
Figure 7.29, the reconstructed numbers of Topoclusters are shown for various conditions.

126

Figure 7.27: Ratio of the RMS and the Gaussian σFigure 7.28: Ratio between the conﬁdence level and the Gaussian sigma

Table 7.4: Mean of reconstructed number of Topoclusters in each calorimeter region
MC Old Repro Dec08 Repro New Topo
Tile
0.6
10.3
8.3
0.2
EM
9.5
23.9
15.9
15.9
HEC
0.4
2.5
0.4
0.4
FCAL 0.2
0.8
0.2
0.2
Total 10.8
38.0
25.0
17.0

127

Number of Topoclusters

Figure 7.29: Number of reconstructed Topoclusters

3
5 ×10

100
10

90
80
104
70
60
3
50
10

40
30
102
20

Empty MC
91639 RNDM Old Reproc.
91639 RNDM Reproc.
91639 RNDM New Topo

10
−5

−4

−3

−2

−1

0

1

2

3
4
5
Topocluster ηFigure 7.30: η distributions of reconstructed Topoclusters

128

Figure 7.31: η −φ distributions in December 08 reprocessing data and new reconstruction

129

The green line shows the Tier-0 reconstruction of run 91639 RNDM stream data. When
the Tier-0 reconstruction was done, the database was containing inconsistent noise values,
and was underestimating them in the TileCal. The blue line shows the distribution
from the December 2008 Tier-1 reprocessing. The correct noise values are stored in the
database, and energy reconstruction in the LAr was improved, which resolved many ofthe pedestal shifts observed in the Tier-0 data. The red line shows the improvement
after the updates in the Topocluster algorithm taking care of the non-Gaussian featureof the noise in TileCal. The black line shows the noise Monte Carlo samples, and is
the expectation. There still remains some discrepancy in the number of reconstructedclusters, but it is mainly due to the slight pedestal shift in the LAr EM calorimeterand the HV problem observed in the presampler. The numbers are explicitly shown in
Table 7.4, and shows that now the TileCal performance is greatly improved and wellconsistent with the expectation. The excess of reconstructed numbers of clusters and theimprovement after the new Topocluster algorithm are obviously visible in Figure 7.30
and 7.31.

7.4

Performance of Missing ET in Random Stream
Data

Missing ET (ETmiss ) is a variable which quantiﬁes the imbalance of energy in the transverse
plane. It provides indirect detection of weakly interacting particles such as neutrinos andpossibly dark matters if they exist. Its performance is crucial for numerous measurements
not only in the Standard Model phenomenon (W, tt̄ decaying leptonically) but also for
Higgs, supersymmetry, and extra dimension searches.
Here, the ETmiss algorithm is brieﬂy reviewed, and the performance of the calorimeter
term of ETmiss from the random stream data was investigated.

7.4.1

Missing ET Algorithm in ATLAS

Two approaches exist in the ETmiss algorithm in ATLAS; calorimeter cell-based and objectbased algorithms. Here, the former will be explained and used.
The main components come from the calorimeter, muon, and the cryostat correction(correction for the energy lost when particles pass through the cryostat) term [23],miss
miss,Calomiss,Muon
miss,Cryo= Ex,y+ Ex,y+ Ex,y
.
Ex,y

(7.5)

Several calibration schemes exist on top of the raw EM scale, in order to achieve goodresolution. Various ETmiss quantities with diﬀerent calibration levels are deﬁned in ATLAS (see Table C.1 in Appendix C). Here, the performance of the calorimeter terms ofETmiss was investigated with the random stream data. The overview of the Missing ET
algorithm including the muon and cryostat terms in ATLAS is described in Appendix C.

130Calorimeter Term in ETmiss
The x and y components of the calorimeter term are calculated from the vector sum ofenergy in each cell.

miss,Calo
cell
= −
Ex,y
(7.6)
Ex,y
ExcellEycellcell
cell= E sin θ cos φ= E cell sin θ sin φ

(7.7)
(7.8)

The angular directions of cells are deﬁned from the central position of the cell. Eightcategories exist in the ATLAS framework for the calorimeter term. Four of them are atthe EM-scale, and the others are calibrated to the hadronic scale with H1-style or the
Local Hadronic Calibration scheme [23]. Random stream data oﬀers a useful training
ground to validate the calorimeter term of the ETmiss , and will be mentioned in the next
section.

7.4.2

Performance of ETmiss in Data and Its Improvement

Measurement of ETmiss in random stream data allow us to understand the performance ofnoise term. Noise term becomes important when the high resolution of ETmiss is required.
Higgs decaying into di-tau is an example, since the resolution of ETmiss will inﬂuence theresolution of the invariant mass of the two taus.
For this study, we produced toy Monte Carlo samples which assume incoherent Gaussian noise. The same noise RMS was taken from the ATLAS noise database for eachchannel. The comparison of data with the toy Monte Carlo allows for the validation of
the ETmiss performance.
MET Base Algorithm
Figure 7.32 shows the distributions of Missing Ex and EY from the random stream
data and toy Monte Carlo. MET Base algorithm was used for the ﬁgure. Fairly goodconsistency is observed, but slight shift in the mean of Missing EY was found. Thisphenomenon matches with the pedestal shifts we have observed for some cells. Figure 7.33
shows the ETmiss distribution from MET Base algorithm. Large tail from the random
stream data is due to the problematic coherent noise in the barrel presampler that wehave been observing. After removing the contribution of the barrel presampler in theETmiss calculation, the consistency of ETmiss between toy Monte Carlo and data becomesmuch better [103].
MET Topo Algorithm
Although, the performance of MET Base in random data was basically consistent with the
expectation, it was found that that the resolution of missing Ex and Ey (and thus ETmiss )in MET Topo algorithm was largely degraded compared to the expectation (Figure 7.34

131

All Calo

Number of Events

Number of Events

5000

5000

Gaussian Noise Model
Random Stream Data

4000

Gaussian Noise Model
Random Stream Data

4000

3000

3000

2000

2000

1000

1000

−20

All Calo

−15

−10

−5

0

5

10
15
20
Missing EX (GeV)

−20

−15

−10

−5

0

5

10
15
20
Missing EY (GeV)

Figure 7.32: “MET Base” Missing EX and EY from random stream data and toy Monte
Carlo samples from a Gaussian noise model. Black histograms show the distributions
from toy Monte Carlo samples assuming incoherent Gaussian noise. Red points show the
distributions from the random stream data.

Number of Events

7000
6000
103
5000

Gaussian Noise Model
Random Stream Data

40002
10
3000
10
2000
1000
1
0

2

4

6

8

10

12

14 16 18
20
Missing ET (GeV)

Figure 7.33: “MET Base” ETmiss from random stream data and toy Monte Carlo samples
from a Gaussian noise model. Black histograms show the distributions from toy Monte
Carlo samples assuming incoherent Gaussian noise. Red points show the distributions
from the random stream data.

132

and 7.35). This degradation was largely due to the non-Gaussian shape of the electronicnoise in TileCal as mentioned in the previous section for the Topocluster performance.
Figure 7.36 and 7.37 shows the improvement seen after the updated Topocluster
algorithm was used. Tails still remain, which is coming from the non-Gaussian feature.
Those tails cannot be simply removed by applying the correct noise threshold. Use ofﬁt quality factor of the pulse shape in the channels may oﬀer a way to remove suchremaining outliers.

133

All Calo

Number of Events

Number of Events

16000

16000

Gaussian Noise Model
Random Stream Data

14000
12000

Gaussian Noise Model
Random Stream Data

12000

10000

10000

8000

8000

6000

6000

4000

4000

2000

2000

−10

All Calo

14000

−8

−6

−4

−2

0

2

4
6
8
10
Missing EX (GeV)

−10

−8

−6

−4

−2

0

2

4
6
8
10
Missing EY (GeV)

Figure 7.34: “MET Topo” Missing EX and EY from random stream data and toy Monte
Carlo samples from a Gaussian noise model. Black histograms show the distributions
from toy Monte Carlo samples assuming incoherent Gaussian noise. Red points show the
distributions from the random stream data.

Number of Events

25000
104

All Calo

20000

Gaussian Noise Model
Random Stream Data

3

10
15000
102
10000
10
5000
1
0

2

4

6

8

10

12

14 16 18
20
Missing ET (GeV)

Figure 7.35: “MET Topo” ETmiss from random stream data and toy Monte Carlo samples
from a Gaussian noise model. Black histograms show the distributions from toy Monte
Carlo samples assuming incoherent Gaussian noise. Red points show the distributions
from the random stream data.

134

All Calo

Number of Events

Number of Events

104
16000

104
16000

Gaussian Noise Model
Data
Data (New Topo)

14000
103
12000

All Calo

Gaussian Noise Model
Data
Data (New Topo)

14000
103
12000

10000

10000

102
8000

102
8000

6000

6000

10
4000

10
4000

2000
1
−10

2000
1
−10

−8

−6

−4

−2

0

2

4
6
8
10
Missing EX (GeV)

−8

−6

−4

−2

0

2

4
6
8
10
Missing EY (GeV)

Figure 7.36: “MET Topo” Missing EX and EY from random stream data with andwithout updated Topocluster algorithm and toy Monte Carlo samples from a Gaussian
noise model. Black histograms show the distributions from toy Monte Carlo samples
assuming incoherent Gaussian noise. Red points show the distributions from the random
stream data.

Number of Events

25000
104

All Calo

20000

Gaussian Noise Model
Data
Data (New Topo)

3

10
15000
102
10000
10
5000
1
0

2

4

6

8

10

12

14 16 18
20
Missing ET (GeV)

Figure 7.37: “MET Topo” ETmiss from random stream data with and without updated
Topocluster algorithm and toy Monte Carlo samples from a Gaussian noise model. Blackhistograms show the distributions from toy Monte Carlo samples assuming incoherent
Gaussian noise. Red points show the distributions from the random stream data.

135

Chapter 8
Calorimeter Measurement with
Cosmic Ray Data
In this chapter, the performance of the ATLAS calorimeters was investigated with cosmic
ray data. High energy deposits in the calorimeters have been continuously observed during the cosmic ray data taking, and the origin of such events were investigated. Monte
Carlo simulation of cosmic muons reaching the ATLAS detector (including the interactions through the ground) was exploited to understand the data. Also, such high energy
deposits in the calorimeters originate fakely reconstructed “jets” and large ETmiss , andbecome background to the physics analyses using jets and ETmiss .
In this chapter, we show that such contributions are well described by the Monte
Carlo simulation and understood. Strategies to reject the non-collision background (socalled the “cleaning”) will be discussed in Chapter 9.

8.1

Cosmic Rays

Cosmic rays are high energy particles traversing cosmic space. The “primary” cosmic
rays originate and are accelerated by astrophysical sources. Electrons, protons, helium,
and other heavier nuclei produced inside the stars belong to the category. Nuclei such aslithium, beryllium, and boron are produced by those particles interacting with interstellar
gas. Antiprotons and positrons are also mostly produced through the interaction of
those primary cosmic rays during the propagation. Neutral particles such as γ-rays andneutrinos compose important fraction of the cosmic rays as well. The energy of cosmic
rays spreads over vast scales, but the acceleration mechanism is not yet fully understood.
When cosmic rays reach the earth, they undergo numerous interactions with theatmosphere, and generate secondary particles (these are often called as “secondary cosmic rays”). Incident protons and nuclei will initiate a hadron shower. Their products
are grouped into three components: hadronic, muonic, and electromagnetic components(Figure 8.1). Muons originate from the decays of kaons and pions, and make up thelargest fraction of the cosmic rays (except for the neutrinos) reaching the earth surface.
Due to the penetration ability of muons, they are detected even at deep underground.
The vertical ﬂuxes of cosmic rays in the atmosphere is shown in Figure 8.2.
136

Figure 8.1: Schematic view of air shower development [117][118].

Figure 8.2: Vertical ﬂuxes of cosmic rays in the atmosphere as a function of atmosphericdepth [74].

137Table 8.1: Proﬁle of generated cosmic Monte Carlo simulation samples.
Generated μ Momentum
10 - 100 GeV
100 - 300 GeV
300 GeV - 1 TeV
1 - 3 TeV
3 - 5 TeV

8.2

Area (Z × X)
300 m × 300 m
600 m × 600 m
1.5 km × 1.5 km
2 km × 2 km
3 km × 3 km

Events
33.4k
105k
144k
145k
142k

Expected Time Duration
134 s
1.47 ×103 s
1.40 ×104 s
2.16 ×105 s
3.75 ×106 s

Cosmic Monte Carlo Simulation in ATLAS

We produced cosmic Monte Carlo simulation samples to understand the performance ofthe calorimeters. Here, the overview of the simulation method is given.
The cosmic muons are generated at the ground level, with a ﬁxed generation area
(Figure 8.3). The muons are randomly generated according to the expected muon spectrum [115]. In this simulation, only single muon events were considered, and did notinclude air shower events. In this thesis, diﬀerent areas were used for each momentum
range (larger areas for high momentum muons; see Table 8.1) for two reasons. The
ﬁrst reason is that when cosmic muons have large energy, they will have more penetration power and can survive even for large incident angles and long traverse through theground. Secondly, the ﬂux of cosmic muons in large azimuthal angles tend to become
more signiﬁcant for high energy muons, because in order to produce such high energymuons from pion and kaon decays, those parent mesons must travel longer distance todecay due to the Lorentz boost (∼ 55 km for muons with 1 TeV energy; the thicknessof the earth troposphere is ∼ 17 km) and tend to originate from mesons coming in large
azimuthal angles.
In order to understand the eﬀect of rock overburden for the propagation of cosmic
muons, the simulation starts from the ground level, and takes into account the interactionof those cosmic muons inside the rock over ATLAS. The simulation also takes account of
the two shafts (PX14 and PX16 in Figure 8.4) used for the transport of ATLAS detector
units used during the construction era.
Since most of the muons simply generated by the expected spectrum and ﬂux will
miss the ATLAS detector, two event ﬁltering algorithms were performed.
The ﬁrst ﬁltering algorithm requires the generated muons to point at a sphere centeredat the interaction point with a ﬁxed radius (25 meters in this thesis). The extrapolation
to the sphere was done assuming a straight line from the generated point.
The second ﬁltering algorithm is often called as the “volume ﬁltering” and makes
use of the Geant-4 hits in some speciﬁc ATLAS sub-detectors. Specifying the “detector
volume,” for the simulation, the ﬁltering will run to record only events with Geant-4 hits
in the sub-detector. Here, the calorimeter volume (“MuonEntryLayer”) was used for theﬁltering, because the Monte Carlo samples were produced to compare with the L1Calostream data (events triggered by the calorimeter towers).
In this thesis, Monte Carlo samples were produced in ﬁve separate slices in muon mo-

138

Z
X

muons

radius 25 m

Figure 8.3: Schematic view of cosmic Monte Carlo simulation. A single cosmic muon is
generated inside ﬁxed area, and events are ﬁltered to have the muon point to ATLAS.

Figure 8.4: Layout of ATLAS cavern at Point 1. Surface buildings and access shafts(PX14, PX15, PX16) are shown.
139

mentum (10 - 100 GeV, 100 - 300 GeV, 300 GeV - 1 TeV, 1 - 3 TeV, 3 - 5 TeV) [A.3.4.42],
in order to provide enough statistics even in high momentum tails.
Figure 8.5 shows the Z-X distributions (Z: the direction of the beam line, X: direction
pointing to the center of the LHC ring, Y: direction pointing perpendicular to the ground)of the generated muons which survived the ﬁltering. The eﬀects from the shafts arevisible for low energy cosmic muons (the upper left ﬁgure). Considering the thickness of
the ATLAS overburden (∼ 100 m), the expected “cut-oﬀ” of muons reaching the detector
is around 50 GeV [116]. The cut-oﬀ momentum is visible in the simulated momentum
distribution of muons reaching the detector (Figure 8.7). Thus, for low energy muons,the most-likely way for them to reach the detector is by passing the shaft and avoid theinteractions with materials. The eﬀects of the shafts were conﬁrmed also from the realcosmic data (Figure 8.6), where the impact points of cosmic muons at the ground levelare estimated from the extrapolation of muon tracks reconstructed by RPC hits [106].
On top of the two large shafts used for the detector transport (PX14 and PX16), eﬀects
from two small elevator shafts (PX15 and PM15) were visible in the real data. These
small shafts are currently not included in the cosmic simulation (planned to be included
in newer Athena versions). Since the shafts make signiﬁcant eﬀects mostly on low energymuons, and also considering the small size of the elevator shafts, their impact will not be
critical for the current studies, which the main emphasis is on high energy cosmic muonsdepositing large energy in the calorimeters.
Figure 8.8 shows the momentum distribution of Muon Spectrometer tracks from cosmic run 91890 L1Calo stream and Monte Carlo simulation samples. Run 91890 was oneof the long runs with good quality of the muon spectrometer and other detector conditions. Since, the trigger simulation is currently not included in the cosmic Monte Carlosamples, only events with a jet with ET >20 GeV were considered in the plot to avoid
trigger eﬀects. The Monte Carlo simulation samples from each muon momentum slices
were merged considering the ﬂux, and normalized to data by entries. The shape of the
distribution is well described by the simulation. We will further look into the calorimeter information with the cosmic ray data and Monte Carlo simulation in the following
sections.

8.3

High Energy Events from Cosmics

High energy deposits in the ATLAS calorimeters from cosmic rays have been observed forseveral years, as reported in the previous notes [96][119]. These energy deposits, ranging
from tens of GeV up to several TeV, can arise from noisy channels, air shower events,and muons undergoing a hard bremsstrahlung in the calorimeters. An additional sourceof “fake” energy deposit can include an incorrect signal reconstruction due to errors in
the calibration or a distorted pulse shape. In the following section we investigate highenergy events in recent cosmic ray runs and discuss their possible origins.
In Figure 8.9, the distribution of the total transverse energy of all calorimeter cells withenergy larger than twice the electronic noise (so called “SumET ” in Athena MET Basealgorithm), were calculated and shown for run 90272 L1Calo stream. This run was anovernight run with good detector quality on September 28, 2008, with the time duration
140

50

40

0

30

X [m]

200

30

100

25

15

20

−100

10

−200

0

−300
−300

−50

0

50

100

150
Z [m]

50

600
400

40

200
30
0
20

−200
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10
5
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0

100

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300
Z [m]

1000

X [m]

−100

20

0

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X [m]

35

60
500
50
40

0

30
20

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10

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200

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600
Z [m]

0

−1000
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X [m]

1500

0

60

1000

50

500

40

0

30

−500

20

−1000

10

−1500
−1500 −1000

−500

0

500

1000

1500
Z [m]

500

1000
Z [m]

0

Number of Generated Muons

0

0

70

10

−600 −400 −200

Number of Generated Muons

50

300

Number of Generated Muons

100

Number of Generated Muons

60

Number of Generated Muons

X [m]

150

0

Figure 8.5: Origin of cosmic muons at the surface in the Monte Carlo simulation. Eachﬁgure consists of diﬀerent muon momentum ranges; 10 - 100 GeV (upper left), 100 - 300
GeV (upper right), 300 GeV - 1 TeV (middle left), 1 - 3 TeV (middle right), and 3 TeV- 5 TeV (bottom).

141

Number of Events

Figure 8.6: Impact point of cosmic muons on surface from the cosmic ray data, estimatedfrom the extrapolation of muon tracks reconstructed by RPC hits [106].
400
350
300
250
200
150
100
50
10

20

30

40

50

60
70
80
90
100
Truth Muon Momentum (GeV)

Figure 8.7: Momentum of generated muons that reached the ATLAS detector. A cut-oﬀ
around 50 GeV is visible in the distribution.
142

Number of Muon Tracks

50000
45000
104
40000
Cosmic L1Calo

35000
103
30000

Cosmic MC

25000
102
20000
15000
10
10000
5000
1
0

500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Muon Momentum [GeV]

Figure 8.8: Momentum distribution of stand-alone muon spectrometer tracks in cosmic
data run 91890 L1Calo stream and cosmic Monte Carlo samples. Cosmic Monte Carlo isnormalized to data by entries.
of 16 hours, having 350k events recorded in the L1Calo stream. Only events with a jet(ET > 20 GeV) were included in the ﬁgures in order to consider the trigger eﬀects in thesimulation as well.
Cosmic Monte Carlo samples are produced in the way mentioned in the previous
section. The simulation only considers single muon events, and does not generate any
air showers. From the simulation, we conﬁrmed that the TeV energy deposit is actually possible from a single cosmic muon [A.3.4.42]. These high energy deposits in thecalorimeters are due to the hard bremsstrahlung from high energy muons (will be explicitly shown in Section 8.4.2). There was a discrepancy of about 7 % in the absolute event
rate between the cosmic Monte Carlo simulation and real data. This is rather a goodconsistency considering that the uncertainty in the previous measurements of cosmic ray
ﬂux are large [116]. The Monte Carlo samples in Figure 8.9 are normalized to data by theentries. The same normalization factor was applied to the corresponding plots in various
calorimeter regions (Figure 8.10). The triggered event rate in the stream is summarizedin Table 8.2. TeV events were recorded in 6 × 10−4 Hz, which is about 2 events per hour.
The shape of the distributions are in good agreement throughout various energy regions.
Figure 8.11 is an event display of one of the high energy events from run 90272 L1Calostream, in which an energy deposit of more than 1 TeV was recorded. In the event display,
the green lines show the muon segments. The muon segments in X-Y view (the left ﬁgurein Figure 8.11) are pointing to the interaction point, due to the lack of RPC φ hits.
The yellow boxes indicate energy deposits in the Tile Calorimeter. The direction of thereconstructed jet is shown by the gray marker. The light blue dotted line is the direction

143

3

Number of Events

100
105

×10

90
4
10
80

Cosmic Data L1Calo
Cosmic Single Muon MC

70
103
60
2
50
10

40
10
30
20
1
10
10−10

500

1000

1500

2000
2500
Sum ET (GeV)

Figure 8.9: Sum ET distribution from cosmic ray data and Monte Carlo simulation. Run
90272 L1Calo stream data is compared with the cosmic Monte Carlo samples normalized
to the data by entries. Energy is calibrated at the EM scale.

of ETmiss . The highlights in the MDT indicate hits (yellow for any hits, and orange whentracks are reconstructed). No track was reconstructed in the inner detector for this event.
The R-Z projection is shown in the right ﬁgure of Figure 8.11, where the energy depositin the calorimeter is clearly seen along the muon path. This event most likely arose froma muon undergoing a hard bremsstrahlung. Such events can fake new physics signals
such as “monojet” candidates, or even multi-jet signatures when overlapped with QCD
events. Figure 8.12 shows an example of air shower events from the same run and stream.
Air shower events also contribute to the high energy tail of the Sum ET distributions,and can initiate a fake multi-jet signature. Such events can be identiﬁed by the presenceof numerous numbers of reconstructed muon segments. We explore strategies to tag andremove those non-collision background in Section 9.1.3.

8.4

Test of Object Reconstruction in the Calorimeters

8.4.1

Topological Clusters in Cosmic Ray Data

The number of clusters per event reconstructed from cosmic ray data is shown in Figure
8.13. During M5 runs, the electronic noise in LAr was overestimated in the database;

144

3

3

×10

100
105

Number of Events

Number of Events

100
105
90

4
80
10

90

4
80
10

Cosmic Data L1Calo
Cosmic Single Muon MC

70

3
10
60

Cosmic Data L1Calo
Cosmic Single Muon MC

70

3
10
60

50

EMB

2
10
40

50

EMEC

2
10
40

30
10
20

30
10
20

10
1
0

10
1
0

500

1000

1500

2000
2500
Sum ET (GeV)

3

500

1000

1500

2000
2500
Sum ET (GeV)

3

×10

100
105

Number of Events

100
105

Number of Events

×10

90

4
80
10

90

4
80
10

Cosmic Data L1Calo
Cosmic Single Muon MC

70

×10

3
10
60

Cosmic Data L1Calo
Cosmic Single Muon MC

70

3
10
60

50

TILECAL

2
10
40

50

HEC

2
10
40

30
10
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30
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1
0

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0

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Sum ET (GeV)

500

1000

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2500
Sum ET (GeV)

3

Number of Events

100
105

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90

4
80
10

Cosmic Data L1Calo
Cosmic Single Muon MC

70

3
10
60

50

FCAL

2
10
40

30
10
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0

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40

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80

100 120 140 160 180 200
Sum ET (GeV)

Figure 8.10: Sum ET distribution in each calorimeter partition in run 90272 L1Calostream. Energy is calibrated at the EM energy scale. From the upper left, distributions
from LAr EM Barrel, LAr EM End-cap, TileCal, LAr hadron End-cap, LAr Forward,
are shown. The normalization factor for the Monte Carlo samples are the same as theone used in Figure 8.9

145

ATLAS

2008-09-28 04:52:37 CEST event:JiveXML_90272_214077 run:90272 ev:214077 geometry: <default>

Atlantis

YX Projection

-10

0

Y (m)

10

FishEye

-10

0

X (m)

10

Figure 8.11: A TeV event in run 90272 L1Calo stream.

Figure 8.12: An air shower event in run 90272 L1Calo stream.

146

Table 8.2: Triggered event rate of high energy events in run 90272 L1Calo stream# of events

20000
104
18000

Overall Rate [Hz]2.689 ± 0.007
0.320 ± 0.002(9.0 ± 0.1) × 10−2
(2.2 ± 0.2) × 10−3
(6 ± 1) × 10−4

TileCal Rate [Hz]1.181 ± 0.005
0.173 ± 0.002(6.5 ± 0.1) × 10−2(2.0 ± 0.2) × 10−3
(5 ± 1) × 10−4

No Masking (CaloNoiseTool)
No Masking (measured noise)
Masked (measured noise)

16000
103
14000

10000
104
9000
8000
103
7000

12000
102
10000

6000
102
5000

8000
10
6000

4000

4000
1
2000

2000

10-10

LAr EM Rate [Hz](6.8 ± 0.1) × 10−2
(1.11 ± 0.04) × 10−2
(3.6 ± 0.3) × 10−3
(1.0 ± 0.4) × 10−4
(7 ± 3) × 10−5

# of events

SumET
20 - 50 GeV
50 - 100 GeV
100 - 500 GeV
500 GeV - 1 TeV> 1 TeVMy Noise RMS
My Noise Sigma
CaloNoiseTool

10
3000
1000
1
10

20

30

40
50
60
# of clusters per event

0

10

20

30

40
50
60
70
# of clusters per event

Figure 8.13: The number of EMTopoclusters in M5 run 29576 NIM0 stream (left). The
number of Tile Topoclusters per event in the same run and stream (right).
thus by using the correctly-measured noise had the eﬀect of increasing the number ofclusters. After masking bad LAr channels, the number of EMTopoclusters signiﬁcantly
decreased, close to the level expected from Monte Carlo simulation.
In TileCal, bad channels were masked in the default reconstruction. In contrast
to LAr, the electronic noise for TileCal was underestimated in the database, and thenumber of Tile Topoclusters signiﬁcantly decreased after the correctly-measured noise
values were used. A slight diﬀerence was observed when the noise in TileCal was deﬁned
as the sigma of from a Gaussian ﬁt compared to the RMS of the cell energy distribution.
This is due to the fact that the cell energy distribution in TileCal has non-Gaussian tails,
as mentioned in Section 7.1. In that case, we recommend the use of the RMS of the cell
energy distribution for TileCal cluster reconstruction.
From our investigation, we ﬁnd that:• Fake Topoclusters are formed from bad channels (noisy channels), because thosechannels become “seeds”.
• Wrongly-estimated noise values in the database signiﬁcantly aﬀect the number ofreconstructed clusters per event.
The energy distribution of Tile Topoclusters is shown in Figure 8.14 for M5 run 29576,
using both TGC-triggered events and TileCal-triggered events (NIM0 stream; this stream
147

Figure 8.14: Energy of Tile Topoclusters for M5 run 29576, for both TGC-triggered andTile-triggered events.
was later merged with CosmicMuons stream in Fall 2008 data). The distribution from
TGC-triggered events is normalized to that from the NIM0 stream to compare the shapeof the distributions. Here, the TileCal cluster energies from TGC-triggered events arefound to be almost pedestal-like, since those events rarely hit TileCal due to the geometryof the trigger. Thus, a clear separation of clusters originating from noise and those formed
from muon energy deposits is observed. A peak around 3 GeV was observed in Tile
Topoclusters from the NIM0 stream, which is consistent with what we expect from muonenergy deposits in TileCal.

8.4.2

Jet Reconstruction With Cosmic Ray Data

Jets are highly collimated bundles of hadrons (and their decay products) produced by
the fragmentations of quarks and gluons. The performance of jet reconstruction is crucially important for Standard Model measurements such as jet cross sections, as well as
for new physics searches (Supersymmetric particle search, Higgs particle search, extradimensional black holes, etc.) Here, the jet reconstruction schemes used in ATLAS are
brieﬂy reviewed, and their performance is shown.
Jet Algorithms in ATLAS
There are several jet ﬁnding schemes implemented in ATLAS: ﬁxed cone jet ﬁnder, sequential recombination such as kT algorithms, and etc. [23] Each jet ﬁnding schemes makeuse of calorimeter clusters as inputs and scan the calorimeters and form a jet. Combined
towers and Topoclusters are the standard inputs to jet reconstruction. An overview ofjet reconstruction in ATLAS is shown in Figure 8.15.
In the ﬁxed cone jet ﬁnder, all cluster inputs are sorted in decreasing order of theirtransverse momenta pT . When the cluster with the highest pT is above the seed threshold

148

(pT >1 GeV), all the clusters within the deﬁned cone size Rcone are combined withthe seed. A new direction is calculated from the sum of the four-momenta from all
the combined clusters, and combining the surrounding clusters will be iteratively done
until the new seed is stable and stays the same as the one before the iteration. Whenthe jet is stable, the next iteration will start with the next-leading clusters passing theseed threshold. Such procedures continue until all the inputs are considered for jet
reconstruction. Cone size of 0.7 has been proposed for the QCD dijet studies, and smaller
cone size of 0.4 has been used in Top and supersymmetric search studies, where events
tend to be more crowded and have more jets.
“Jets” in Cosmic Ray Data
“Fake jet” can be formed from calorimeter noise, bad channels, and cosmic rays. Running jet reconstruction on cosmic ray events is an important approach to validate thereconstruction software and study the jet algorithm performance at an early stage before collision data are available. Fake jets from the eﬀects mentioned above have been
observed in the real cosmic data, and understanding their properties will lead to thedevelopment of possible quality cuts on jets. (Note that here we refer to “jet” as theresults of running reconstruction algorithms on cosmic ray data, not to be confused withthe real hadron jets that will result from proton-proton collisions.)
In Figure 8.16, the number of reconstructed jets with an ET > 7 GeV cut is shown for
2 cone sizes (R = 0.4 and 0.7) and 2 types of input (combined towers and Topoclusters).
ET > 7 GeV is the default transverse energy threshold used for jet containers in the
ATLAS data model. Figure 8.17 shows the same distributions, except that the thresholdis raised to ET > 20 GeV, which is the standard transverse energy cut applied for physics
analyses. The triggered event rate of reconstructed jets are also summarized in Table 8.3(for TopoJets with cone size 0.4 and ET > 20GeV).
In Figure 8.18, the distribution of reconstructed jet transverse energy is shown for
CosmicMuons and L1Calo stream. In the CosmicMuons and L1Calo streams, a larger
number of jets was reconstructed compared to the RNDM stream. This gives the ﬁrst
indication that additional fake jets can be initiated by the energy deposits of cosmic raymuons. Figure 8.19 conﬁrms the hypothesis, since the reconstructed jets are found to beconcentrated around φ ∼ ±π/2 for the CosmicMuons stream, which is triggered by the
RPC and TGC In the L1Calo stream, jets populated the entire η −φ region (Figure 8.20).
This is due to the fact that L1Calo does not require such a top-bottom coincidence, butrather simply an energy deposit over threshold in the entire calorimeter.
Multi-jet events are also observed in the CosmicMuons and L1Calo streams. These
events are likely formed by air shower events, which is supported by the observation ofmultiple hits in the muon spectrometer system. An example event display of such an
event is shown in Figure 8.12.
Finally, comparison of jet transverse energy distributions from run 90272 L1Calostream and cosmic Monte Carlo simulation samples was done for Cone4H1TowerJets andCone4H1TopoJets (Figure 8.21). The normalization factor extracted from Figure 8.9 wasused, and the distributions were well consistent for both the TowerJets and TopoJets.

149

Figure 8.15: Schematic overview of jet reconstruction in ATLAS.

150

Tower R=0.7
Tower R=0.4

1604
10
140

Topo R=0.7
Topo R=0.4

3
120
10
100

10
9
1
8

Tower R=0.7
Tower R=0.4

7
10−1
6

Topo R=0.7
Topo R=0.4

5
10−2

2
10
80

20
1
0

4

6

8 10 12 14 16 18 20 22# of Jets91
8

Tower R=0.7
Tower R=0.4

7
10−1
6

Topo R=0.7
Topo R=0.4

10 3

4
−3
10 3

2
10−4
1

2
10−4
1

90272 C.M.

−3

−5

2

10

5
10−2

4

90272 RNDM

60
10
40

Event Rate [Hz]Event Rate [Hz]×10

Event Rate [Hz]

3

200
5
10
180

10 00

90272 L1Calo

−5

2

4

6

8 10 12 14 16 18 20 22# of Jets

10 00

2

4

6

8 10 12 14 16 18 20 22# of Jets

Figure 8.16: Number of reconstructed cone-algorithm jets (ET > 7 GeV) for various
cluster input and cone size, in run 90272 for RNDM stream (left), CosmicMuons stream(center), and L1Calo stream (right).

Tower R=0.7
Tower R=0.4

1604
10
140

Topo R=0.7
Topo R=0.4

3
120
10
100

9
1
8

Tower R=0.7
Tower R=0.4

7
10−1
6

Topo R=0.7
Topo R=0.4−2590272 RNDM

60
10
40

4

6

8 10 12 14 16 18 20 22# of Jets91
8

Tower R=0.7
Tower R=0.4

7
10−1
6

Topo R=0.7
Topo R=0.4

10

4
10 3

4
−3
10 3

2
10−4
1

2
10−4
1

90272 C.M.

−3

−5

2

10

−2
5

10

2
10
80

20
1
0

10

Event Rate [Hz]Event Rate [Hz]×10

Event Rate [Hz]

3

200
5
10
180

10 00

90272 L1Calo

−5

2

4

6

8 10 12 14 16 18 20 22# of Jets

10 00

2

4

6

8 10 12 14 16 18 20 22# of Jets

Figure 8.17: Number of reconstructed cone-algorithm jets (ET > 20 GeV) for various
cluster input and cone size, in run 90272 for RNDM stream (left), CosmicMuons stream(center), and L1Calo stream (right).

Table 8.3: Triggered event rate for numbers of TopoJets per event (cone size 0.4 andET > 20 GeV) in run 90272.
Number of Jets L1Calo stream [Hz] CosmicMuons stream [Hz]05.146 ± 0.009
0.109 ± 0.001
10.909 ± 0.004(7 ± 1) × 10−42
(1.23 ± 0.05) × 10−2
(2 ± 2) × 10−5≥3
(1.1 ± 0.1) × 10−4
(9 ± 4) × 10−5

151

9
1
8

Tower R=0.7
Tower R=0.4

7
10−1
6

Topo R=0.7
Topo R=0.4

Inclusive Rate of Jets [Hz]Inclusive Rate of Jets [Hz]

10

10−2
5
4

90272 C.M.

−3

10

3

2
10−4
1
−5
10 0
0

50

100

150

200
250
Jet ET [GeV]

10
9
1
8

Tower R=0.7
Tower R=0.4

7
10−1
6

Topo R=0.7
Topo R=0.4

10−2
5
4

90272 L1Calo

−3

10

3

2
10−4
1
−5
10 0
0

500

1000

1500

2000
2500
Jet ET [GeV]

5

3

4.5
2

4

# of Jets

Jet φ

Figure 8.18: Transverse energy of reconstructed jets in post-M8 run 90272 for three
diﬀerent trigger streams. Jets with ET >7 GeV are shown.

3.5

1

3
0

2.5
2

−1

1.5
1

−2

0.5
−3

−4

−2

0

2

4
Jet η

0

Figure 8.19: Reconstructed jets’ η-φ distribution for post-M8 run 90272 CosmicMuons
stream, showing TowerJets with cone size 0.4 and ET >7 GeV (left), TopoJets with cone
size 0.4 and ET >7 GeV (right).

152

4000
2

3500

# of Jets

Jet φ

3

3000

1

2500
0

2000

−1

1500
1000

−2

500
−3

−4

−2

0

2

4
Jet η

0

Figure 8.20: Reconstructed jets’ η-φ distribution for post-M8 run 90272 L1Calo stream,
showing TowerJets with cone size 0.4 and ET >7 GeV (left), and TopoJets with cone size
0.4 and ET >7 GeV (right).

153

3
5 ×10

Number of Events

100
10

90
4
10
80

Cosmic Data L1Calo
Cosmic Single Muon MC

70
103
60
2
50
10

Cone4Tower

40
10
30
20
1
10
10−10

500

1000

1500

2000
2500
Jet ET (GeV)

3
5 ×10

Number of Events

100
10

90
4
10
80

Cosmic Data L1Calo
Cosmic Single Muon MC

70
103
60
2
50
10

Cone4Topo

40
10
30
20
1
10
10−10

500

1000

1500

2000
2500
Jet ET (GeV)

Figure 8.21: Comparison of Jet distributions in run 90272 L1Calo stream with cosmic
Monte Carlo samples for TowerJet (top) and TopoJet (bottom). The normalization factor
in Figure 8.9 was used.
154

Number of Brems Photons/Jets

50000
45000
104
40000
103
35000

Muon Brems

30000
102
25000
10
20000

Cone4TopoJet

15000
1
10000
10−1
5000
10−20

500

1000

1500
2000
2500
3000
Muon Brems/Jet Energy [GeV]

Figure 8.22: Comparison of energy distributions of bremsstrahlung photons from cosmic
muons and reconstructed “fake” jets in cosmic Monte Carlo samples.
Figure 8.22 compares the energy distributions of “fake jets” reconstructed from calorimeter clusters and the bremsstrahlung photons from the muons in the cosmic Monte Carlosimulation. Using the Monte Carlo’s generation proﬁle, photons emitted from the cosmic
muons within the calorimeter volume were considered in the ﬁgure. Through this comparison, it was conﬁrmed that the “fake jets” mostly originates from hard bremsstrahlung
photons from the cosmic muons, since the energy distributions of the bremsstrahlung
photons and the fakely reconstructed jets are very well consistent including the normalization. The small discrepancy in the lower energy region is due to the feature of jet
reconstruction, such as the splitting or merging of the reconstructed objects.

8.4.3

Missing Transverse Energy Reconstruction Using Cosmic
Ray Data

As mentioned in the previous chapter, ETmiss is a variable which indicates the magnitudeof energy imbalance in the plane perpendicular to the beam axis. In order to make use ofthis variable for physics measurements, we must control any sources that lead to a “fake”
ETmiss .
In previous notes [119], we found that electronic failures in the calorimeter front-end
electronic can easily generate tens of GeV of ETmiss and that cosmic ray air showers andcosmic ray muons undergoing hard bremsstrahlung can generate hundreds of GeV of155

ETmiss . Figure 8.23 shows ETmiss distributions from run 90272 L1Calo stream with the
MET Base and MET Topo algorithm. Cosmic Monte Carlo samples are normalized tothe data with the same normalization factor mentioned before. Only events with a jet(ET > 20 GeV) were included in the ﬁgures in order to consider the trigger eﬀects inthe simulation as well. Good agreement between data and Monte Carlo was observed.
Figure 8.24 shows a 2-dimensional plot of Sum ET versus ETmiss from an M8 run [96].
Since we are looking at cosmic events (and some contribution from noisy channels), there
is a strong correlation between Sum ET and ETmiss . This indicates that the high energy
deposits from cosmics and/or unmasked noisy channels in the calorimeters are responsible
for the large ETmiss values, as expected.
These sources of fake ETmiss can be a serious background to Standard Model measurements and in searches for new physics. Initial cleaning cuts to remove such sources offake ETmiss , such as those based on calorimeter timing [122]-[128], were studied, together
with cuts on the calorimeter electromagnetic fraction, Fem in the previous note [119].
Investigations on these cleaning cuts will be described in detail in Section 9.1.3.

156

3
5 ×10

Number of Events

100
10

90
4
10
80

Cosmic Data L1Calo
Cosmic Single Muon MC

70
103
60
50
102
40

MET_Base

30
10
20
1
10
0

500

1000

1500

2000
2500
Missing E (GeV)
T

3
5 ×10

Number of Events

100
10

90
4
10
80

Cosmic Data L1Calo
Cosmic Single Muon MC

70
103
60
50
102
40

MET_Topo

30
10
20
1
10
0

500

1000

1500

2000
2500
Missing E (GeV)
T

Figure 8.23: ETmiss from MET Base algorithm (top) and MET Topo algorithm (bottom) in
post-M8 run 90272 triggered by the L1Calo. Cosmic Monte Carlo samples are normalized
to data using the same factor as in Figure 8.9.
157

Missing ET (GeV)

300

250

200

150

100

50

50

100

150

200

250
300
SET (GeV)

Figure 8.24: Sum ET vs ETmiss as measured in M8 run 77585.

158

Chapter 9
Quality of Calorimeter Objects
As described in Chapter 7 and 8, detector failures, cosmic rays and other non-collision
backgrounds can originate high-energy in the calorimeters, and deteriorate the energy
measurement during the collision data taking. Such contributions will be obstacles tophysics analyses using the calorimeters. Thus, it is of great importance that we develop
strategies to identify and remove such eﬀects from the non-collision background. Here,the quality of reconstructed calorimeter objects was proposed at various stages of event
reconstruction; from the channel-level, cluster-level, and to the jet and event-level.
The most ideal strategy is to identify the channels aﬀected by non-collision sources,
and recover the correct energy. However, such strategy is not always possible due to thediﬃculty in identifying the origin of the background, and also in recovering the correctenergy especially in case of overlaps of cosmic muons or beam halos. The oﬄine rejectionof non-collision background by applying cuts on quality variables of calorimeter objects isoften called “cleaning.” This is rather a fall back solution to discard the physics objects oreven events which seem to be contaminated with non-collision background. This strategy
was highly used in the Tevatron experiments, and played a very important role especiallyin the new physics searches using jets and ETmiss .
The rejection power and the eﬃciency of those cleaning cuts were investigated inthis chapter. Note that choice of quality cuts and their optimal thresholds are analysisdependent. This chapter is dedicated to show the possible cleaning cuts to be used invarious analyses, and is not intended to ﬁx the strategies. An adoption of these cleaning
cuts for monojet search is discussed in Chapter 11.
The ﬁrst half of the chapter is dedicated to the investigations with pure cosmic events.
However, the rejection power and signal eﬃciency of those cleaning cuts can change
when the energy deposits from cosmic or beam halo muons overlap with collision events(e.g. Minimum Bias, QCD di-jet), and the rate of such overlap is dependent on theluminosity. The software algorithm called the “Event Overlay,” which allows to combine
two diﬀerent events (in this case, a cosmic ray event and Monte Carlo event) and run
the reconstruction, was used for the study. The latter half of the chapter shows theperformance of the cleaning cuts under such “overlaid” conditions.

159

9.1

Quality Variables and Cleaning Cuts

In order to discriminate the signal and background, we searched for useful variables
which successfully tag and reject the background and still keep the signals with higheﬃciency. Background rejection can be performed at the various stages of reconstructionof energy deposits in the calorimeters, and the variables to be used for cleaning cuts canbe categorized as the following,• Channel-level qualities: identiﬁcation of problematic channels, quality factor of thepulse shape ﬁt from calorimeter channels.
• Cluster-level qualities: energy fraction of the cell with the highest energy (or 2highest energy cells) inside Topoclusters, timing information from Topoclusters,other geometrical features.
• Jet-level qualities: electromagnetic (EM) energy fraction, number of associatedtracks, number of clusters and cells in jets.
• Event-level qualities (used for “ETmiss cleaning”): timing information, jets’ ET
weighted-average of EM fraction, numbers of tracks, clusters, or cells.
Here, we concentrate on jet, and event-level quality, which are crucially important for
new physics searches using jets and ETmiss (e.g. SUSY, extra dimensions). We havedeveloped series of “data cleaning cuts,” which can be performed at various stages of
event reconstruction and analysis. Note that the validity of cleaning cuts highly depends
on the physics analyses as previously mentioned and their event signatures, and thus someof the cleaning cuts can be powerful tools for some analyses but can be inappropriate forothers (will be mentioned again in the following sections).

9.1.1

Jet-level quality

Here, some quality cuts which can be useful for “cleaning” fake jets, will be mentioned.
Jet Electromagnetic Fraction
The electromagnetic (EM) fraction is an example of a powerful cleaning cut, and has
been used in the previous experiments at the Tevatron. The EM fraction can be deﬁned
as the ratio of the energy deposited in the EM calorimeters to the total energy. The ﬁrst
attempt of using the EM fraction for removing cosmic ray backgrounds in ATLAS canbe found in [119], in which the EM fraction was calculated not within a single jet, but
by adding all the cell energies above a given threshold (the calorimeter EM fraction).
Here, we consider the EM fraction in jets. The jet EM fraction is calculated for eachas:

Jet EM Fraction =(Sum of Energy Deposit in jet in EMB, EMEC)(for jet |η| < 3.2)
(Sum of Energy Deposit in jet for all layers)
(9.1)
160

Table 9.1: Deﬁnition of QCD dijet Monte Carlo samples at the center-of-mass energy of
10 TeV.
Sample Leading parton pT [GeV]xsec
J1
17 - 35
0.859 mb
J2
35 - 70
56.15 μbJ370 - 140
3.24 μbJ4140 - 280
150.9 nb
J5
280 - 560
5.18 nb
J6
560 - 1120
0.11 nb
If we also consider forward jets, the deﬁnition should be extended using the ﬁrst (electromagnetic) sampling layer of the Forward Calorimeter (FCAL):

Jet EM Fraction =(Sum of Energy Deposit in jet in EMB, EMEC, FCAL0)(Sum of Energy Deposit in jet for all layers)(9.2)

For fake jets arising from cosmic ray muons undergoing a hard bremsstrahlung, we expectthat the EM fraction will be concentrated around zero or one. This is due to the factthat the energy deposit from bremsstrahlung is highly concentrated in a small region,and thus only in the EM or hadronic calorimeter. For jets originating from electronicnoise, we expect that most of the jet energy is isolated in a particular channel or groupof nearby channels. In this case, the EM fraction should also be concentrated aroundaround zero or one, depending on whether those channels were in the EM or hadronic
calorimeters.
The jet EM fraction from run 90272 is shown in Figure 9.1 for both Cone4H1TowerJets(jets reconstructed by the ATLAS cone algorithm with the cone size of 0.4 and the
combined towers as inputs) and Cone4H1TopoJets (jets from the ATLAS cone algorithmwith the cone size 0.4 and the Topoclusters as inputs). The jet EM fraction from cosmic
L1Calo stream, cosmic Monte Carlo and the QCD dijet Monte Carlo samples are shownin the ﬁgure. The details of cosmic Monte Carlo samples were already mentioned in
Chapter 8. The QCD dijet Monte Carlo samples are produced in slices considering theleading parton transverse momentum (Table 9.1), to cover adequate statistics even in thehigh energy range as well. They are called “JX” (X=0∼8) samples, where X denotes the“Xth slice” of the QCD dijet samples. J1 to J6 samples are considered in the ﬁgure. Jets
with transverse energy larger than 20 GeV are shown, and the energy is at the EM scale.
The jet EM fraction from the L1Calo streams is mostly concentrated around 0 or 1
as expected, and they are consistent with the cosmic Monte Carlo simulation. QCD dijet
has a large EM fraction value with the peak around 0.8, and thus a clear separation isseen between fake jets from cosmics and the “real” QCD jets.
Note that the negative EM fraction and EM fraction larger than 1 originate fromnoise with negative energy. Cone4H1TowerJets tend to have larger tails aside the peaks
due to the fact that combined towers are more likely to include noise contributions inside
jets, compared to Cone4H1Topoclusters. Slight discrepancy between the cosmic data andMonte Carlo for Cone4H1TowerJets (the left plot of Figure 9.1) is due to the electronic161

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC

7
10−1
6
5
10−2
4
10−3
3
2
10−4
1
10−5
0
−0.5

0

0.5

1

1.5
Jet EM Fraction

Normalized Number of Jets

Normalized Number of Jets

9
1
8

9
1
8

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC

7
10−1
6
5
10−2
4
10−3
3
2
10−4
1
10−5
0

−0.2

0

0.2

0.4

0.6

0.8

1
1.2
Jet EM Fraction

Figure 9.1: Jet EM fraction from Tower jets with cone size 0.4 (left) and Topocluster jetswith cone size 0.4 (right) in post-M8 run 90272 and QCD di-jet Monte Carlo samples.
Only jets with ET > 20 GeV are shown here.
noise values (especially TileCal) higher in the data than the Monte Carlo. Apart fromthe eﬀect, the consistency of data and Monte Carlo is very good.
In Figure 9.2, the jet EM fractions are shown for various calorimeter regions. As seenfrom the plots, the jet EM fraction is eﬀective in all the calorimeter regions including theforward region.
The correlation plots of jet EM fraction and jets’ transverse energy from both TowerJets and TopoJets are shown in Figure 9.2 for cosmic L1Calo stream and cosmic Monte
Carlo. As seen from the plots, most fake jets are concentrated in low ET region with EM
fraction around 0. Note that high ET jets tend to have better concentration on jet EM
fraction 0 or 1, which conﬁrms the validity of the cleaning cuts.
Table 9.2 and 9.3 shows the breakdown of jets remaining after a series of cuts on the
jet EM fraction (thus shows the rejection power and eﬃciency). The low EM fraction
cut deﬁnitely removes most of the fake jets coming from the noise or cosmics, and still
provides good eﬃciency for the “real” QCD jets. A cut on EM fraction 0.2 removes morethan 90% of the reconstructed jets (with ET >20 GeV) in cosmic runs, but eﬃciency
for the QCD jets remains to be more than 99%. On top of that, a cut on the upper
EM fraction also removes additional background [121]. However, an upper EM fraction
cut also tends to lower the eﬃciency of the real QCD jets unlike a lower EM fraction
cut. Electrons identiﬁed as jets can also be removed by the upper EM fraction cut, sosome cares must be taken in case of adopting the upper EM fraction cut. Fake jets with
mid-EM fraction values are something that cannot be rejected by the EM fraction cut.
Those jets originate when a cosmic muon radiates a bremsstrahlung around the boundary
region of the EM and hadronic calorimeter; a cosmic muon forms multiple clusters fromhadrons originated from muon’s photonuclear interaction; or when there are signiﬁcantamounts of energy over the whole calorimeter due to air showers (Figure 9.4). For fakejets with mid-EM fraction, other complementary quality cuts will be applied, which are
mentioned in the following sections.

162

Table 9.2: The breakdown of the ratio of jets remaining after diﬀerent cut values on the
EM fraction from run 90272 for Cone4H1TowerJets (ET > 20 GeV). Bad channels are
not masked in RNDM stream events. Only statistical errors were considered.
Cut> 0.05> 0.10> 0.15> 0.20> 0.25> 0.30< 0.85< 0.90< 0.95< 1.0

L1Calo stream
16.6 ± 0.2%
8.7 ± 0.1%
7.4 ± 0.1%
7.1 ± 0.1%
6.9 ± 0.1%
6.8 ± 0.1%
94.2 ± 0.1%
94.5 ± 0.1%
95.21 ± 0.09%
98.15 ± 0.06%

J3
99.99 ± 2e-3%
99.96 ± 4e-3%
99.89 ± 7e-3%
99.75 ± 0.01%
99.51 ± 0.02%
99.13 ± 0.02%
65.58 ± 0.01%
78.56 ± 0.09%
89.31 ± 0.07%
97.07 ± 0.04%

J6
99.99 ± 3e-3%
99.95 ± 6e-3%
99.87 ± 9e-3%
99.71 ± 0.01%
99.40 ± 0.02%
98.90 ± 0.03%
78.57 ± 0.1%
87.75 ± 0.08%
94.22 ± 0.06%
98.47 ± 0.03%

Table 9.3: The breakdown of the ratio of jets remaining after diﬀerent cut values on the
EM fraction from run 90272 for Cone4H1TopoJets (ET > 20 GeV). Bad channels are notmasked in RNDM stream events. Only statistical errors were considered.
EMF Cut> 0.05> 0.10> 0.15> 0.20> 0.25> 0.30< 0.85< 0.90< 0.95< 1.0

L1Calo stream
8.2 ± 0.1%
7.7 ± 0.1%
7.5 ± 0.1%
7.4 ± 0.1%
7.3 ± 0.1%
7.2 ± 0.1%
93.9 ± 0.1%
94.2 ± 0.1%
94.96 ± 0.09%
98.21 ± 0.06%

J3
99.98 ± 3e-3%
99.95 ± 5e-3%
99.86 ± 8e-3%
99.69 ± 0.01%
99.41 ± 0.02%
98.98 ± 0.02%
68.6 ± 0.1%
80.98 ± 0.08%
90.97 ± 0.06%
98.70 ± 0.02%

163

J6
99.98 ± 3e-3%
99.94 ± 7e-3%
99.85 ± 0.01%
99.68 ± 0.01%
99.34 ± 0.02%
98.81 ± 0.03%
79.6 ± 0.1%
88.50 ± 0.08%
94.66 ± 0.06%
99.30 ± 0.02%

4
10−3
3

0.5

9
1
8

1

1.5
Jet EM Fraction

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC710−1 End−Cap
6 TowerJets
5
10−2
4
10−3
3
2
10−4
1
10−5
0
−0.5

0

0.5

9
1
8

1

7
10−1 Forward
6 TowerJets
5
10−2
4
10−3
3
2
10−4
1
10−5
0
−0.5

0

0.5

1

4
10−3
3

−0.2

0

0.2

0.4

9
1
8

0.6

0.8

1
1.2
Jet EM Fraction

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC710−1 End−Cap
6 TopoJets
5
10−2
4
10−3
3

−0.2

0

0.2

0.4

9
1
8

0.6

0.8

1
1.2
Jet EM Fraction

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC710−1 Forward
6 TopoJets
5
10−2
4
10−3
3
2
10−4
1
10−5
0

1.5
Jet EM Fraction

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC710−1 Barrel
6 TopoJets
5
10−2

2
10−4
1
10−5
0

1.5
Jet EM Fraction

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC

9
1
8

2
10−4
1
10−5
0

Normalized Number of Jets

Normalized Number of Jets0

Normalized Number of Jets7
10−1 Barrel
6 TowerJets
5
10−2

2
10−4
1
10−5
0
−0.5

Normalized Number of Jets

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC

Normalized Number of Jets

Normalized Number of Jets

9
1
8

−0.2

0

0.2

0.4

0.6

0.8

1
1.2
Jet EM Fraction

Figure 9.2: Jet EM fraction from Tower jets with cone size 0.4 (left) and Topocluster jetswith cone size 0.4 (right) in post-M8 run 90272 and QCD di-jet Monte Carlo samples foreach calorimeter region. Only jets with ET > 20 GeV are shown here.

164

4000

14000
12000
10000
6000

−0.5

0

4000

1.5

3500
3000

1

2500
0.5

2000

102

2000
3

10
Jet Et [GeV]

1.5

0

12000
10000

1

8000
0.5

6000

1500
4000
0

1000
Cosmic MC
TowerJets

−0.5

102

0

3

10
Jet Et [GeV]2000

Cosmic MC
TopoJets

500
−0.5

0

102

3

10
Jet Et [GeV]0

Figure 9.3: Correlation plots of transverse energy of jets and jet EM fraction for TowerJets
with cone size 0.4 (left) and TopoJets with cone size 0.4 (right) in post-M8 run 90272
and cosmic Monte Carlo samples. Only jets with ET > 20 GeV are shown here.

165# of Jets

3

4000

90272 L1Calo
TopoJets

1000

10
Jet Et [GeV]# of Jets

16000
1

0

Jet EM Fraction

102

18000

8000

2000
90272 L1Calo
TowerJets

20000

1.5

0.5

3000

0

−0.5

Jet EM Fraction

5000

1

0.5

Jet EM Fraction# of Jets

6000# of Jets

Jet EM Fraction

1.5

Figure 9.4: Examples of events with mid-EM fraction. The upper ﬁgure shows a jet
originated from a single muon, and the lower ﬁgure shows an air shower event.

166

Number of Associated Tracks in Jets
The number of Inner Detector tracks matching to jets also provides useful information
to discriminate the QCD and fake jets. QCD jets tend to have many tracks associatedwith them, due to the fact that numbers of charged hadrons are inside the jets. On theother hand, fake jets generated from noise or problematic channels will not have tracks
associated to them except for accidental association of unrelated tracks. Jets produced
from a single cosmic muons should contain only one track, or may not have any track
associated to them when the cosmic muons are non-projective (do not pass near theinteraction point). Jets generated from air shower events may have more tracks inside
jets, but even those jets have far less associated tracks compared to the QCD jets.
Here, the Inner Detector tracks (called the “TrackParticleCandidate” in the ATLAS
analysis framework) were considered for the track association. In cosmic run data, theInner Detector tracks were not assumed to be coming from the interaction point, but
as passing downward through the detector, and thus were reconstructed using the whole
inner detector hits (a single track passes through the top and bottom module of thedetector; see Figure 9.5). All the φ values in the cosmic tracks are set as negative, since
they are going downward. In this study, the motivation is to understand the eﬀects ofcosmic tracks during the collision data taking, so the track reconstruction was re-done in
the cosmic data with the same criteria as those during the collision data taking. In such
criteria, two tracks can originate from a single muon, each track reconstructed from theinteraction point, one track going upward and the other traversing downward.
The number of reconstructed Inner Detector tracks with the collision settings areshown in Figure 9.6. The upper left plot shows the number from cosmic run 90272
IDCosmic stream, and the upper right plot shows the one from the L1Calo stream of thesame run. The lower plot is from the QCD dijet Monte Carlo. The number of tracks
are shown for the default reconstruction, and after applying some cuts on the number ofhits in the silicon detectors. Most tracks only have TRT hits, and they do not have η
information or do not have reliable η values due to the detector feature (straws runningparallel to the beam direction). Such tracks cannot be used for jet association. In orderto have reliable η − φ information, we required one hit in the Pixel B-layer (which is theinnermost layer, closest to the interaction point) and at least ﬁve hits in the whole silicon.
Applying the cuts, almost no tracks are reconstructed in the cosmic data. The L1Calostream has far less number of tracks than the IDCosmic stream, because many cosmic
muons triggered by the Level-1 Calorimeter trigger do not pass near the interaction point,whereas the IDCosmic stream tend to have more tracks since the Level-2 Inner Detector
trigger is used.
When tracks were within the cone size of jets (0.4) in regards to dR = Δη 2 + Δφ2 ,they were regarded as associated to jets.
In Figure 9.7, number of tracks in jets are shown for both TowerJets and TopoJets
with cone size 0.4. Clear separation of fake jets from cosmic events and QCD jets areobserved. Table 9.4 shows breakdowns of the ratio of jets remaining after cuts withnumber of associated tracks. As seen from there, even a requirement of single associatedtrack removes most of the fake jets with good eﬃciency on QCD jets.
Although, cleaning cuts using the number of tracks seem to show powerful fake re167

Figure 9.5: Inner Detector event display from cosmic run 90270 with solenoidal magnetic
ﬁeld on. The red line shows the reconstructed Inner Detector track and points show thehits in the detector [106].
jection power, the performance is deteriorated when we start to consider cosmic muons
overlapping high cross section collision events such as the Minimum Bias or the QCD
dijets. Under such circumstances, even the fake jets tend to have larger number of associated tracks which is due to the accidental overlap of tracks coming from the collision
events. In such cases, additional information from tracks, such as “charge fraction” ofjets can be useful to remove backgrounds. It is deﬁned astracks
Charge Fraction =(i)PT
Jet ETi(9.3)

and was used in Tevatron experiments. The performance of this variable under cosmiccollision overlap condition will be mentioned in Section 9.2.
Number of Clusters in Jets
Number of calorimeter clusters in jets is expected to have discrimination power on realand fake jets as well. This is called “jet size” in the ATLAS software. For TowerJets, jet
size is the number of combined towers inside the jets, whereas the number of Topoclusters
inside the jets corresponds to the jet size for TopoJets.

168

3

3

×10

200
5
10
180

Number of Events

Number of Events

200
5
10
180

IDCosmic

1604
10
140

802
10
60

10
40

10
40

20
1
0

20
1
0

20

30

Default
Silicon Hits ≥ 1
B−Layer Hits≥ 1, S.Hits ≥ 5

1203
10
100

802
10
60

10

L1Calo

1604
10
140

Default
Silicon Hits ≥ 1
B−Layer Hits≥ 1, S.Hits ≥ 5

1203
10
100

×10

40
50
60
70
Number of Tracks

5

10

15
20
25
Number of Tracks

3

Number of Events

200
5
10
180

×10

Default
Silicon Hits ≥ 1
B−Layer Hits≥ 1, S.Hits ≥ 5

1604
10
140

QCD

1203
10
100
802
10
60
10
40
20
1
0

50

100

150
200
250
Number of Tracks

Figure 9.6: Number of Tracks in cosmic data and QCD dijet Monte Carlo. The upper
left plot shows the number from cosmic run 90272 IDCosmic stream, and the upper rightplot shows the one from the L1Calo stream of the same run. The lower plot is from the
QCD dijet Monte Carlo.

Table 9.4: The breakdown of the ratio of jets remaining after diﬀerent cut values on thenumber of associated tracks in jets from post-M8 run 90272 (for Cone4H1TopoJets witha cone size of 0.4and ET > 20 GeV, |η| < 2.5). Only statistical errors were considered.
Cut
≥1
≥2
≥3
≥4
≥5

L1Calo stream
IDCosmic
0± — %
0.15 ± 0.06%
0± — %
0± — %
0± — %
0± — %
0± — %
0± — %
0± — %
0± — %

169

J3
99.67 ± 0.02%
98.37 ± 0.04%
95.02 ± 0.06%
89.00 ± 0.09%
80.4 ± 0.1%

Fraction of Jets

2
1
1.8

Cosmic IDCosmic
Cosmic L1Calo
QCD Di−jet (J3)

−1
1.6
10

1.4
10−2
1.2
10−31
0.8
10−4
0.6

−5
0.4
10

0.2
10−6
0
0

10−150

100
1

15010
200 102 250
Number of Tracks in Jets

Figure 9.7: Numbers of tracks associated with Cone4H1TopoJets with cone size 0.4 inrun 90272, cosmic and QCD di-jet Monte Carlo samples. Only jets with ET > 20 GeV
and |η| < 2.5 are shown here.
Figure 9.8 shows the number of clusters for Cone4H1TopoJets. The number of clusters
for TopoJets seems to be useful for jet cleaning. This is due to the fact that jets originatedfrom noisy channels or cosmic muons tend to have only a few numbers of Topoclusters.
On the other hand, Topoclusters have a good correlation with the number of particles in
QCD jets, which leads to a clear separation in jet size. About 1 Topocluster corresponds
to 1.6 MC Truth stable particles passing the calorimeter [23]. (e.g. π 0 ’s are not counted,
but the two photons from their decays are counted)
We need to investigate the eﬀect of calorimeter geometry on this quantity as well, since
the cluster reconstruction is severely aﬀected by calorimeter geometry such as the granularity. Figure 9.9 shows the distributions of the number of clusters in Cone4H1TopoJets
in various calorimeter regions, such as the barrel (|η| < 1.5), end-cap(1.5 < |η| < 3.2).
Good separation between fake and real jets are observed in Cone4H1TopoJets for thebarrel and the end-cap. Here, we investigate the breakdown of TopoJets within |η| < 3.2,
as shown in Table 9.5. A cut on the number of clusters in TopoJets removes most of the
fake jets, and still maintains good eﬃciency on the QCD jets. However, like other cleaning cuts, the performance needs to be conﬁrmed under overlapping condition of cosmics
and collision events, which will be mentioned in Section 9.2.
Number of Cells in Jets
Number of cells inside Topoclusters or jets can also provide useful information to perform a clean-up. Jets formed from noisy channels or cosmic ray muons undergoing hardbremsstrahlung tend to have low number of cells, whereas QCD jets have a large number of cells inside jets due to contributions from the large number of particles [94]. In
170

Table 9.5: The ratio of jets remaining after diﬀerent cut values on the jet size from postM8 run 90272 (for Cone4H1TopoJets with a cone size of 0.4 and ET > 20 GeV, |η| < 3.2).
Only statistical errors were considered.
Cut L1Calo stream>2
28.1 ± 0.2%
>4
3.23 ± 0.08%>6
0.59 ± 0.03%>8
0.20 ± 0.02%> 10 0.07 ± 0.01%> 15 0.01 ± 5e-3%J399.64 ± 0.01%
97.61 ± 0.03%
92.60 ± 0.04%
84.49 ± 0.08%
73.03 ± 0.1%
36.17 ± 0.1%

J6
99.82 ± 0.01%
98.77 ± 0.03%
95.66 ± 0.05%
89.32 ± 0.08%
78.97 ± 0.1%
42.17 ± 0.1%

Table 9.6: Mean number (RMS in parenthesis) of cells in jets (ET > 20 GeV) in di-jet
Monte Carlo samples
Type of Jets
J1
J3
TopoJets (R = 0.4) 723 ± 1(362.0 ± 0.8) 1202 ± 1(559.8 ± 0.9)

J61930 ± 2(905 ± 2)

Table 9.7: The breakdown of the ratio of jets remaining after diﬀerent cut values on thenumber of cells in jets from post-M8 run 90272 (for TopoJets with a cone size of 0.4,
ET > 20 GeV, and |η| <3.2).
Cut L1Calo stream
J3
J6
> 50
91.2 ± 0.1%
99.999 ± 7e-4% 99.999 ± 7e-4%> 10080.0 ± 0.2%
99.98 ± 3e-3% 99.99 ± 3e-3%> 150
63.2 ± 0.2%
99.87 ± 8e-3% 99.95 ± 6e-3%> 200
47.4 ± 0.2%
99.52 ± 0.02% 99.79 ± 0.01%> 300
25.5 ± 0.2%
97.40 ± 0.04% 98.95 ± 0.03%> 400
13.1 ± 0.1%
94.56 ± 0.05% 97.94 ± 0.04%> 500
6.5 ± 0.1%
92.14 ± 0.06% 97.024 ± 0.04%> 600 3.38 ± 0.08%
89.58 ± 0.07% 95.82 ± 0.05%> 700 1.93 ± 0.06%
86.15 ± 0.08% 94.06 ± 0.06%

171Normalized Number of Jets

2
1
1.8

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC

1.6
10−1
1.4
1.2
10−2
1
−3
0.8
10
0.6
−4
0.4
10

0.2
10−5
0
0

5

10

15 20 25 30 35 40 45 50
Number of Calorimeter Clusters in Jets

Figure 9.8: Number of clusters in Cone4H1TopoJets (jet with cone size 0.4 and Topoclusters as inputs) from run 90272 L1Calo stream and QCD di-jet Monte Carlo samples. Only
jets with ET > 20 GeV are shown here.
Figure 9.10, a diﬀerence in the number of cells inside TopoJets is observed between jets
reconstructed from cosmic ray events and those from di-jet Monte Carlo samples. Good
separation is seen in TopoJets between reconstructed jets from cosmic events and QCD
jets. However, large numbers of cells in jets were observed in a few cosmic ray events.
It was conﬁrmed from Figure 9.11 in Section 9.1.1 that these were mainly coming fromLAr EM calorimeter. These can be due to sporadic noise bursts in the calorimeter.
The number of cells inside TowerJets seems to have little discrimination power, regardless of the calorimeter region, which may be due to the similar reason mentioned
for the jet size. As shown in Table 9.7, for real jets, higher the jet energy, the large thenumber of cells reconstructed inside jets (also mentioned in [94]). The breakdown of ratioof jets remaining after various cuts on the number of cells is shown in Table 9.7.
Correlation of Jet Quality
As mentioned in the previous sections, several quality cuts are proposed. We expect thateach of them perform a removal of fake contributions in complementary ways. Here, welook at the correlation of quality variables. In Figure 9.11, it is obvious that most jets
are concentrated around jet EM fraction 0 and small number of tracks, clusters, andcells. Note that most jets with mid-EM fraction also tend to have small numbers of
tracks and clusters. This shows that these quality cuts are performing complementary“cleaning.” Jets with large tremendous numbers of cells in run 90272 L1Calo stream
seems to generate in LAr EM region, as Jet EM fraction shows. However, even for thosejets, numbers of tracks and clusters remain small, so these two variables may perform172

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC

1.6
10−1
1.4
1.2
10−2
1

BarrelTopoJets

−3
0.8
10
0.6

Normalized Number of Jets

Normalized Number of Jets

2
1
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2
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1

−4
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10

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0
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0
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15 20 25 30 35 40 45 50
Number of Calorimeter Clusters in Jets

End−Cap
TopoJets

−3
0.8
10
0.6

−4
0.4
10

5

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC

1.6
10−1
1.4

5

10

15 20 25 30 35 40 45 50
Number of Calorimeter Clusters in Jets

Normalized Number of Jets

Figure 9.9: Number of clusters in Cone4H1TopoJets in run 90272 L1Calo stream and
QCD di-jet Monte Carlo samples in the barrel (upper ﬁgure) and the end-cap (lower
ﬁgure). Only jets with ET > 20 GeV are shown here.

2
1
1.8

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC

1.6
10−1
1.4
1.2
10−2
1
−3
0.8
10
0.6
−4
0.4
10

0.2
10−5
0
0

500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Number of Calorimeter Cells in Jets

Figure 9.10: Number of cells in Cone4H1TopoJets from run 90272 L1Calo stream and
QCD di-jet Monte Carlo samples. Only jets with ET > 20 GeV are shown here.

173

4000

QCD MCTopoJets

3500

60

12000

50

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40

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Jet EM Fraction

4000

1000

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500

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# of Cells

5000
4500
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8000

90272 L1Calo
TopoJets

1500

20

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# of Jets

10

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Jet EM Fraction

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5000

# of Cells

20

40

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QCD MCTopoJets

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1000

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Jet EM Fraction0

0# of Cells

5000
4500

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90272 L1Calo
TopoJets

3000# of Jets

0

2500

2000

500
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40

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Jet Size

0

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QCD MCTopoJets

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Jet EM Fraction

10000

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0
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Jet Size

0Figure 9.11: Correlation of jet quality variables for post-M8 run 90272 L1Calo stream
and QCD Monte Carlo.

174# of Jets

90272 L1Calo
TopoJets

Jet Size70# of Jets

Jet Size80better fake removal than the numbers of cells.
Preliminary look at the overall “cleaning” of jets will be mentioned again in Section 9.1.3.

9.1.2

Event-level Quality

In the previous sections, quality of jets were mentioned. “Event cleaning” is a fake removal
procedure performed at the event level. This is the most stringent cut to be performed
during the analyses, since the event itself is discarded when the contamination from noncollision sources is identiﬁed, rather than only removing the reconstructed objects. Thisapproach is very important and crucial for new physics searches using large ETmiss (andpossibly accompanying jets), and is often called the “ETmiss cleaning.” Many of the eventlevel quality can be provided from jet information (see 9.1.2). Here, we also investigate
on the timing information from TileCal using the time-of-ﬂight.
Event-level Quality from Jet Information
As mentioned above, jet information can provide quality cuts even at the event-level.
Taking the weighted-average by jet transverse energy of the previously mentioned jet
quality will also be a good possibility for event-level quality, and is useful for physics
analyses with multi-jet events. Such a quality using information from jets’ EM fraction
is called the event EM fraction derived as below.

jet EMFjet × ET jet(9.4)
Event EM Fraction =jet ET jet
It is calculated event-by-event to remove backgrounds with large fake ETmiss . This quantity was used in the analyses at Tevatron experiments to remove events with large fakeETmiss . Figure 9.12 shows the event EM fraction calculated from Cone4H1TowerJets andCone4H1TopoJets with ET > 20 GeV. A clear separation is seen between cosmic ray
events and real QCD di-jet events using this variable. However, this quantity can besmeared when the transverse energy of fake jet(s) are comparable or smaller than theother real QCD jets. In such a case, an alternative and more stringent cut is to reject
events when there is at least one jet not fulﬁlling the quality requirement. Which strategy
to take depends on the physics analysis and its motivation.
The same calculation performed for number of tracks, clusters, and cells (replace
EMFjet by these quantities in equation 9.4) may also serve as event-level quantities forthe cleaning as shown in Figure 9.12.

9.1.3

Cleaning for Pure Cosmic Events

Here, we show a result of applying cleaning cuts for jets. Note that the pure cosmic eventswere analyzed in the plot and either contributions from electronic noise or cosmic rays
show up. The performance of cleaning cuts on cosmic ray overlapping collision events
will be mentioned in Section 9.2.

175

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC

7
10−1
6
5
10−2
4
10−3
3
2
10−4
1
10−5
0
−0.5

0

0.5

1
1.5
Event EM Fraction

Normalized Number of Events

Normalized Number of Events

9
1
8

9
1
8

Cosmic Data L1Calo
Cosmic Single Muon MCQCD Dijet MC

7
10−1
6
5
10−2
4
10−3
3
2
10−4
1
10−5
0

−0.2

0

0.2

0.4

0.6

0.8
1
1.2
Event EM Fraction

Figure 9.12: Event quality from jet information. Left side shows quality derived fromTowerJets with cone size 0.4 and right side shows quality calculated from TopoJets withcone size 0.4 in post-M8 run 90272, cosmic Monte Carlo and QCD di-jet Monte Carlosamples. Jets with ET > 20 GeV are used for the calculations.
Figure 9.1.3 shows a result on jet cleaning for post-M8 run 90272 L1Calo stream andcosmic Monte Carlo samples for Cone4H1TopoJets. Quality cuts with EM fraction andnumber of clusters in jets were considered. The actual values used for the selection are
as follows.
• Cone4H1TopoJets with EM fraction < 0.2 or > 0.97 were rejected.
• Cone4H1TopoJets with jet size ≤ 6 were rejected.

9.2

Event Overlay Studies

In the previous section, jet and ETmiss quality were studied with pure cosmic ray events.
It is not obvious whether those cleaning cuts remain to be valid when cosmic ray muons
overlap with physics events with large cross sections (such as Minimum Bias and QCD
di-jet events). Here, so called “Event Overlay” algorithm developed in ATLAS was used
to study such cases. The algorithm has originally been developed for the pile-up studies.

9.2.1

Event Overlay Algorithm

The algorithm allows to read and mix events from various streams at the Geant-4 “Hits”level. The number of events to mix can be set arbitrarily. It can combine not just Monte
Carlo events, but also real events or mixture of several Monte Carlo and real events. Thisalgorithm allows to investigate not just the eﬀects of the pile-up, but also the overlap of
non-collision sources on the signal events.

176

Number of Jets

3
5 ×10

100
10

90
4
10
80

Cosmic Data L1Calo
Cosmic Single Muon MC

70
103
60

Before Cleaning Cuts
After EMF Cut
After EMF + NClus Cut

50
102
40
30
10
20
1
10
0

500

1000

1500
2000
2500
Jet Transverse Energy (GeV)

Figure 9.13: Preliminary cleaning on TowerJets and TopoJets with cone size 0.4 in postM8 run 90272 L1Calo stream and cosmic Monte Carlo samples.

9.2.2

Investigations on Event Overlap of Cosmic Rays on Collision Events

The event overlay was done for the cosmic ray data and the high cross section Monte
Carlo events, such as the Minimum Bias and QCD dijet samples.
Figure 9.14 shows the jet EM fraction from cosmic-Minimum Bias mixed samples andcosmic-J3 mixed samples. The blue lines show what we observed in the pure cosmic data,
whereas the red lines show the EM fraction from fake jets originated from cosmics underoverlaid conditions. The black line show the EM fraction of the QCD jets under overlaid
conditions. The determination of whether the jets originated from cosmics or the real
QCD jets was done using the Monte Carlo generator’s information. The ATLAS framework provides so called the “TruthJets” where the jet algorithm runs among the particles
at the generator’s level with a particular cone size, and oﬀers the “true” jet information
without any detector eﬀects. In the overlaid samples, the generator’s information remains
even after the Monte Carlo events are mixed with real data. Thus, matching between
the reconstructed jets in the mixed data, and “TruthJets” were performed to categorize
them into “fake jets” originated from cosmics, and the real QCD jets coming from thepartons’ fragmentations and hadronizations. The breakdown of the rejection power of177

Cosmic Data L1Calo
Cosmic Jets in MB−Overlay

7
10−1
6
5
10−2
4
10−3
3
2
10−4
1
10−5
0

−0.2

0

0.2

0.4

0.6

0.8

1
1.2
Jet EM Fraction

Normalized Number of Jets

Normalized Number of Jets

9
1
8

9
1
8

Cosmic Data L1Calo
Cosmic Jets in J3−Overlay
QCD Jets in J3−Overlay

7
10−1
6
5
10−2
4
10−3
3
2
10−4
1
10−5
0

−0.2

0

0.2

0.4

0.6

0.8

1
1.2
Jet EM Fraction

Figure 9.14: Jet EM fraction in overlaid samples; cosmic L1Calo on Minimum Bias events(left) and cosmic L1Calo on QCD dijet J3 events (right).
Table 9.8: The breakdown of the ratio of jets remaining after diﬀerent cut values on
the EM fraction for Cone4H1TopoJets (ET > 20 GeV) in pure cosmic L1Calo stream,
overlaid cosmic samples on Minimum Bias and QCD dijet J3 Monte Carlo samples. Only
statistical errors were considered.
EMF Cut> 0.05> 0.10> 0.15> 0.20> 0.25> 0.30< 0.85< 0.90< 0.95< 1.0

L1Calo stream
8.2 ± 0.1%
7.7 ± 0.1%
7.5 ± 0.1%
7.4 ± 0.1%
7.3 ± 0.1%
7.2 ± 0.1%
93.9 ± 0.1%
94.2 ± 0.1%
94.96 ± 0.09%
98.21 ± 0.06%

Cosmic jets on MB
13.6 ± 0.9%9.2 ± 0.7%
8.1 ± 0.7%
7.8 ± 0.7%
7.4 ± 0.7%
7.2 ± 0.7%
93.7 ± 0.6%
94.3 ± 0.6%
95.2 ± 0.6%
98.3 ± 0.3%

Cosmic jets on J3 QCD jets in cosmic-on-J3
54 ± 1%99.98 ± 9e-3%
47 ± 1%99.92 ± 0.02%
45 ± 1%99.82 ± 0.03%
43 ± 1%99.59 ± 0.04%
43 ± 1%99.34 ± 0.05%
42 ± 1%98.9 ± 0.7%
86.2 ± 0.8%
74.4 ± 0.3%
91.0 ± 0.7%
86.5 ± 0.2%
94.6 ± 0.5%
95.2 ± 0.1%
98.9 ± 0.2%
99.78 ± 0.03%

Table 9.9: The breakdown of the ratio of jets remaining after diﬀerent cut values on thenumber of clusters in Cone4H1TopoJets (ET > 20 GeV) in pure cosmic L1Calo stream,
overlaid cosmic samples on Minimum Bias and QCD dijet J3 Monte Carlo samples. Only
statistical errors were considered.
EMF Cut
>2
>4
>6
>8
> 10
> 15

L1Calo stream
28.1 ± 0.2%
3.23 ± 0.08%
0.59 ± 0.03%
0.20 ± 0.02%
0.07 ± 0.01%
0.01 ± 5e-3%

Cosmic jets on MB
53 ± 1%17 ± 1%6.3 ± 0.6%
2.1 ± 0.4%
0.8 ± 0.2%
0.8 ± 0.2%
178

Cosmic jets on J3 QCD jets in cosmic-on-J3
83.2 ± 0.9%
99.76 ± 0.03%
61 ± 1%97.8 ± 0.1%
47 ± 1%92.9 ± 0.2%
38 ± 1%84.7 ± 0.2%
32 ± 1%73.2 ± 0.3%
32 ± 1%73.2 ± 0.3%

Cosmic Data L1Calo
Cosmic Jets in MB−Overlay

1.6
−1
1.4
10

1.2
1
−2

10
0.8

0.6
−3
10
0.4

Normalized Number of Jets

Normalized Number of Jets

2
1
1.8

0.2
−4

10 0

2
1
1.8

Cosmic Data L1Calo
Cosmic Jets in J3−Overlay
QCD Jets in J3−Overlay

1.6
−1
1.4
10

1.2
1
10−2
0.8
0.6
−3
10
0.4
0.2

5

10

15

20

10−40

25 30 35 40 45 50
Number of Clusters in Jets

5

10

15

20

25 30 35 40 45 50
Number of Clusters in Jets

91
8

Cosmic Data L1Calo
Cosmic Jets in MB−Overlay

7
10−1
6
5
10−2
4
10−3
3
2
10−4
1
10−5
0
0

Normalized Number of Jets

Normalized Number of Jets

Figure 9.15: Number of Topoclusters in overlaid samples; cosmic L1Calo on Minimum
Bias events (left) and cosmic L1Calo on QCD dijet J3 events (right).

9
1
8

Cosmic Data L1Calo
Cosmic Jets in MB−Overlay
QCD Jets in MB−Overlay

7
10−1
6
5
10−2
4
10−3
3
2
10−4
1

1

2

3

4

5

6
7
8
9
10
Number of Tracks in Jets

10−5
0
0

5

10

15

20

25
30
35
40
Number of Tracks in Jets

Figure 9.16: Number of associated tracks in jets in overlaid samples; cosmic L1Calo on
Minimum Bias events (left) and cosmic L1Calo on QCD dijet J3 events (right).

Table 9.10: The breakdown of the ratio of jets remaining after diﬀerent cut values on
the number of associated tracks for Cone4H1TopoJets (ET > 20 GeV) in pure cosmic
L1Calo stream, overlaid cosmic samples on Minimum Bias and QCD dijet J3 Monte Carlosamples. Only statistical errors were considered.
EMF Cut
≥1
≥2
≥3
≥4
≥5

L1Calo stream
0± — %
0± — %
0± — %
0± — %
0± — %

Cosmic jets on MB
20.8 ± 0.7%
5.3 ± 0.4%
1.2 ± 0.2%
0.4 ± 0.1%
0.17 ± 0.07%

179

Cosmic jets on J3 QCD jets in cosmic-on-J3
58.2 ± 0.8%
99.12 ± 0.06%
38.0 ± 0.8%
96.3 ± 0.1%
28.4 ± 0.7%
90.2 ± 0.2%
22.4 ± 0.7%
81.4 ± 0.3%
16.5 ± 0.6%
70.6 ± 0.3%

Normalized Number of Jets

2
1
1.8

Cosmic Jets in MB−Overlay
Cosmic Jets in J3−Overlay
QCD Jets in J3−Overlay

1.6
−1
1.4
10

1.2
1
10−2
0.8
0.6
10−3
0.4
0.2
10−4
0

0.5

1

1.5

2
2.5
3
Jet Charge Fraction

Figure 9.17: Jet charge fraction in overlaid samples; jet originated from cosmic muons incosmic L1Calo on Minimum Bias events (red square), cosmic L1Calo on QCD dijet J3
events (red open triangle), and from QCD jets in cosmic L1Calo on QCD dijet J3 events(black).
the fake jets and the eﬃciency of the QCD jets are shown in Table 9.11. The cleaning cutby the EM fraction still remains to be very eﬀective when cosmic muons overlap with asingle Minimum Bias event, rejecting more than 90 % of the fake jets when EM fraction
cut of 0.1 (or tighter) is used. The pile-up of a single Minimum Bias event in averagecorresponds to the luminosity of 5 × 1032 cm−2 s−1 . However, the performance is degradedwhen cosmic muons overlap with more “crowded” events such as the QCD dijet withhigh energy (here, J3 is the example).
Figure 9.15 shows the number of clusters in Cone4H1TopoJets from cosmic-Minimum
Bias mixed samples and cosmic-J3 mixed samples. The color convention is the same as
Figure 9.14. The performance of cleaning cuts is slightly degraded as in the case of thejet EM fraction for cosmic-Minimum Bias mixed events (Table 9.9), but it still removes
more than 90 % of the fake jets and keeps more than 90 % of eﬃciency for QCD jets.
For cosmic-J3 mixed events, only 50 % of the fake jets are removed.
The information from the Inner Detector tracks will provide further cleaning, as mentioned in the previous section. The number of associated tracks in jets and the charge
fraction deﬁned in equation (9.3) are shown in Figure 9.16 and 9.17.

180

Table 9.11: The breakdown of the ratio of jets remaining after diﬀerent cut values on
the charge fraction for Cone4H1TopoJets (ET > 20 GeV) in pure cosmic L1Calo stream,
overlaid cosmic samples on Minimum Bias and QCD dijet J3 Monte Carlo samples. Only
statistical errors were considered.
EMF Cut≥ 0.01≥ 0.05≥ 0.1≥ 0.15≥ 0.2

L1Calo stream
0± — %
0± — %
0± — %
0± — %
0± — %

Cosmic jets on MB
19.4 ± 0.7%
5.6 ± 0.4%
1.4 ± 0.2%
0.6 ± 0.1%
0.3 ± 0.1%

181

Cosmic jets on J3 QCD jets in cosmic-on-J3
56.6 ± 0.8%
99.0 ± 0.07%
38.4 ± 0.8%
96.5 ± 0.1%
30.4 ± 0.7%
91.9 ± 0.2%
25.9 ± 0.7%
85.6 ± 0.2%
21.5 ± 0.7%
78.0 ± 0.3%

Part III
Prospects towards New Physics
Search182

Chapter 10
Searches for New Physics with Jets
and ETmiss Event Topologies
The current status of the ATLAS calorimeters were investigated and understood withthe real commissioning data as mentioned in the previous chapters.
In the next chapters, the strategies to search for the physics beyond the Standard
Model were investigated with jets and ETmiss event topologies. Jets and ETmiss event topologies oﬀer important search modes for many physics models beyond the Standard Modelas will brieﬂy be mentioned in the next section. Jets and ETmiss highly depend on theperformance of the calorimeters, but the commissioning studies of the calorimeters indicate that the detector is in a stable condition, and we are ready to proceed into theperformance studies of the physics objects and to the physics analyses.
In Chapter 11, we investigate the monojet event topology, which is an importantchannel to search for the Large Extra Dimensions. In order to perform successful searches,
understandings of the detector performance and the rate of the Standard Model and noncollision background are highly essential. We were able to understand the eﬀects fromthe problematic calorimeter channels, and also the cosmic background from the real data.
In Chapter 12, prospects towards multi-jet plus ETmiss event topology are described.
Since the uncertainty of multi-jet processes are still large for the Monte Carlo simulations,data-driven methods to estimate the Standard Model background are indispensable. The
ways to perform the estimations were proposed. The expected rate of cosmic backgroundwas estimated with the real cosmic data and also the event overlay technique with cosmic
data and QCD multi-jet Monte Carlo simulation.
Note that in Part III, the studies are fully done with Monte Carlo simulation, except
for parts related to the cosmic background.
In this chapter, the overview of searches with jets and ETmiss is given, and the deﬁnitions and criteria of physics objects and algorithms used in Chapter 11 and 12 will bementioned.

183

10.1

Jets & ETmiss Signatures from New Physics

In this thesis, we are motivated to develop search strategies for physics beyond the Standard Model with jets and ETmiss . As mentioned in Chapter 1, SUSY and extra dimensionalmodels predict weakly-interacting particles (neutralinos for many of the SUSY models,and KK-gravitons for Large Extra Dimensions model) which are good candidates for thedark matter in the universe. Thus, the searches using ETmiss are highly important. Dueto the feature of hadron colliders, and also from the properties of particle production,
and/or the decays of produced new particles, high pT jets are expected to accompany inevents with SUSY particles or KK-gravitons.

10.1.1SUSY Search Modes in ATLAS

Event topology-based searches allow for model-independent searches for SUSY. The following modes are considered in ATLAS,• No-lepton search mode
• One-lepton search mode
• Opposite-sign dilepton search mode
• Same-sign dilepton search mode
• Multi-lepton search mode
The search modes are considered with various jet multiplicity (usually starting fromtwo high pT jets, up to three or four). Search modes with b-jets or hadronic τ ’s may
also provide additional sensitivity for the SUSY searches. Monojet signatures may arise
from some of the AMSB SUSY, or split SUSY models as will be brieﬂy mentioned in
Chapter 11.
Among the search modes, no-lepton and one-lepton search modes have the largest
discovery reach among various mSUGRA parameters [23][129], and thus highly importantfor early data analyses. However, observations in various search modes allow us to narrow
down the candidates of new physics, once excess(es) are found in some of the searchmodes, as excesses themselves do not carry signiﬁcant amount of information about their
physics origin.
In Chapter 12, we concentrate on the investigations of no-lepton search mode, because
it is one of the most important channel for the discovery. The most challenging part of theno-lepton search mode is the estimation of background. Strategies to perform successful
prediction will be mentioned in the chapter.

10.2

Physics Objects and Algorithms

Identiﬁcation criteria including isolation and overlap removal were mentioned below foreach object.
184

10.2.1

Electrons

Electrons are reconstructed and identiﬁed using the inner detector and LAr electromagnetic calorimeter by so called the “eGamma” algorithm [23]. The basic concept is tomatch the tracks from the Inner Detector to the clusters in the EM calorimeter. The“eGamma” algorithm is actually an inclusive term for three algorithms; an algorithm
exploiting calorimeter clusters as the seeds (the algorithm for high pT electrons), an algorithm using the Inner Detector tracks (for low pT electrons), or the one speciﬁcally used
for forward electrons with no requirement on the matching of tracks to the calorimeter.
Here, the transverse momentum of an electron is required to be larger than 20 GeV, andthe absolute value of pseudorapidity η to be less than 2.5. On top of the basic identiﬁcation criteria (such as track ﬁt quality, shower shape in the calorimeter), isolation
conditions are required to avoid identifying
electrons originating from b-jets. The trans
2verse energy in a cone of ΔR (= Δη + Δφ2 ) < 0.2 from the electrons is required tobe smaller than 10 GeV. When electrons are within 0.2 < ΔR < 0.4 from a jet, they
are discarded, since they are likely to have originated from b-jets (in case of ΔR < 0.2,
the electron is kept and the overlapping jet is discarded since they are very likely comingfrom the same electron).

10.2.2Muons

So called the STACO algorithm [23][52] was used for the muon reconstruction. It makes
use of a statistical combination of tracks reconstructed in the Muon Spectrometer andthe Inner Detector. The tracks in the Muon Spectrometer and the Inner Detector werematched correspondingly with a requirement of a reasonable matching quality (here,χ2 < 100 is required). The transverse energy inside a cone of ΔR < 0.2 from muons isrequired to be smaller than 10 GeV. When muons are within ΔR < 0.4 from a jet, they
are discarded, since they are likely to have originated from b-jets.

10.2.3Jets

Jets reconstructed with the seeded Cone algorithm were used. The details of jet reconstruction were previously mentioned in Section 8.4.2. Here, jets reconstructed from
Topoclusters as inputs by the ATLAS cone algorithm with the cone size 0.4 (“Cone4H1TopoJets”)
are used for the analysis.

10.2.4

Missing ET

As frequently mentioned in this thesis, ETmiss is the imbalance of energy (momentum, tobe precise) in the transverse plane. Due to the feature of the hadron collider, the energy
in the z direction is not balanced, because only fractions of the energy of incoming protons
are carried by the interacting partons (and the energy of the partons are unknown foreach event).
ETmiss carries important information for weakly interacting particles (neutrinos andpossibly new particles such as LSP’s and KK particles), and thus very important for185

searches of physics beyond the Standard Model (needless to mention its importance for
the Standard Model measurements).
The algorithm of ETmiss and its categories were mentioned in Sec. 7.4.1 and Appendix C. Here, “MET Final” was used in the analysis. It is a calorimeter cell-based
ETmiss , with corrections applied for the muon contribution and the energy lost in thecryostat.

10.2.5b-Taggingb-Tagging is the identiﬁcation of jets originating from the hadronization of b-quarks. The
tagging of b-jets is especially important for Top physics, Higgs searches, and searches for
SUSY.
The identiﬁcation of b-jets are performed using their features that is quite diﬀerent
from jets originating from lighter quarks. Such features are,• Long lifetime of hadrons containing a b quark (cτ ∼ 450 μm). Presence of displaced
vertices are expected.
• Hard fragmentation. A b-hadron carries large fraction of the original momentumof the b quark.
• Relatively large mass of b-hadrons. Decay products of b-hadrons have large transverse momentum from the jet axis.
The ﬁrst feature indicate that the reconstruction of ID tracks and measurement of theirimpact parameters are highly important. Also, the presence of leptons in jets can complementarily used for the tagging when semi-leptonic decays occur from b-hadrons. In
this thesis, the standard b-tagging algorithm in ATLAS using the combination of information from the impact parameter of ID tracks associated with jets, and the presence of
secondary vertex is used. Weights are deﬁned and calculated from the likelihood ratios,
and a cut is used to tag b-jets. Figure 10.1 shows the b-tagging weight for b-jets, c-jets,and light ﬂavor jets. In this thesis, jets with weights larger than 3 are deﬁned as b-jets.
Currently, the performance of b-tagging is evaluated from Monte Carlo simulations, but
it will be measured from data during the actual data taking by using QCD di-jet and tt̄
events.

186

Arbitrary units

10-1b-jetsc-jetsLight jets

ATLAS

10-2

-3

10

10-4
-5

10

-6

10

-20

-10
0
10
20
30
40
3D impact parameter + secondary-vertex weight

Figure 10.1: Distribution of jet b-tagging weight for b-jets (red), c-jets (green), and lightﬂavor jet (blue) [23].

187Chapter 11
Monojet Search for the Large Extra
Dimensions
In the previous chapter, the quality of calorimeter objects was extensively investigatedwith both pure and MC-overlaid cosmic events. Those cleaning cuts using quality variables are crucial for physics measurements using jets and ETmiss . Here, we estimatedthe contribution of cosmic background to various event signature with jet(s) and ETmiss ;from monojet to multi-jet plus large ETmiss . These event signatures are expected invarious new physics models; from the Supersymmetry, the Large Extra Dimensions, Leptoquarks, and Heavy Vector Bosons, for example. The contribution on the Standard
Model background was investigated in detail in the previous studies [23][130]. Here, theﬁrst data-driven studies on the cosmic background is shown, and the strategy to copewith such background is proposed.
In Section 10.2, the deﬁnition and identiﬁcation criteria of physics objects used in
the analyses were mentioned. In this chapter, the emphasis is on the monojet searchwhere non-collision background can signiﬁcantly aﬀect the analyses, and the Large Extra
Dimensions (LED) model [27] with KK-graviton emissions was taken as a benchmark forthe monojet search.

11.1

Monojet Signature from the Large Extra Dimensions

Previous studies of the Standard Model background in monojet search are mentionedin [137][138]. Here, we show the ﬁrst investigation in ATLAS on the contribution ofcosmic background to monojet signature using the real data, and its eﬀects on the Large
Extra Dimensions search. Sherpa [139] Monte Carlo generator was used for the signal
production, which has a robust framework to generate the spin-2 KK-gravitons using thematrix-element calculator AMEGIC++ [140][141].
A brief overview of real KK-Graviton emission processes in the Large Extra Dimensions model is given in Section 11.1.1. The proﬁle of Monte Carlo samples used in thisstudy is mentioned in Section 11.2. Section 11.3 shows investigations on the eﬀects of188

selection cuts and cosmic background in the monojet search.

11.1.1

Real KK-Graviton Emission in Large Extra Dimensions
Model

As mentioned in Chapter 2, according to the Large Extra Dimension model, the Planckscale can be as low as the TeV-scale due to the presence of the extra dimensions (thenumber of the extra dimensions is described as δ in the following text). The small observed value of the Newton’s constant may be due to the existence of a large compactiﬁed
volume of the extra dimensions [27]. The interactions of the Kaluza-Klein excitations of
the gravitons can be described by a 4-dimensional eﬀective ﬁeld theory [142]. The effects of those KK-excitations from the LED model can manifest themselves in several
event signatures such as “jet + ETmiss (monojet)”, “photon + ETmiss ,” by emission of
KK-gravitons (Figure 11.1) or excess of dijet or a lepton pair productions in the highenergy tail (virtual KK-graviton exchange). Those signals were extensively searched in
the previous experiments at the LEP [31]-[34] and the Tevatron [35]-[39]. The current
exclusion limit is around 1 TeV for most numbers of extra dimensions (Figure 2.6). Here,we consider the monojet signature originating from the KK-graviton emissions.
In the LED model, the rate of graviton emission is enhanced by the large phase spaceof the Kaluza-Klein modes, and counteract the suppression from MP . The KK-graviton
modes have masses |n|/R (n: a δ-dimensional vector with components indicating the KKexcitation level in each extra dimension, R: size of the compactiﬁed extra dimensions),
and the mass splitting Δm of the KK-modes is described as,

2/δ
 δ+2

12δ−31
1
MD
MD 2
Δm ∼
= MD
∼
10 δ eV(11.1)R
TeV
M̄P√
M¯P = MP / 8π .
(11.2)For small numbers of extra dimensions, the mass splitting is far below the energy resolution of the detector (4 meV for MD =2 TeV, δ = 2; 160 keV for MD =2 TeV, δ = 4
for example). For such small numbers of extra dimensions (and thus large R), the sum
over Kaluza-Klein modes can be replaced by a continuous integral. Considering a hypersphere with a unit radius in δ dimensions, the number of modes between n and n + dn(|n| is written as n for simplicity) can be written as,dN =2π δ/2 δ−1n dn .
Γ(δ/2)(11.3)

This equation can be rewritten in terms of m(= n/R), the mass of a Kaluza-Klein mode,22π δ/2 M¯Pmδ−1 dm .
dN =Γ(δ/2) MD2+δ

(11.4)

Thus, the diﬀerential cross section for inclusive KK-graviton production is described as,
22π δ/2 M¯P
dσm
d2 σ
=
,
mδ−1
2+δ
dtdm
Γ(δ/2) MD
dt

189(11.5)

g

g

g

GKK g

q

g

GKK q

GKK

q

q

Figure 11.1: Leading order Feynman diagrams of KK-graviton emission with monojet
signature.
where t is the Mandelstam variable and m corresponds to the mass of the KK-graviton.
For the monojet signature the leading order diagrams are as shown in Figure 11.1, andthe diﬀerential cross section for each process is,
αs 1
dσm(gg → gGKK ) =F1 (t/s, m2 /s) ,dt
36 sM¯P 2
dσmαs 1(qg → qGKK ) =F2 (t/s, m2 /s) ,dt
96 sM¯P 2
3αs 1
dσm(q q̄ → gGKK ) =F3 (t/s, m2 /s) ,dt
16 sM¯P 2(11.6)(11.7)(11.8)

where t and s follow the standard deﬁnition of the Mandelstam variables. The F1 ,F2 ,and F3 functions will be explicitly shown in Appendix D. Thus, in order to calculate thediﬀerential cross section for monojet signature, an integral should be taken in regards
to m. The upper limit of the integral is typically taken as MD , and this approach was
adopted here as well.

11.2

Monte Carlo Simulation Samples and Data

As mentioned in the previous section, SHERPA (version 1.1.3) was used for the signal
production. Three processes (Figure 11.1) were inclusively generated. Figure 11.2 shows
the cross section of ADD monojet processes in SHERPA as a function of the number ofextra dimensions. The cross sections shown in the ﬁgure are after a cut on the leading
parton (pT >80 GeV) was applied. Cross sections for MD =2,3,4√ TeV are shown. Here,we will use four samples shown in Table 11.2. The cut on ŝ < MD was applied forthe summation of KK-graviton excitations in each sample (the calculation was based on[142])For the Standard Model background, Z/W+jets (separate samples for Z(→ ν ν̄), Z(→+ −e e ), Z(→ μ+ μ− ), and Z(→ τ + τ − )), tt̄, single top and QCD dijets were considered.
The information for each sample is shown in Table E.2 in Appendix . QCD dijet samples
were generated in momentum slices as mentioned in the table and Section 9.1.1.
Real cosmic data and cosmic data overlaid on the Minimum Bias Monte Carlo samples
were used to estimate the contribution of cosmic background.

190

Cross Section [pb]300
MD=2 TeV
MD=3 TeV
MD=4 TeV

102
250
200
10
150
100
1
50
10−1
1.5

2

2.5

3

3.5

4
4.5
5
5.5
6
6.5
Number of Extra Dimensions

Figure 11.2: Cross section of LED monojet processes in SHERPA as a function of thenumber of extra dimensions. Cut on the leading parton (pT >80 GeV) is applied. Cross
sections for MD =2,3,4 TeV are shown.
Table 11.1: Cross section of LED processes in SHERPA. Cut on the leading parton(pT >80 GeV) is applied.
Planck Scale MD
2 TeV
2 TeV
4 TeV
4 TeV

11.3

δ
xsec [pb]272.42 ± 0.12
6 9.810 ± 0.015
2 4.897 ± 0.008
6 0.1562 ± 0.0002

Event Selection

Search for the Large Extra Dimensions in the monojet channel is one of the eﬀective
ways, and has a good sensitivity to the signals if they exist.
Considering the kinematics of the processes, the events are expected to have thefollowing features,• Absence of leptons• High pT monojet• Large ETmiss
Here, we will investigate on the trigger eﬀects and useful selection cuts to perform searches
in the monojet signature.
191

11.3.1

Trigger Menus for the Monojet SearchHere, we consider trigger menus “lumi1E31 no Bphysics no prescale” in the Monte Carlosamples (reconstructed with Athena version 14.2.25.9), which are intended for 1031 cm−2 s−1
luminosity. The prescales are not considered in the simulation itself, so the proposedprescales are implemented by hand in the analyses when needed (in such cases, it will
explicitly be mentioned in the text). The menus have minor diﬀerences from what are
currently proposed (see Table 4.1), but the diﬀerences are not signiﬁcant in the following
studies.
The trigger menus without prescales useful for the monojet searches are “EF J265
(leading jet pT > 265 GeV),” “EF J350 (leading jet pT > 350 GeV),” and “EF j70 xe30
(leading jet pT > 70 GeV and ETmiss > 30 GeV).” Trigger menus with lower thresholds
and prescales will allow for estimating the trigger eﬃciencies in data-driven ways. The
trigger eﬃciencies from the jet trigger menus as a function of the leading jet transverse
momentum are shown in Figure 11.3. The QCD dijet Monte Carlo samples are used in
the ﬁgure. ETmiss trigger eﬃciencies as a function of ETmiss are shown in Figure 11.4 forvarious ETmiss algorithms. We used MET Final for the analysis here.
In order to reduce the trigger eﬀects in the analyses as much as possible, the cut on
the leading jet pT is preferable to be more than about 300 GeV for EF J265 and 400 GeV
for EF 350. For EF j70 xe30, the cut on the leading jet pT should be larger than about
100 GeV and ETmiss to be larger than 80 GeV.

11.3.2

Absence of Reconstructed Leptons

Since no leptons exist in the leading-order diagrams of KK-graviton emissions, it is appropriate to reject events with leptons, which will remove large fraction of W(→ lν)and Z(→ τ + τ − ) + jet events. In QCD dijets and LED samples, electrons can fakely bereconstructed from jets in a low rate.
In Figure 11.5, the numbers of reconstructed electrons are shown for events with theleading jet pT > 100 GeV for three diﬀerent selection criteria of electron reconstructions(which are mentioned as “loose,” “medium,” and “tight”). Here, we take leading jet
pT > 100 GeV cut as a starting benchmark. The breakdown of events remaining after anelectron veto for each criteria is shown in Table 11.2. Reconstructing electrons with loose
selection criteria and rejecting events with such electrons do not signiﬁcantly reduce thesensitivity to the LED signals (reduction by about a few percent), but reduces backgroundby a few tens of percent compared to the case with tighter electron selection criteria.
Thus, it is preferable to use loose electron selection criteria to veto more Standard Modelbackground.
Due to the same reason as the electrons, events with muons should be rejected forthe monojet search. Well isolated “combined muons” (reconstructed by the combinationof Inner Detector and Muon Spectrometer tracks) originate from Z/W + jets events
where the bosons decay into muon(s) or tau(s) consecutively decaying into muon(s).
Requiring absence of combined muons will reject large amount of Z(→ μ+ μ− )+jets and
W (→ μν)+jets.

192

Trigger Efficiency

1
EF_J10
EF_J50
EF_J80
EF_J115
EF_J140
EF_J180
EF_J265
EF_J350

0.8
0.6
0.4
0.2
0

50

100

150

200

250

300

350 400 450 500
Leading Jet pT [GeV]Trigger Efficiency

Figure 11.3: Eﬃciency of jet triggers at the Event Filter level as a function of the leading
jet transverse momentum. The eﬃciencies from QCD dijet Monte Carlo samples areshown in the ﬁgure.

1
MET_Final

0.8

EF_xe15
EF_xe20
EF_xe25
EF_xe30
EF_xe40
EF_xe50
EF_xe70
EF_xe80

0.6
0.4
0.2
0

20

40

60

80

100 120 140 160 180 200 220
Missing ET [GeV]

Figure 11.4: Eﬃciency of ETmiss triggers at the Event Filter level as a function of ETmiss .
The eﬃciencies from Z(→ ν ν̄) + jets Monte Carlo samples are shown in the ﬁgure.
MET Final is used for the ETmiss algorithm.

193

Number of Events [/200 pb−1]

6

1000
109

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
700
106
600
105
500
104
400
103

Loose Electrons

300
102
10
200
100
1
10−1 0

2

4

6
8
10
Number of Loose Electrons

Number of Events [/200 pb−1]

6

1000
109

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
700
106
600
105
500
104
400
103

Medium Electrons

300
102
10
200
100
1
10−1 0

2

4

6
8
10
Number of Medium Electrons

Number of Events [/200 pb−1]

6

1000
109

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
700
106
600
105
500
104
400
103

Tight Electrons

300
102
10
200
100
1
10−1 0

2

4

6
8
10
Number of Tight Electrons

Figure 11.5: Number of reconstructed electrons (left: loose IsEM, center: medium IsEM
criteria, right: tight IsEM) from LED KK-graviton signals and background (for eventswith the leading jet pT >100 GeV). The distributions are normalized to 200 pb−1 .
194

Number of Events [/200 pb−1]

6

1000
109

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
106
700
105
600
104
500
103
400
102
300
10
200

Combined Muons

1
100
−1
10

0

1

2

3
4
5
6
Number of Combined Muons

Number of Events [/200 pb−1]

6

1000
109

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
106
700
105
600
104
500
103
400
102
300
10
200
1
100
−1
10

MS Standalone Muons

0

2

4

6

8
10
12
Number of MS Muon Tracks

Figure 11.6: Number of reconstructed combined and MS-standalone muons from LED
KK-graviton signals and background (for events with the leading jet pT >100 GeV). The
distributions are normalized to 200 pb−1 .

195

On top of combined muons, standalone Muon Spectrometer (MS) tracks provide additional background rejection. Figure 11.6 shows the numbers of reconstructed combined
and stand-alone Muon Spectrometer tracks. Table 11.3 shows the breakdown of numberof events without combined or MS-standalone muons.
The utmost motivation to require the absence of standalone MS tracks is to reject thecosmic background. Most of the cosmic muons are non-projective (not passing near theinteraction point), and will not be reconstructed as combined tracks, but are very often reconstructed as standalone MS tracks. As obvious from Table 11.3, the cosmic backgroundis reduced by a factor of 16. Also, this selection cut further removes Z(→ μ+ μ− )+jets and
W (→ μν)+jets, when the muons failed the combined muon reconstruction but tagged
as MS-standalone muon tracks. Another byproduct of this rejection scheme is that it
also rejects events with pion “punch-through’s” from jets’ shower leakage, where suchleakage forms tracks in the muon spectrometer. Events with punch-throughs have badly
reconstructed ETmiss , so it is appropriate to rejects such events for both signals andbackground.

11.3.3

Selection Cuts on Jets

The most important cut to be applied to jets is the second-leading jet veto. Whereas
Z(→ ν ν̄)+jets and most of the W (→ τ ν) jets are irreducible background, QCD dijet
events can signiﬁcantly be reduced by selection cuts on jets. Among those selection cuts,
the second leading jet veto is the most important strategy.
Figure 11.7 and 11.8 show the leading and second-leading jet distributions. There are
signiﬁcant contributions from QCD dijets and cosmic rays as expected, which surpass
the signals without any speciﬁc considerations. There are no signiﬁcant diﬀerence in thepseudorapidity (η) distributions in the signals and background, but it is meaningful toselect jets in the central region to avoid the cracks (1.37 < η < 1.52) in the calorimeters,
which one of the dijet can be lost and fake monojet signals.
Since the second-leading jets tend to be soft for the KK-graviton emission processes,
a veto on the second-leading jets still keeps high eﬃciency of the signals, but signiﬁcantly
removes the QCD dijet background (however, too tight a veto also reduces the signals bysome factor so the cut should be balanced considering both eﬀects).

11.3.4Non-collision Background Rejection

The jet EM fraction (EMF) is a useful discrimination variable to reject non-collision
background as mentioned in the previous chapter. Figure 11.10 shows the distributionsof jet EM fraction for signals and background. Rejecting events which has low EM fraction
values of the leading jet removes signiﬁcant amount of cosmic background when a hardmuon bremsstrahlung occurs in TileCal. Applying higher cut on the jet EM fraction notjust removes remaining background from the cosmic muons when the bremsstrahlung
takes place in LAr EM calorimeter, but also rejects events with an electron failing to be
identiﬁed and reconstructed as a jet (e.g. background from W(→ eν) events).
The jet charge fraction (CHF) is another variable to successfully reject non-collision

196

Table 11.2: Number of events with no reconstructed electrons (only events with theleading jet pT >100 GeV are counted below). Integrated luminosity is assumed to be 200
pb−1 .
SampleNo Loose Electron
LED MD =2, δ = 27662 ± 88
LED MD =2, δ = 6
520 ± 23
LED MD =4, δ = 2
263 ± 16
LED MD =4, δ = 6
10 ± 3
Cosmic(5.512 ± 0.007) × 105
QCD Dijet(1.7631 ± 0.0001) × 108
tt̄
12357 ± 111
Z(→ ν ν̄)+jets
13252 ± 115
Z(→ e+ e− )+jets
787 ± 28
+ −Z(→ μ μ )+jets
2327 ± 48+ −Z(→ τ τ )+jets
2099 ± 46
W (→ eνe )+jets
9015 ± 95
W (→ μνμ )+jets
18763 ± 137
W (→ τ ντ )+jets
17438 ± 132
Single Top
2002 ± 45

No Medium Electron
7859 ± 89
536 ± 23273 ± 17
10 ± 3(5.518 ± 0.007) × 105(1.8303 ± 0.0001) × 108
14114 ± 119
13395 ± 116
945 ± 312384 ± 49
2362 ± 49
10310 ± 102
19078 ± 138
18537 ± 136
2184 ± 47

No Tight Electron
7860 ± 89
536 ± 23272 ± 17
10 ± 3(5.518 ± 0.007) × 105(1.832 ± 0.0001) × 108
14825 ± 122
13402 ± 116
1127 ± 34
2401 ± 49
2417 ± 49
11799 ± 109
19135 ± 138
18739 ± 137
2280 ± 48

Table 11.3: Number of events with no reconstructed muons (only events with the leading
jet pT >100 GeV are counted below). Integrated luminosity is assumed to be 200 pb−1 .
SampleNo Combined Muon
LED MD =2, δ = 27859 ± 89
LED MD =2, δ = 6
536 ± 23
LED MD =4, δ = 2
273 ± 17
LED MD =4, δ = 6
10 ± 3
Cosmic(5.507 ± 0.007) × 105
QCD Dijet(1.8318 ± 0.0001) × 108
tt̄
14091 ± 119
Z(→ ν ν̄)+jets
13402 ± 116
Z(→ e+ e− )+jets
3184 ± 56+ −Z(→ μ μ )+jets
283 ± 17+ −Z(→ τ τ )+jets
2348 ± 49
W (→ eνe )+jets
21234 ± 146
W (→ μνμ )+jets
7008 ± 84
W (→ τ ντ )+jets
18438 ± 136
Single Top
2144 ± 46

197

No MS-standalone Muon
7052 ± 84
484 ± 22242 ± 169±335205 ± 188(1.6706 ± 0.0001) × 108
8423 ± 92
12696 ± 113
3022 ± 55
71 ± 81890 ± 43
20195 ± 142
2593 ± 51
16075 ± 127
14400 ± 38

Number of Events [/200 pb−1]

6
9 × 101000
10Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
106
700
105
600
104
500
103
400
102
300
10
200
1
100
10−1

200

400 600

800 1000 1200 1400 1600 1800 2000
Leading Jet pT (GeV)

Number of Events [/200 pb−1]

6

1000
109

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
106
700
105
600
104
500
103
400
102
300
10
200
1
100
10−1
−5

−4

−3

−2

−1

0

1

2

3
4
5
Leading Jet η

Figure 11.7: Leading jet distributions of LED KK-graviton signals and background(pT > 100GeV). The left ﬁgure shows the transverse momentum and the right shows
the pseudorapidity. The distributions are normalized to 200 pb−1 .

198

Number of Events [/200 pb−1]

6
9 × 101000
10Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
106
700
105
600
104
500
103
400
102
300
10
200
1
100
10−1
0

100 200 300 400 500 600 700 800 900 1000
Second Leading Jet pT (GeV)

Number of Events [/200 pb−1]

6

1000
109

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
106
700
105
600
104
500
103
400
102
300
10
200
1
100
10−1
−5

−4

−3

−2

−1

0

1

2
3
4
5
Second Leading Jet η

Figure 11.8: Second leading jet distributions of LED KK-graviton signals and background(when leading jet pT > 100GeV). The left ﬁgure shows the transverse momentum andthe right shows the pseudorapidity. The distributions are normalized to 200 pb−1 .

199

6

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
6

10
700
5

10
600
104
500
103
400
102
300
10
200
1
100
10−1

Number of Events [/200 pb−1]

Number of Events [/200 pb−1]

6

1000
109

(pT >20 GeV)

0

5

10

1000
109
800
107
106
700
105
600
104
500
103
400
102
300
10
200

Number of Events [/200 pb−1]

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
6

10
700
5

10
600
104
500
103
400
102
300
10
200
1
100
−1
10

(pT >40 GeV)

0(pT >30 GeV)

0

5

10

15
20
Number of Jets (pT >30GeV)

6

× 10

5

10

15
20
Number of Jets (pT >40GeV)

Number of Events [/200 pb−1]

Number of Events [/200 pb−1]

6

1000
109

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108

1
100
10−1

15
20
Number of Jets (pT >20GeV)

× 10

1000
109

1000
109

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
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600
104
500
103
400
102
300
10
200
1
100
−1
10

(pT >50 GeV)

0

5

10

15
20
Number of Jets (pT >50GeV)

6

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
106
700
105
600
104
500
103
400
102
300
10
200
1
100
−1
10

(pT >60 GeV)

0

5

10

15
20
Number of Jets (pT >60GeV)

Figure 11.9: Number of jets with pT > 20 GeV (upper left), 30 GeV (upper right), 40
GeV (middle left), 50 GeV (middle right), and 60 GeV (bottom) for events with theleading jet pT > 100 GeV. The distributions are normalized to 200 pb−1 .

200

Table 11.4: Number of events with second leading jet veto (only events with the leading
jet pT >100 GeV are counted below). Integrated luminosity is assumed to be 200 pb−1 .
Sampleveto 20 GeV
LED MD =2, δ = 22032 ± 45
LED MD =2, δ = 6
118 ± 11
LED MD =4, δ = 2
66 ± 8LED MD =4, δ = 6
2±1
Cosmic(5.249 ± 0.007) × 105
QCD Dijet(1.567 ± 0.001) × 106tt̄
101 ± 10Z(→ ν ν̄)+jets
8273 ± 91
Z(→ e+ e− )+jets
647 ± 25
+ −Z(→ μ μ )+jets
1035 ± 32+ −Z(→ τ τ )+jets
295 ± 17W (→ eνe )+jets
6592 ± 81
W (→ μνμ )+jets
8696 ± 93
W (→ τ ντ )+jets
4950 ± 70
Single Top
84 ± 9veto 30 GeV
2989 ± 55
185 ± 14100 ± 103±2(5.384 ± 0.007) × 105(2.628 ± 0.002) × 106
395 ± 204136 ± 64
1067 ± 33
1452 ± 38
550 ± 239831 ± 99
11775 ± 109
8022 ± 90
213 ± 15

Sampleveto 50 GeV
LED MD =2, δ = 24612 ± 70
LED MD =2, δ = 6
290 ± 17
LED MD =4, δ = 2
157 ± 13
LED MD =4, δ = 6
5±2
Cosmic(5.447 ± 0.007) × 105
QCD Dijet(1.2829 ± 0.0004) × 107
tt̄
2200 ± 47
Z(→ ν ν̄)+jets
10912 ± 104
Z(→ e+ e− )+jets
1605 ± 40+ −Z(→ μ μ )+jets
1938 ± 44+ −Z(→ τ τ )+jets
1103 ± 33
W (→ eνe )+jets
13758 ± 117
W (→ μνμ )+jets
15464 ± 124
W (→ τ ντ )+jets
12449 ± 112
Single Top
645 ± 25

201

veto 40 GeV
3913 ± 63
241 ± 16129 ± 114±2(5.429 ± 0.007) × 105(5.424 ± 0.002) × 106
1066 ± 33
9872 ± 991345 ± 37
1746 ± 42806 ± 28
12046 ± 110
13936 ± 118
10505 ± 102
407 ± 20

veto 60 GeV
5227 ± 72
336 ± 18
177 ± 136±2(5.460 ± 0.007) × 105(2.6637 ± 0.0005) × 107
3699 ± 61
11663 ± 108
1835 ± 432076 ± 46
1347 ± 37
15193 ± 123
16652 ± 129
14125 ± 119
914 ± 30

Number of Jets [/200 pb−1]

6

100
108

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

907
10
806
10
705
10
60
104
50
103
40
102
30
10
20
1
10
10−1
0

0.2

0.4

0.6

0.8
1
Leading Jet EMF

Figure 11.10: EM fraction of leading jet from LED KK-graviton signals and background(when leading jet pT > 100GeV). The distributions are normalized to 200 pb−1 .

Number of Jets [/200 pb−1]

6

1000
109

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

900
108
800
107
106
700
105
600
104
500
103
400
102
300
10
200
1
100
−1
10
0

0.2

0.4

0.6

0.8

1
1.2
Leading Jet CHF

Figure 11.11: Charge fraction of leading jet from LED KK-graviton signals and background (when leading jet pT > 100GeV). The distributions are normalized to 200 pb−1 .

202background. Figure 11.11 shows the distributions of charge fraction of the leading jets
for signals and background. As fake jets reconstructed from cosmic muon energy deposits
have almost no Inner Detector tracks associated to the jets, the charge fraction is steeply
concentrated around 0.
Table 11.6 shows the breakdown of numbers after the EM fraction cut (0.15 <leading
jet EMF< 0.95) and charge fraction cut (leading jet CHF> 0.1).

11.3.5

Event Selection

Here, the following cuts were used for the event selection.
• Cut 1: No lepton (e, μ)• Cut 2: Leading jet pT > 200 GeV, |η| < 1.2• Cut 3: ETmiss > 250 GeV• Cut 4: second leading jet veto: second leading jet pT < 50 GeV• Cut 5: Δφ(second leading jet, ETmiss ) > 0.15
• Cut 6: No jet within ΔR < 0.1 from dead or masked channels• Cut 7: No tracks in the muon spectrometer• Cut 8: Jet EM fraction > 0.15
• Cut 9: Jet Charge Fraction > 0.1
The ETmiss distributions after each cut are shown in Figure 11.12 (from the upper left; Cut
2 is applied, and remaining cuts are additionally applied one by one). The non-existenceof leptons are required, since the physics process of the LED KK-graviton emissions does
not include a lepton. The leading jet is expected to have a large transverse momentum,
since it is recoiled against an LED KK-graviton with mass of TeV order. Only the leading
jet in the barrel region is required to avoid fake background from the QCD dijet, where
one of the jets is badly reconstructed due to detector features such as the crack region. A
cut on ETmiss is applied, which is a straightforward requirement from the characteristics ofthe physics process. A veto on the second leading jet is required in order to avoid dijets
with ETmiss , which can originate from calibration uncertainty or other systematic eﬀects.
Cut 5-7 are the “cleaning cuts” for non-collision background rejection. Since the standard
identiﬁcation of muons in ATLAS requires a good combination of reconstructed tracks
from the Inner Detector and the Muon Spectrometer, most of the cosmic muons fail thecriteria due to the fact that not many cosmic muons pass the Inner Detector. Cut 5 takesuch an eﬀect into account, and removes events with tracks from the muon spectrometer.
Cut 5 signiﬁcantly removes the cosmic background, but still a non-negligible amount ofbackground remains. This is due to the fact that a large fraction of high energy cosmic
muons have a large azimuthal angle, and do not leave enough hits to be well reconstructedin the muon spectrometer. On top of the angular characteristics, cosmic muons reach the203detector with a large in-coincidence to the bunch crossing. Such a large timing oﬀsets of
the hits in the muon spectrometer often leads to the non-reconstruction of muon tracks.
This phenomenon was actually observed in the cosmic data in the Fall of 2008.
After all the event selection and cleaning cuts, the most dominant cosmic backgroundis mostly removed and are under control. This result indicates the successful understandings and treatment of the cosmic background.

204

3

× 10

106
4500

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

After Cut 2

4000
105
35004
10
3000
103
2500
102
2000
1500
10
1000
1
500
10−1
0

200

400

600

800

Number of Events [/200 pb−1]

Number of Events [/200 pb−1]

3

5000

5000
106
4500

102
2000
1500
10
1000
1
500
10−1

1000 1200 1400 1600
Missing ET [GeV]

200

After Cut 4

102
2000
1500
10
1000
1
500
−1
10
200

400

600

800

1000

Number of Events [/200 pb−1]

Number of Events [/200 pb−1]

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

4000
105
35004
10
3000
103
2500

5000

1500
10
1000
1
500
−1
10
200

4000
105
35004
10
3000
103
2500
102
2000
1500
10
1000
1
500
−1
10

400

600

800

1000

5000

400

600

800

1000

1200 1400 1600
Missing ET [GeV]× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

6

10
4500

After Cut 7

4000
105
35004
10
3000
103
2500
102
2000
1500
10
1000
1
500
−1
10

1200 1400 1600
Missing ET [GeV]

200

400

600

800

1000

1200 1400 1600
Missing ET [GeV]

3

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

After Cut 8

4000
105
35004
10
3000
103
2500
102
2000
1500
10
1000
1
500
−1
10
200

400

600

800

1000

1200 1400 1600
Missing ET [GeV]

Number of Events [/200 pb−1]

3

Number of Events [/200 pb−1]1200 1400 1600
Missing ET [GeV]

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

After Cut 5

200

Number of Events [/200 pb−1]

Number of Events [/200 pb−1]

After Cut 6

102
2000

106
4500

1000

3

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

4000
105
35004
10
3000
103
2500

5000

800

× 10

10
4500

1200 1400 1600
Missing ET [GeV]

× 10

6

10
4500

600

6

3

5000

400

3

× 10

6

10
4500

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

After Cut 3

4000
105
35004
10
3000
103
2500

3

5000

× 10

5000
106
4500

× 10

Cosmic Data L1Calo
Z(→ ν ν )W(→ l ν )QCD dijetttSingle Top
Z(→ ll)ADD MD=2TeV, d=2

After Cut 9

4000
105
35004
10
3000
103
2500
102
2000
1500
10
1000
1
500
−1
10
200

400

600

800

1000

1200 1400 1600
Missing ET [GeV]

Figure 11.12: Expected ETmiss distributions in monojet signature assuming the integrated
luminosity of 200 pb−1 .
205

Table 11.5: Number of events after EM fraction and charge fraction cuts (only eventswith the leading jet pT >100 GeV are counted below). Integrated luminosity is assumed
to be 200 pb−1 .
SampleEM Fraction Cut
LED MD =2, δ = 27709 ± 88
LED MD =2, δ = 6
526 ± 23
LED MD =4, δ = 2
269 ± 16
LED MD =4, δ = 6
10 ± 3
Cosmic7624 ± 87
QCD Dijet(1.7892 ± 0.0001) × 108
tt̄
18632 ± 137
Z(→ ν ν̄)+jets
13002 ± 114
Z(→ e+ e− )+jets
2218 ± 47+ −Z(→ μ μ )+jets
2330 ± 48+ −Z(→ τ τ )+jets
2521 ± 50
W (→ eνe )+jets
17974 ± 134
W (→ μνμ )+jets
18498 ± 136
W (→ τ ντ )+jets
19040 ± 138
Single Top
2743 ± 52

206

Charge Fraction Cut
7234 ± 85
510 ± 23251 ± 1610 ± 3448 ± 21(1.6047 ± 0.0001) × 108
17843 ± 134
12169 ± 110
2303 ± 48
2194 ± 47
2378 ± 49
17557 ± 133
17170 ± 131
17670 ± 133
2323 ± 48

Table 11.6: Number of events remaining after each selection cuts for background processes. Integrated luminosity is assumed to be 200 pb−1 .
Z(νν) + j
Cut 1, 2 763 ± 28
Cut 3
309 ± 18Cut 4
309 ± 18Cut 5
309 ± 18Cut 6
293 ± 17Cut 7
266 ± 16Cut 8
262 ± 16Cut 9
257 ± 16

W (τ ν)+jtt̄
798 ± 28 800 ± 28
164 ± 1358 ± 8164 ± 1358 ± 8164 ± 1358 ± 8164 ± 1355 ± 7125 ± 1126 ± 5119 ± 1125 ± 5119 ± 1125 ± 5

CosmicCut 1, 2 (1.267 ± 0.004) × 105
Cut 3(1.249 ± 0.004) × 105
Cut 4(1.249 ± 0.004) × 105
Cut 5(1.249 ± 0.004) × 105
Cut 6(1.222 ± 0.003) × 105
Cut 78745 ± 94
Cut 8
0±—
Cut 9
0±—

single-t
78 ± 9
7±3
7±3
7±3
7±3
3±2
3±2
3±2

W (eν, μν)+j Z(ll)+j
581 ± 2498 ± 10107 ± 107±3
107 ± 107±3
107 ± 107±3
107 ± 107±354 ± 74±2
48 ± 74±2
48 ± 74±2

QCD(4.519 ± 0.002) × 106
297 ± 17297 ± 17297 ± 17241 ± 1630 ± 630 ± 530 ± 5

Table 11.7: Number of events remaining after each selection cuts for the LED signals.
Integrated luminosity is assumed to be 200 pb−1 .
MD = 2, δ = 2 MD = 2, δ = 6
Cut 1, 21446 ± 38
112 ± 11Cut 3
974 ± 3174 ± 9Cut 4
974 ± 3174 ± 9Cut 5
974 ± 3174 ± 9Cut 6
925 ± 3071 ± 8Cut 7
769 ± 2863 ± 8Cut 8762 ± 2862 ± 8Cut 9757 ± 2862 ± 8207

MD = 4, δ = 2 MD = 4, δ = 6
51 ± 73±2
34 ± 62±1
34 ± 62±1
34 ± 62±1
32 ± 62±1
26 ± 52±1
26 ± 52±1
26 ± 52±1

Chapter 12
Multi-jet plus ETmiss Search for
R-parity Conserving SUSY Models
Searches for R-parity conserving SUSY models with “multi-jet plus ETmiss ” event topology
is one of the standard strategies, as mentioned in Chapter 10. In such models, squarks
and gluinos are copiously produced at the LHC, and those SUSY particles emit jets
through cascade decays down to the lightest SUSY particles (LSP’s).
In ATLAS, the “multi-jet plus ETmiss ” searches are further categorized into several
channels (see Section 10.1.1). The searches are separately done for various multiplicities of reconstructed leptons (here, electrons and muons). Presence of reconstructed
hadronic taus, or b-jets are also optionally used for the inclusive searches. Among thosesearch channels, no-lepton search mode (no reconstructed electrons or muons in the event
topology) and one-lepton search mode (one reconstructed electrons or muons) have highdiscovery potentials, and are especially considered for searches with early data.
Here, we investigate the contribution of the Standard Model and non-collision background to the event topology, especially without reconstructed electrons or muons (socalled the “No-lepton search mode” in ATLAS). Data-driven methods to estimate thebackground are developed. We also consider the systematic eﬀects (especially comingfrom the calorimeters) on the analysis, and show sensitivity to some of the benchmark
mSUGRA signals considered in ATLAS. The proﬁle of the benchmark SUSY signals are
mentioned in the next section.

12.1

Data-Driven Methods for Background Estimation

In order to perform successful searches for new physics (in this case, SUSY), the background from the Standard Model (SM) processes must be well understood. Since uncertainties of the cross sections of SM processes are quite large especially for multi-jet
events, and our knowledge is poor of the parton distribution functions, underlying eventand parton showering at the LHC energy scale. Also, knowledge of detector performance
will also be a large source of uncertainty, especially during the early data taking. Thus,

208

it is more reliable to predict the SM background from the real data as much as possible,
rather than fully relying on Monte Carlo simulations.
In data-driven methods, we make use of so called the “standard candles” or the“control samples,” which we have well understandings of the events, and extrapolate themin some ways to estimate background. For example, it is quite straightforward to tag
Z+jets events by requiring dilepton and its invariant mass near the Z mass. Due to thesame kinematic properties of Z(→ l+ l− )+jets and Z(→ ν ν̄)+jets, Z(→ ν ν̄) backgroundfor No-lepton search mode can be predicted from tagged events of Z(→ l+ l− )+jets byremoving the dilepton and apply the appropriate normalization factor.
The major advantages of data-driven methods are that we do not have to rely on
the Monte Carlo predictions including the shape of distributions and the normalization,and many of the crucial uncertainties of Monte Carlo simulations are disentangled indata-driven methods, including detector performance.
The data-driven methods are particularly important in early data analyses. Here, we
propose and validate data-driven methods to estimate major Standard Model backgroundfor the no-lepton search mode under the assumption of the integrated luminosity of 200
pb−1 .

12.2

Monte Carlo Simulation Samples and Data

For SUSY signals, we considered the ATLAS benchmark points for mSUGRA models.
The Monte Carlo simulation samples were generated by HERWIG with JIMMY’s multiparton interaction model. The mass inputs for the SUSY particles were derived from
Isasugra [144]. Table 12.2 shows the cross section and parameters of the benchmark
mSUGRA points in ATLAS.
The proﬁle of Monte Carlo simulation samples are mentioned in Appendix E. For electroweak process background such as Z/W + jets, the Monte Carlo generator Alpgen [145]was used with the HERWIG and JIMMY to accommodate parton showers and multiparton interaction. Alpgen treats the hard component of parton emission by explicitly
including it in the matrix element (up to ﬁve parton emissions by the matrix elements
are processed by Alpgen; see Appendix E), and the remaining soft parton emission iscovered by the parton shower. On top of it, the “matching” procedure was used to avoid
overlaps among samples with diﬀerent parton multiplicity, because a simple combinationof matrix element and the parton shower can “double count” the same event. Alpgenwith matching procedure is expected to better describe multi-jet processes compared tothe ones simply generated by the parton shower. tt̄ and QCD multi-jet processes wererespectively generated with MC@NLO and Pythia.

12.3

Trigger Menus for the Multi-jet Search

On top of “EF j70 xe30” trigger (mentioned in Section 11.3; it triggers for events withleading jet ET larger than 70 GeV and ETmiss larger than 30 GeV), unprescaled jet+ETmiss trigger “EF 2j42 xe30” and multi-jet triggers “EF 3J60” and “EF 4J45” are useful for the209Table 12.1: Cross section and proﬁle of the benchmark SUSY mSUGRA samples
MC Sample xsec [pb]
SU1
2.42
SU3
5.46
SU4
107.4
SU6
1.23
SU8
1.79

M0 [GeV]
70
100
200
320
210

M1/2 [GeV]
350
300
160
375
360

A0 [GeV]
0
-300
-400
0
0

tan β
10
6
10
50
40

sgn(μ)+++++

Table 12.2: Particle mass spectrum for the benchmark mSUGRA points [23]. The masses
are in GeV.
Particle
d˜L
ũL
b̃1
t̃1
d˜R
ũR
b̃2
t̃2
ẽL
ν˜e
τ̃1
ν˜τ
ẽR
τ̃2
g̃
χ̃01
χ̃02
χ̃03
χ̃04
χ̃+
1
χ̃+
2
h0
H0
A0
H+

SU1
764.90760.42697.90572.96733.53735.41
722.87749.46255.13238.31146.50237.56154.06256.98832.33136.98263.64466.44483.30262.06483.62115.81515.99512.39521.90SU3
636.27631.51575.23424.12610.69611.81610.73650.50230.45216.96149.99216.29155.45232.17717.46117.91218.60463.99480.59218.33480.16114.83512.86511.53518.15210SU4419.84412.25358.49206.04406.22404.92399.18445.00231.94217.92200.50215.53212.88236.04413.3759.84
113.48308.94327.76113.22326.59113.98370.47368.18378.90SU6870.79866.84716.83641.61840.21
842.16779.42797.99411.89401.89181.31358.26351.10392.58894.70149.57287.97477.23492.23288.29492.42116.85388.92386.47401.15SU8801.16797.09690.31603.65771.91773.69743.09766.21
325.44315.29151.90296.98253.35331.34856.45142.45273.95463.55479.01
274.30479.22116.69430.49427.74440.23

searches.

12.4

Event Selection for No-lepton Search ModeIn order to investigate on the no-lepton search mode, and start from the standard benchmark selection cuts used in ATLAS with slight modiﬁcations. The event selection is
mentioned below.
• No isolated electrons and muons• High jet multiplicity expected from SUSY signals by cascade decays ofsquarks and gluinos:– Cut 1: Number of jets > 3, Leading jet pT > 100 GeV– Cut 2: Second leading jet pT > 50 GeV– Cut 3: Third leading jet pT > 50 GeV• Large Emissfrom LSP, and Emiss/Meﬀ cut (Mef f = ETmiss +TT
the suppression of QCD background:3

jet(i)) for
i=1 pT– Cut 4: ETmiss > 100 GeV– Cut 5: ETmiss /Mef f > 0.2• Rejection of QCD background by the angular correlation of jets and
:
Emiss
T
– Cut 6: 0.3 < Δφ(leading jet, ETmiss ) < π − 0.3– Cut 7: 0.2 < Δφ(2nd leading jet, ETmiss ) < π − 0.2– Cut 8: 0.2 < Δφ(3rd leading jet, ETmiss ) < π − 0.2
The eﬀective mass Mef f is roughly proportional to the mass of produced SUSY particles [53]. The cut “ETmiss > 0.2Mef f ” is used to reject QCD background, because fakeETmiss tends to increase when jets’ energies are larger. The cuts on the angular diﬀerenceof jets and ETmiss are also used to suppress QCD background.
Figure 12.1 shows the ETmiss distributions of the SUSY signals and the Standard Modelbackground after each selection cut. The distributions of ETmiss and eﬀective mass areshown in Figure 12.2 for the SUSY signal and the Standard Model background. The
breakdown of number of events remaining after the SUSY event selection is shown in
Table 12.3 and 12.4. As is obvious from the tables, Z, W, tt̄, and QCD backgrounds
contribute almost equally to the no-lepton search, but in the high ETmiss region, QCD
background is less signiﬁcant compared to the other background.
In the next section, we investigate on the methods to estimate the background indata-driven ways.

211

6

× 10

SU3
SM BG
Top
W
Z
QCD

104
100
3

10
80
After Cut 1

602
10

Number of Events / 200 pb−1

Number of Events / 200 pb−1

6

200
108
180
107
160
106
140
105
120

40
10
20
1

200
108
180
107
160
106
140
105
120

× 10

SU3
SM BG
Top
W
Z
QCD

104
100
3
10
80

After Cut 2

602
10
40
10
20
1

0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

SU3
SM BG
Top
W
Z
QCD

104
100
3
10
80

After Cut 3

602
10
40
10
20
1
0

SU3
SM BG
Top
W
Z
QCD

16000
103
14000
12000
102
10000
8000
10
6000

After Cut 4

4000
1
2000
100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

Number of Events / 200 pb−1

20000
104
18000

SU3
SM BG
Top
W
Z
QCD

16000
103
14000
12000
102
10000
8000
10
6000

After Cut 5

4000
1
2000
0

20000
104
18000

Number of Events / 200 pb−1

× 10

0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

20000
104
18000

Number of Events / 200 pb−1

Number of Events / 200 pb−1

6

200
108
180
107
160
106
140
105
120

SU3
SM BG
Top
W
Z
QCD

16000
103
14000
12000
102
10000
8000
10
6000

After Cut 6

4000
1
2000
100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]
Number of Events / 200 pb−1

20000
104
18000
16000
103
14000
12000
102
10000
8000
10
6000

0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

SU3
SM BG
Top
W
Z
QCD

After Cut 7

4000
1
2000
0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

Figure 12.1: ETmiss distributions of SUSY signals and Standard Model background after
selection cuts assuming the integrated luminosity of 200 pb−1 .
212

Number of Events / 200 pb−1

103

SU3
SM BG
Top
W
Z
QCD

102

10
After All Cuts
1

Number of Events / 200 pb−1

0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

103

SU3
SM BG
Top
W
Z
QCD

102

10
After All Cuts
1
0

500

1000

1500
2000
2500
Effective Mass Meff [GeV]

Figure 12.2: ETmiss (top) and eﬀective mass (bottom) distributions after No-lepton Modeselection cuts for SUSY signals and Standard Model background.

213

Table 12.3: Breakdown of events after event selection of no-lepton search mode for integrated luminosity of 200 pb−1 .
CutZAfter Cut 1(1.04±0.01)×104
After Cut 2
8018± 90
After Cut 3
2971± 55
After Cut 4
1032± 32
After Cut 5
904 ± 30
After Cut 6
575 ± 24
After Cut 7
518 ± 23
After All Cuts
422 ± 21
All Cuts & ETmiss > 300
24 ± 5W(3.09±0.02)×104(2.45±0.02)×104
9203±962118±461801±421089 ± 33
979 ± 31801 ± 28
22 ± 5tt̄
9207±968471±925680±751604±401422±38841 ± 29725 ± 27606 ± 258±3

QCD(1.0678±0.0001)×108(9.722±0.001)×107(3.2943±0.0006)×107
6569± 813180± 56892 ± 30565 ± 24
148 ± 12—±—

Table 12.4: Breakdown of events after event selection of no-lepton search mode for integrated luminosity of 200 pb−1 .
CutAfter Cut 1
After Cut 2
After Cut 3
After Cut 4
After Cut 5
After Cut 6
After Cut 7
After All Cuts
All Cuts & ETmiss > 300 GeV

SU1SU3
246 ± 16 683±26
242 ± 16 668±26
185 ± 14 514±23174 ± 13 474±22
153 ± 12 412±20
98 ± 10 273 ± 17
92 ± 10 252± 16
77 ± 9 216 ± 15
51 ± 7 122 ± 11

214

SU4
(1.14±0.01)×104(1.10±0.01)×104
8600±935933±775102±713321± 58
2980 ± 55
2532 ± 25
246 ± 16SU6146±12144±12116±11110±1094±1059±854 ± 746 ± 732 ± 6SU8227±15222±15171±13162±13140±1290± 983 ± 971 ± 849 ± 7

Number of Events / 200 pb−1

3
5 × 10100
10Z(→ μ+ μ−)Z(→ τ+ τ−)
W(→ μ ν μ)
tt
SU3

90
4
10
80
70
103
60
502
10
40
10
30
20
1
10
10−1
60

70

80

90

100

110

120 130 140 150
Dimuon Mass [GeV]

Figure 12.3: Invariant mass of dimuons for integrated luminosity of 200 pb−1 .

12.5

Estimation of Background

Major background to the all-hadronic multi-jet search consists of Z(→ ν ν̄)+jets, W +jets,
tt̄, and QCD multi-jet events as mentioned in the previous section. Since the theoreticaluncertainty of multi-jet processes from perturbative QCD is still large, major backgroundshould be estimated by data-driven ways using well-known references. We will call suchwell-known references as “control samples.”12.5.1Z(→ ν ν̄)+jets Background

One of the straightforward ways to estimate Z(→ ν ν̄)+jets background is to use Z(→l+ l− )+jets events, where l’s are electrons or muons. Here, we use the muon channel forthe investigation. Z(→ μ+ μ− ) events are fairly background free once the invariant massof the dimuons are required (Figure 12.3). However, small contamination is expectedfrom tt̄ events and possibly SUSY signals. Contributions from Z(→ τ + τ − ), W (→ μνμ ),and QCD are negligible.
Z(→ ν ν̄) and Z(→ μ+ μ− ) events share the same kinematic properties, so tagging
Z(→ l+ l− )+jets events and calculating ETmiss without considering the dilepton will basically reproduce the distributions of Z(→ ν ν̄)+jets events. However, the identiﬁcationeﬃciency and acceptance eﬀects for the leptons in Z(→ l+ l− )+jets events should be takencare of, in order to correctly reproduce Z(→ ν ν̄)+jets background.
Two methods were considered in ATLAS within the approach mentioned above. One215

is fully using the Z(→ l+ l− )+jets events to reproduce Z(→ ν ν̄)+jets background. We
call this approach the “Replace Method.” This method was investigated by the author
for the ﬁrst time in ATLAS [A.3.4.1] [A.3.4.2]. The other method uses the shape of theZ(→ l+ l− )+jets Monte Carlo simulation, but uses normalization obtained from data tocope with the uncertainty of cross section [A.3.4.6] [A.3.4.7] [A.3.4.9]. We will describe
these method in more detail below.
Replace Method
In the former method, Z(→ μ+ μ− )+jets events are tagged and directly used for theestimation. Muon trigger “mu10” (pT > 10 GeV threshold; without prescale for low
luminosity running) was considered for the events. Z(→ μ+ μ− )+jets events are selectedby requiring two reconstructed leptons with invariant mass in the Z mass window (here81 < MZ < 101 GeV). When reconstructing ETmiss , the muons are removed from thecalculation in order to reproduce the distributions for Z(→ ν ν̄) background. Several
corrections are needed to reproduce Z(→ ν ν̄) distributions from Z(→ μ+ μ− ) events,
in order to recover the muon trigger eﬃciency, identiﬁcation eﬃciency and acceptanceeﬀects for the leptons . They can be obtained by the following way,
NZ(→l+ l− ) (ETmiss )BR(Z(→ ν ν̄))
× C(pT (Z)) ×
NZ(→ν ν̄) (ETmiss ) = .
+ l− ))trig
trig
BR(Z(→
lreco reco
1 − (1 − εl+ )(1 − εl+ ) εl+ εl−(12.1)
trig
First of all, the trigger eﬃciency εl should be considered. In this equation, εtriglis slightly diﬀerent from the standard deﬁnition of the trigger eﬃciency. Here, we needto know the eﬃciency that the trigger ﬁres for the events with a “reconstructed muon.”
The trigger eﬃciency for such conditions is shown in Figure 12.4 as a function of pT , η,and φ respectively. The dips in the trigger eﬃciency are coming from the gaps of muon
trigger chambers for η, and from the presence of eight-fold toroidal magnets for φ. Afterthe trigger eﬃciency correction, the identiﬁcation eﬃciency should also be considered(Figure 12.5). Regions with inferior eﬃciency are due to the same reason as the trigger
eﬃciency for φ and η. The decrease of eﬃciency in high pT is due to the isolation
conditions (transverse energy around ΔR < 0.1 from the muon is required to be lessthan 10 GeV). Both trigger and muon identiﬁcation eﬃciencies will be estimated fromdata by the tag-and-probe method [23] (will not be mentioned in this thesis).
C(pT (Z)) is a function of Z pT and corrects for the acceptance eﬀects of the leptons(pT >20 GeV, |η| <2.5) and kinematic eﬀects (invariant mass requirement for the dilepton). In this thesis, correction terms for the trigger eﬃciency, identiﬁcation eﬃciency,
and kinematic correction were inclusively considered (Figure 12.6).
Finally, branching ratios of Z(→ ν ν̄) and Z(→ l+ l− ) is considered.
Figure 12.7 shows the data-driven estimation of ETmiss and eﬀective mass distributions
from Z(→ ν ν̄) background.

216

Trigger Efficiency

1
0.8
0.6
0.4
0.2

Trigger Efficiency

00

50

100

150
200
250
300
Reconstructed Muon pT [GeV]

−2

−1

0

1
2
3
Reconstructed Muon η

−2

−1

0

1
2
3
Reconstructed Muon φ

1
0.8
0.6
0.4
0.2

Trigger Efficiency

0
−3

1
0.8
0.6
0.4
0.2
0

−3

Figure 12.4: Trigger eﬃciency of reconstructed muons as a function of muon pT (top), η(middle), φ (bottom).
217

Identification Efficiency

1
0.8
0.6
0.4
0.2

Identification Efficiency

00

50

100

150

200
250
300
MC Truth Muon pT [GeV]

−2

−1

0

1

2
3
MC Truth Muon η

−2

−1

0

1

2
3
MC Truth Muon φ

1
0.8
0.6
0.4
0.2

Identification Efficiency

0
−3

1
0.8
0.6
0.4
0.2
0

−3

Figure 12.5: Identiﬁcation eﬃciency of muons as a function of muon pT (top), η (middle),φ (bottom).
218

Efficiency

1
0.8
0.6
0.4
0.2
0
0

100

200

300

400

500
600
700
Truth Z pT [GeV]

Figure 12.6: Eﬃciency of Z reconstruction using dimuons as a function of pT .
Estimation using Monte Carlo Simulation Samples
The latter estimation method for Z(→ ν ν̄) background exploits the shape of distributions
from the Monte Carlo, in order to cope with the limited statistics of Z(→ l+ l− )+jets data.
The validation of Monte Carlo is preceded by comparing distributions with Z(→+ −l l ) data tagged by dilepton invariant mass. Once the Monte Carlo is tuned, and thenormalization factor needed to correct for the cross section uncertainty is also extracted.
Since, Monte Carlo is fully used except for the normalization, various uncertainties must be estimated from the data and considered. Uncertainties from the jets andETmiss (energy scale and resolution) have the largest impact on the background estimation.
An example of estimation for the case center-of-mass energy 14 TeV and 1 fb−1 integrated luminosity is shown in Appendix F. Estimation of ETmiss scale and resolution canalso be estimated from Z(→ l+ l− ) events, and are mentioned in Appendix F.

12.5.2W +jets Background

The largest component of W +jets background for no-lepton search mode comes from
W (→ τ ντ )+jets, where τ is decaying hadronically. The remaining contributions comefrom W (→ τ ντ )+jets with τ decaying leptonically and the lepton is not identiﬁed, or
W (→ eνe ) or W (→ μνμ )+jets with electrons or muons going out-of-acceptance or not
identiﬁed due to isolation criteria or etc. (Table 12.6).

219

Number of Events / 200 pb−1

10

1

10−1
0

Number of Events / 200 pb−1

Z(→ ν ν ) BG
Est. from Z(→ μ+ μ−)
Est. from Z(→ μ+ μ−) + SM BG
Est. from Z(→ μ+ μ−) + SM BG + SU3

102

100

200

300

400

500

600

700 800 900
Missing ET [GeV]

Z(→ ν ν ) BG
Est. from Z(→ μ+ μ−)
Est. from Z(→ μ+ μ−) + SM BG
Est. from Z(→ μ+ μ−) + SM BG + SU3

102

10

1

10−1
200

400

600

800

1000

1200 1400 1600 1800
Effective Mass Meff [GeV]

Figure 12.7: Estimation of Z(→ ν ν̄) background using Z(→ μ+ μ− ) events (top: ETmiss ,bottom: eﬀective mass). Estimations from pure Z(→ μ+ μ− ) (red), with other Standard
Model background (blue), and with other Standard Model background and SUSY signal
contamination (green) are shown.
220

Table 12.5: Number of events of Z(→ ν ν̄) background and data-driven estimation from
Z(→ μ+ μ− ).
BG & Estimation
Z(→ ν ν̄)
Estimation from Z(→ μ+ μ− )Est. from Z(μ+ μ− ) & SM BG
Est. from Z(μ+ μ− ) & SM BG & SU1 contami.
Est. from Z(μ+ μ− ) & SM BG & SU3 contami.
Est. from Z(μ+ μ− ) & SM BG & SU4 contami.
Est. from Z(μ+ μ− ) & SM BG & SU6 contami.
Est. from Z(μ+ μ− ) & SM BG & SU8 contami.

ETmiss > 100 GeV
406 ± 20388 ± 48426 ± 51429 ± 51434 ± 51476 ± 53428 ± 51427 ± 51

ETmiss > 300 GeV
24 ± 540 ± 1540 ± 1541 ± 1642 ± 1649 ± 1741 ± 1641 ± 16

Table 12.6: Origin of W background to no-lepton search mode.
Originτ within acceptanceτ out of acceptancee, μ within acceptance but unidentiﬁede, μ out of acceptance

Fraction
47.8 %
17.6 %
7.4 %
27.1 %

Data-driven Estimation
Since the largest component of W +jets background comes from the W (→ τ ντ ), thekinematics of those events are the same as W (→ eνe ) and W (→ μνμ )+jets with electrons
and muons identiﬁed. Such background with τ within the acceptance (pT > 20 GeV
and |η| <2.5) can be estimated from the following control samples. Here, we consider
two types of control samples with the following criteria (basically the same as no-lepton
search mode selection except for those related to leptons).
• Control Sample I– Requirement of one lepton in order to select W ’s with leptonic decays: Presence of one electron or muon with pT > 20 GeV and |η| <2.5, andpassing isolation criteria: ET (ΔR < 0.2) < 10 GeV– Avoidance of the signal region of the SUSY one-lepton search mode:transverse mass MT <100 GeV– Rejection of tt̄ events (W-puriﬁcation): no b-tagged jets
– SUSY selection cuts to match the kinematic properties of the control
sample as the no-lepton search mode background:221

Table 12.7: Number of events of W (→ τ ντ ) background with τ in the acceptance andestimation.
BG & Estimation
W (→ τ ντ ) No Lepton BG
Estimation from W (→ μνμ ) Control Sample
Est. from W with tt̄ contami.
Est. from W with tt̄ & SU1 contami.
Est. from W with tt̄ & SU3 contami.
Est. from W with tt̄ & SU4 contami.
Est. from W with tt̄ & SU6 contami.
Est. from W with tt̄ & SU8 contami.

ETmiss > 100 GeV ETmiss > 300 GeV
362 ± 19
13 ± 4346 ± 219±4397 ± 23
10 ± 4401 ± 23
13 ± 4402 ± 23
13 ± 4464 ± 25
14 ± 4399 ± 23
11 ± 4398 ± 23
11 ± 4∗ At least four jets with pT > 50 GeV and |η| <2.5, and at least one of themwith pT > 100 GeV∗ ETmiss > 100 GeV and ETmiss > 0.2Mef f∗ 0.3 < Δφ(jet1, ETmiss ) < π − 0.3, 0.2 < Δφ(jet2, ETmiss ) < π − 0.2,
0.2 < Δφ(jet3, ETmiss ) < π − 0.2• Control Sample II– Basically the same as Control Sample I, but the lepton is replaced as a “jet,”and included in the eﬀective mass calculation if it is among the three leading“jets.” The same event selection is applied other than the lepton-jet replacement.
Before using the information from b-tagging, the one lepton control samples mostly
consist of tt̄ with semi-leptonic decays and leptonically decaying W events. For theestimation of W background, the control samples should be puriﬁed into W-only samples.
Here, we use b-tagging to separate W and tt̄ events. Due to the presence of two b-jets in
tt̄ events and low presence of b-jets in W+jets events, the separation of W and tt̄ events
can be performed with high eﬃciency and rejection power (94% tt̄-rejection for 75% b-tag
eﬃciency).
Considering the lepton identiﬁcation eﬃciency, b-tagging eﬃciency, and eﬃciency
coming from the cut on the transverse mass of the one-lepton control samples, the nolepton W (→ τ ντ ) background with τ within the acceptance can be estimated with Control
Sample II (Figure 12.10).
Also, no-lepton background originating from W (→ eνe ) or W (→ μνμ ) with electrons
or muons within acceptance but not identiﬁed, can be estimated from Control Sample I
considering the lepton identiﬁcation eﬃciency, b-tagging eﬃciency, and eﬃciency (Figure 12.9).
For no-lepton background originating from leptons out of acceptance, informationfrom the Monte Carlo simulation is needed. The strategy to estimate such backgroundwill not be mentioned here.
222

Number of Events / 200 pb−1

102

W No−lepton BG (τ in acc.)
Est. from Pure W
Est. from Control Sample (w/o SUSY)
Est. from Control Sample (w SUSY)

10

1

Number of Events / 200 pb−1

0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]102

W No−lepton BG (τ in acc.)
Est. from Pure W
Est. from Control Sample (w/o SUSY)
Est. from Control Sample (w SUSY)

10

1

200

400

600

800

1000

1200 1400 1600 1800
Effective Mass Meff [GeV]

Figure 12.8: Estimation of W (→ τ ντ ) background using one-lepton control samples (top:ETmiss , bottom: eﬀective mass). Estimations from pure W (→ μνμ ) control samples (red),with tt̄ contamination (blue), and with tt̄ and SUSY signal contamination are shown.

223

Number of Events / 200 pb−1

102
W No−lepton BG (μ not−ID’ed)
Est. from Pure W
Est. from Control Sample (w/o SUSY)
Est. from Control Sample (w SUSY)
10

1

Number of Events / 200 pb−1

0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]102
W No−lepton BG (μ not−ID’ed)
Est. from Pure W
Est. from Control Sample (w/o SUSY)
Est. from Control Sample (w SUSY)
10

1

200

400

600

800

1000

1200 1400 1600 1800
Effective Mass Meff [GeV]

Figure 12.9: Estimation of W (→ μνμ ) no-lepton background using W (→ μνμ ) onelepton control samples (top: ETmiss , bottom: eﬀective mass). Estimations from pureW (→ μνμ ) control samples (red), with tt̄ contamination (blue), and with tt̄ and SUSY
signal contamination are shown.

224

Table 12.8: Number of events of W (→ μνμ ) no-lepton background with μ in the acceptance and estimation from one-lepton control samples.
BG & Estimation
W (→ μνμ ) No Lepton BG with μ in acc.
Estimation from W (→ μνμ ) Control Sample
Est. from W with tt̄ contami.
Est. from W with tt̄ & SU1 contami.
Est. from W with tt̄ & SU3 contami.
Est. from W with tt̄ & SU4 contami.
Est. from W with tt̄ & SU6 contami.
Est. from W with tt̄ & SU8 contami.

ETmiss > 100 GeV ETmiss > 300 GeV
29 ± 53±2
29 ± 31.0 ± 0.6
36 ± 3
1.2 ± 0.6
37 ± 3
1.6 ± 0.7
37 ± 3
1.8 ± 0.747 ± 42.0 ± 0.7
36 ± 3
1.4 ± 0.6
36 ± 3
1.3 ± 0.6

Estimation using Monte Carlo Simulation Samples
The Monte Carlo method used for Z(→ ν ν̄) background can also be expanded to theestimation of W +jets background. Since gluons in protons mainly contribute to themulti-jet processes associating W productions, the same normalization factor is expected
to be valid to apply on W +jets background (and it was conﬁrmed by investigating thedependence of Z/W+jets Monte Carlo on various parameters).

12.5.3tt̄+jets Background

Main component of tt̄+jets background is the semi-leptonic decay events where the leptons are tau’s most of which are decaying hadronically. The remaining tt̄+jets backgroundis due to the non-identiﬁed electron or muon [A.3.4.4].
The background can be estimated in a data-driven way using semi-leptonic tt̄+jets
events with an identiﬁed electron or muon. The one-lepton control samples with b-tagged
events can be used. The event selection for the tt̄ one-lepton control sample is shown
below.
• Control Sample Ib– Requirement of one lepton in order to select W ’s with leptonic decays: Presence of one electron or muon with pT > 20 GeV and |η| <2.5, andpassing isolation criteria: ET (ΔR < 0.2) < 10 GeV– Avoidance of the signal region of the SUSY one-lepton search mode:transverse mass MT <100 GeV– Rejection of W events (selection of tt̄ events): Presence of at least one
b-tagged jet– SUSY selection cuts to match the kinematic properties of the control
sample as the no-lepton search mode background:225

Table 12.9: Origin of tt̄ background to no-lepton search mode.
Originτ within acceptanceτ out of acceptancee, μ within acceptance but unidentiﬁede, μ out of acceptance

Fraction
53.4 %
20.9 %
8.5 %
17.1 %

Table 12.10: Number of events of tt̄ no-lepton background with τ inside the acceptanceand estimation from one-lepton control samples.
BG & Estimation
ETmiss > 100 GeV
tt̄ No-lepton BG with τ in acc.
286 ± 17
Estimation from tt̄ One-lepton Control Sample
219 ± 10
Est. from tt̄ with W contamination
253 ± 18
Est. from tt̄ with W & SU1 contami.
255 ± 18
Est. from tt̄ with W & SU3 contami.
259 ± 18
Est. from tt̄ with W & SU4 contami.
423 ± 18
Est. from tt̄ with W & SU6 contami.
255 ± 18
Est. from tt̄ with W & SU8 contami.
254 ± 18

ETmiss > 300 GeV
4±2
2±2
4±2
6±3
6±3
10 ± 45±3
5±3∗ At least four jets with pT > 50 GeV and |η| <2.5, and at least one of themwith pT > 100 GeV∗ ETmiss > 100 GeV and ETmiss > 0.2Mef f∗ 0.3 < Δφ(jet1, ETmiss ) < π − 0.3, 0.2 < Δφ(jet2, ETmiss ) < π − 0.2,
0.2 < Δφ(jet3, ETmiss ) < π − 0.2• Control Sample IIb– Basically the same as Control Sample Ib, but the lepton is replaced as a“jet,” and included in the eﬀective mass calculation if it is among the threeleading “jets.” The same event selection is applied other than the lepton-jet
replacement.
Here, we use these control samples for the estimation. Without the use of b-tagging,tt̄+jets events can also be selected by reconstructing top quark masses in the leptonic andhadronic parts [146]. Here, we will only mention the strategy with using the b-tagging.
The no-lepton tt̄ background from semileptonic decay with an electron or muon within
the acceptance but unidentiﬁed can be estimated with Control Sample Ib (Figure 12.10),
whereas the no-lepton tt̄ background from semileptonic decay with a τ within the acceptance can be estimated with Control Sample IIb (Figure 12.10).
226

Number of Events / 200 pb−1

102

t t No−lepton BG (τ in acc.)
Est. from Pure tt
Est. from Control Sample (w/o SUSY)
Est. from Control Sample (w SUSY)

10

1

Number of Events / 200 pb−1

0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]102

t t No−lepton BG (τ in acc.)
Est. from Pure tt
Est. from Control Sample (w/o SUSY)
Est. from Control Sample (w SUSY)

10

1

200

400

600

800

1000

1200 1400 1600 1800
Effective Mass Meff [GeV]

Figure 12.10: Estimation of tt̄(→ bb̄τ ντ qq) background using tt̄(→ bb̄μνμ qq) one-lepton
control samples (top: ETmiss , bottom: eﬀective mass).

227

12.5.4

QCD Multi-jet Background

QCD multi-jet events ideally do not have signiﬁcantly large ETmiss . Thus, QCD events
become background to the no-lepton search mode, only when those events have large
ETmiss originated due to some reason. The origins of large ETmiss in QCD background canbe ascribed to the following sources:• b-jets• Jet mis-measurement• Shower leakage• Detector problems (ETmiss formed from dead channels)
In either cases, peculiar feature of QCD background is that ETmiss is highly correlated tothe direction of a jet. After rejecting events with high angular correlation of jets andETmiss , the QCD background becomes very small in the interesting region for SUSY (setas ETmiss > 300 GeV in ATLAS).

12.5.5

Non-collision BackgroundNon-collision sources such as cosmic rays and beam halo particles can deposit large
amount of energy when traversing the calorimeters by hard bremsstrahlung as continuously mentioned in the previous chapters. However, it is not trivial how much of suchbackgrounds will actually contaminate the data and aﬀect the analyses during the collision data taking. Considering the fact that the calorimeter signals last over several
bunch crossings (O(100) ns) and the time resolution of L1Calo trigger is coarse and only
identiﬁes which bunch crossing (25 ns interval) the event belongs to, cosmic muons willbasically be triggered with the same rate as currently observed in cosmic ray runs [131][132] (Cosmic muons generate fake jets with pT >20 GeV at the rate of about 2 Hz).
Thus, the eﬀects from non-collision background can be grouped into two categories,• Non-collision sources ﬁring the trigger by themselves.
• Non-collision backgrounds overlapping collision events which have already been
triggered.
Air shower events can ﬁre the trigger, and also clear multi-jet event selections bythemselves without overlapping collision events.
The estimation of background originating from cosmic overlaps on collision events wasdone with the event overlay technique mentioned in Section 9.2. In order to estimate thecross section of overlap eﬀects, it was assumed that the cosmic muons have meaningful
eﬀects on the energy reconstruction of the calorimeters when they were within two bunch
crossings from the collision events (±50 ns). The amount of such background was estimated from this assumption and the cross section of the collision events. Due to the factthat the rate of high energy cosmic muons with a hard bremsstrahlung overlapping QCD
dijet events is rare, none of the cosmic-overlaid events passed the SUSY event selection.
228

SU3
SM BG
Cosmic+QCD

7

10

106
105
4

10

3

10

After Cut 1

102

Number of Events / 200 pb−1

Number of Events / 200 pb−1

108

10

106
5

10

104
3

10

After Cut 3

102

105
104
103
After Cut 2

102

1

SU3
SM BG
Cosmic+QCD

107

106

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]0

Number of Events / 200 pb−1

Number of Events / 200 pb−1

108

SU3
SM BG
Cosmic+QCD

107

10

1
0

108

10

104

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

SU3
SM BG
Cosmic+QCD

103
102
10

After Cut 4

1

1

104

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

SU3
SM BG
Cosmic+QCD

103
102
10

After Cut 50

Number of Events / 200 pb−1

Number of Events / 200 pb−1

0

1

104

102
10

After Cut 7

1
0

103
102
10

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

SU3
SM BG
Cosmic+QCD

103

SU3
SM BG
Cosmic+QCD

After Cut 6

10

Number of Events / 200 pb−1

Number of Events / 200 pb−1

0

104

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

104

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

SU3
SM BG
Cosmic+QCD

103
102
10

After All Cuts

1
100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

Figure 12.11: ETmiss distributions of SUSY signals and Standard Model background after
selection cuts assuming the integrated luminosity of 200 pb−1 .
229

Table 12.11: Breakdown of the Standard Model background and cosmic-QCD overlay
events after event selection of no-lepton search mode for integrated luminosity of 200
pb−1 .
Cut
SM BG
After Cut 1(1.0682 ± 0.0001)×108
After Cut 2(9.726 ± 0.001)×107
After Cut 3(3.2943 ± 0.0006)×107
After Cut 4(1.132 ± 0.001)×105
After Cut 57299 ± 85
After Cut 63389 ± 58
After Cut 72785 ± 53
After All Cuts
1971 ± 44

Cosmic+QCD
378 ± 19
122 ± 1136 ± 69±3
9±3
0.7 ± 0.9
0.5 ± 0.7
0.4 ± 0.6

With the same assumption on the event overlap, (0.4 ± 0.6) events/200 pb−1 are
expected in the multi-jet signature from the cosmic overlaps. Thus, the overlaps ofcosmic muons on QCD multi-jet events are almost negligible in No-lepton search mode,but even such background can be further rejected by the use of cleaning cuts mentioned
in Chapter 9.

12.6

Systematic Uncertainties

For Monte Carlo-based estimation, systematic uncertainties arise from vast amount ofcomponents. For purely Monte Carlo issues, the systematics originate from the renormalization scale, description of the parton distribution function (PDF), factorization scale,description of gluon radiation, hadronization and decays, and eﬀects from multi-parton
interactions. Some additional systematics arise in case of Alpgen related to the matchingprocedure. Some of the systematics mentioned above have previously been investigatedwith Z+jets Monte Carlo simulation samples [A.3.4.3], [A.3.4.5].
Detector and performance eﬀects such as the calorimeter response (which aﬀects theenergy scale and resolution of jets and ETmiss ), trigger eﬃciency and object reconstruction
and identiﬁcation eﬃciency also contribute to the systematics.
However, in case of data-driven estimation, most of the above systematics are disentangled. Needless to say the absence of Monte Carlo systematics, but many of the systematics from the detector performance are also absent, especially the jet and ETmiss systematics, since the output from data is directly used for the estimation. For data-driven
estimations, major uncertainties come from the methods themselves (e.g. presence of
background contamination in the control samples, uncertainty of extrapolations if any).
Here, we will review in Section 12.6.1 the source of systematics for each data-driven
estimation method mentioned in this chapter. The eﬀects of jet and ETmiss systematics
on the signal and background selection eﬃciency will be mentioned in Section 12.6.2.

230

Table 12.12: Systematic uncertainties expected for data-driven estimation of Z(→ ν ν̄)background for 200 pb−1 .
SourceMuon trigger eﬃciency
Muon identiﬁcation eﬃciency
Kinematic corrections
StatisticalEﬀects from BG & SUSY signals

12.6.1

Uncertainty
0.5 %2%
not considered
12.3 %
12.0 %

Systematics in Data-driven Methods

In this section, we investigate the systematic uncertainties from the data-driven methods
themselves.
Systematics of Z(→ ν ν̄) Background Estimation
First of all, for estimating Z(→ ν ν̄) background, the systematic uncertainty originatesfrom components in equation (12.1), the muon trigger eﬃciency, muon identiﬁcation eﬃciency, and kinematic correction for the acceptance eﬀects. The systematics from trigger
eﬃciency is quite small. It can be estimated from data using tag-and-probe method anduncertainty less than 0.5 % will be achieved with 50 pb−1 of integrated luminosity [23].
Here, we assume 0.5 % even for 200 pb−1 analyses, to be conservative. This uncertainty
should be considered separately for each muon pT bins, but here we considered uniform
uncertainty over various pT regions with tight assumption. Similar uncertainty is expected
for the reconstruction eﬃciency using the tag-and-probe method. Here, we assume overall uncertainty of reconstruction eﬃciency to be 2 % to be conservative (∼ 1 % expectedfrom previous studies [23] for 100 pb−1 in pT range of 20 to 70 GeV). The systematics ofC(pT (Z)) will not be considered here. The jet and ETmiss systematics do not contribute to
the uncertainty of the method itself, but only aﬀect the selection eﬃciency of Z(→ ν ν̄)background, and thus will not be considered here, but mentioned in Section 12.6.2. Thus,
the major systematic uncertainty is from the background contribution of tt̄ and SUSY
signals. For low mass SUSY case (SU4), the uncertainty becomes large. Here, we show
SU3 as the benchmark for the eﬀects of signal contamination in Table 12.12. Considering
the systematics mentioned here, the number of events remaining after no-lepton searchmode selection can be estimated by 17.3 % (and 37.9 % for the region of ETmiss > 300
GeV).
Systematics of W Background Estimation
For estimation W (→ τ ντ ) no-lepton background with τ in the acceptance, the systematics
of data-driven method arise from the lepton reconstruction eﬃciency, b-tag eﬃciency,
cut eﬃciency using the transverse momentum, eﬀects of substitution of the τ with a231

Table 12.13: Systematic uncertainties expected for data-driven estimation of W (→ τ ντ )background with τ in acceptance for 200 pb−1 .
SourceMuon trigger eﬃciency
Muon identiﬁcation eﬃciency
b-tagging eﬃciency
Selection eﬀ. using MT
StatisticalEﬀects from BG & SUSY signals

Uncertainty on BG
0.5 %
2%
3%
not considered but small
6.1 %
15.9 %

Table 12.14: Systematic uncertainties expected for data-driven estimation of W (→ μνμ )background with μ in acceptance but unidentiﬁed for 200 pb−1 .
SourceMuon trigger eﬃciency
Muon identiﬁcation eﬃciency
b-tagging eﬃciency
Selection eﬀ. using MT
StatisticalEﬀects from BG & SUSY signals

Uncertainty on BG
0.5 %
2%
3%
not considered but small
10.3 %
27.4 %

lepton, and background in the control sample. The uncertainty of cut eﬃciency withtransverse momentum is rather small and will not be considered here. We assume thesame systematics for the lepton reconstruction eﬃciency as mentioned above. The btagging eﬃciency will be estimated from tt̄ data with the precision of less than 5 % with
100 pb−1 integrated luminosity [23], thus we assume 5 % uncertainty. When the b-tag
eﬃciency ﬂuctuate by 5 %, the amount of tt̄ contamination change by less than 3 %.
Thus, we conservatively assume 5 % ﬂuctuation of contamination in the pure-W control
samples. We will not consider the systematics of substitution of τ ’s at the moment.
Under such assumption, the number of events remaining after no-lepton search mode
selection can be estimated by 17.4 % (and 62.9 % for the region of ETmiss > 300 GeV; notethe small statistics in this region; 13 events expected). Table 12.13 shows the uncertainty
from each contribution.
Similarly, the W (→ μνμ ) no-lepton background with the muon in acceptance but
unidentiﬁed, can be estimated by 29.5 % (and 100 % for the region of ETmiss > 300
GeV; note the small statistics in this region; 3 events expected). Table 12.14 shows theuncertainty from each component.

232

Table 12.15: Systematic uncertainties expected for data-driven estimation of tt̄(→bb̄τ ντ qq) background with τ in acceptance for 200 pb−1 .
SourceMuon trigger eﬃciency
Muon identiﬁcation eﬃciency
b-tagging eﬃciency
Selection eﬀ. using MT
Limitation of method
StatisticalEﬀects from BG

Uncertainty on BG
0.5 %
2%
3%
not considered but small
30.1 %
4.6 %
18.3 %

Systematics of tt̄ Background Estimation
For estimation tt̄(→ bb̄τ ντ qq) no-lepton background with τ in the acceptance, the same
systematics should be considered as the W (→ τ ντ ) no-lepton background (Table 12.15).
Since a deviation is seen for this data-driven method even for the pure tt̄ control samples,the discrepancy was included in the systematic uncertainty. This background will be
estimated by 36.0 % (and 245 % for the region of ETmiss > 300 GeV; 4 events expected).
The important component of the Standard Model background can be estimated with
data-driven method as mentioned in this chapter. For the remaining background (which
only dominate about 7 % in ETmiss > 300 GeV region) which were not estimated by thedata-driven method), we extract the numbers from the Monte Carlo to check the realistic
signiﬁcance mentioned in Section 12.7 with the normalization uncertainty of 20 %.
The overall number of events in the interesting region (ETmiss > 300 GeV region) after
the SUSY no-lepton search mode selection can be estimated as 67 ± 23 events (the actual
value is 54 ± 7) in case of the presence of SU3.

12.6.2

Systematics from Jet and ETmiss Performance

Various uncertainties from the performance of the detector and reconstructed objects
highly aﬀect the background and signal tagging eﬃciency. Here, we concentrate on theuncertainties arising from the calorimeters, namely those from jet and ETmiss energy scaleand resolution. We will not consider the systematics of jet algorithms themselves, such as
the splitting and merging eﬀects on reconstructed jets. The eﬀects of these uncertainties
will not propagate into the uncertainty in data-driven methods, but should be consideredfor Monte Carlo-based estimation and investigation on the selection eﬃciency of expected
SUSY signals.
The performance of jets and ETmiss will be validated with early data, and their energy
scale and resolution will be estimated.
The energy scale of jets will be evaluated with the pT balance of γ+jets and Z+jets
events, which were adopted at the Tevatron as well. In the previous studies in ATLAS,
233

the jet energy scale is expected to be calibrated and evaluated within about 1 % with
100 pb−1 except for very low pT jets and up to 300-500 GeV range [23][147]-[150]. Here,
we investigate two cases, a realistic and slightly conservative case of 2 % uncertainty anda pessimistic case of 5 %.
The energy resolution of jets can be estimated in-situ using the pT balance of dijet
events. About 1 % precision can be achieved with 100 pb−1 for an η bin size of 0.1 andup to about 500 GeV of pT [23] [151]. Here, uncertainty of a realistic case (2 %) and a
pessimistic case (5 %) were assumed and investigated.
The performance of ETmiss will be validated with data. Minimum Bias and QCD
multijet events allow for the evaluation of ETmiss resolution. The estimation of ETmiss scale
performance will be done with Z(→ e+ e− ) and Z(→ μ+ μ− ) by replacing the dilepton,
Z(→ τ + τ − ) by measuring the invariant mass of the τ ’s with collinear approximation of
the emitted ντ , W (→ lν) by using the shape of the transverse mass, and tt̄ events withsemileptonic decays using the invariant mass. These events allow for the estimation ofETmiss performance with various event topologies. ETmiss scale is expected to be estimatedwithin about 1 - 2 % with 200 pb−1 data [23]. Here, we considered 2 % and 5 % ETmiss scale
uncertainty for the systematics studies. For ETmiss resolution, we adopted 5 % and 10 %uncertainty for the studies.
Although the four systematics mentioned here are highly related and not independent(especially the jet and ETmiss scale), we considered each eﬀect separately and also totally(with jet and ETmiss scale correlating and shifting in the same direction with the sameratio).
Investigation on the Selection Eﬃciencies
Eﬀects of jet and ETmiss systematics on the selection eﬃciency were investigated here. Theenergy scale were simply forced to shift by ±2% or ±5 % (thus four sets of trials) in theoﬄine analysis to see their eﬀects. The impact of degraded resolution was consideredhere by independent smearing the jet px and py by a Gaussian of 2 or 5 % spread (sothe direction was also smeared accordingly). The systematics of ETmiss was treated in theoﬄine analysis in the same way.
Table 12.16 and 12.18 show the breakdown of number of the Standard Model background and SUSY signal events remaining after the no-lepton search mode event selection
with the consideration of various jet and ETmiss systematics mentioned above. For “Overall Scale Up (2)” and “Overall Scale Down (2)” in the tables, the jet and ETmiss scale
was treated in a correlated way; shifted in the same direction with the same amount ±2 % (± 5 %), and also the resolution was degraded by 2 % (5 %) for jets and 5 % (10%) for ETmiss (here, the resolution was degraded for both the “Up” and “Down” cases).
Table 12.17 and 12.19 show the uncertainties from the overall systematics just mentioned.
The impact of the uncertainties of jet and ETmiss resolution is rather small (about 1 %)for both the background and SUSY signals. The uncertainty is slightly larger for the low
mass case (SU4). On the other hand, the outcome of shifts in the energy scale of jets andETmiss is quite large for the Standard Model background (about 10 %). Note that QCD
background mostly originates from fake ETmiss , and thus it is from the tail of the jet andETmiss performance, which explains the small sensitivity to the jet and ETmiss systematics
234

Table 12.16: Breakdown of events after event selection of no-lepton search mode for
integrated luminosity of 200 pb−1 . Eﬀects of jet and ETmiss systematics were investigated.
Cut
After All Cuts
Jet Scale +2%
Jet Scale +5%
Jet Scale -2%
Jet Scale -5%
Jet Reso +2%
Jet Reso +5%
ETmiss Scale +2%
ETmiss Scale +5%
ETmiss Scale -2%
ETmiss Scale -5%
ETmiss Reso +5%
ETmiss Reso +10%
Overall Scale Up
Overall Scale Up 2
Overall Scale Down
Overall Scale Down 2ZW
422 ± 21 801 ± 28
440 ± 21 840 ± 29
471 ± 22 896 ± 30
407 ± 20 764 ± 28
380 ± 20 716 ± 27425 ± 21 801 ± 28
421 ± 21 804 ± 28
436 ± 21 836 ± 29
460 ± 21 890 ± 30
406 ± 20 764 ± 28
384 ± 20 707 ± 27424 ± 21 808 ± 28
427 ± 21 815 ± 29
461 ± 21 886 ± 30
514 ± 23 1006 ± 31
391 ± 20 735 ± 27
347 ± 19 640 ± 25tt̄
606 ± 25630 ± 25668 ± 26582 ± 24545 ± 23605 ± 25605 ± 25638 ± 25681 ± 26575 ± 24529 ± 23610 ± 25615 ± 25665 ± 26754 ± 27555 ± 24
485 ± 22QCD
148 ± 12150 ± 12112 ± 11153 ± 12153 ± 12150 ± 12115 ± 11153 ± 12153 ± 12150 ± 12112 ± 11150 ± 12186 ± 14150 ± 12227 ± 15150 ± 12154 ± 12

Table 12.17: Uncertainty of selection eﬃciency from jet and ETmiss systematics for theStandard Model background, derived from the numbers in the “Overall Scale” rows inTable 12.16.
Cut
Uncertainty
Uncertainty (pessimistic)

Z

W

tt̄

QCD

+9.2%
−7.3%
+21.8%−17.8%+10.6%−8.2%+25.6%−20.1%+9.7%−8.4%+24.4%−20.0%+1.4%−—%+53.4%−—%

235

Table 12.18: Breakdown of SUSY signal events after event selection of no-lepton searchmode for integrated luminosity of 200 pb−1 . Eﬀects of jet and ETmiss systematics were
investigated.
Cut
Default
Jet Scale +2%
Jet Scale +5%
Jet Scale -2%
Jet Scale -5%
Jet Reso +2%
Jet Reso +5%
ETmiss Scale +2%
ETmiss Scale +5%
ETmiss Scale -2%
ETmiss Scale -5%
ETmiss Reso +5%
ETmiss Reso +10%
Overall Scale Up
Overall Scale Up 2
Overall Scale Down
Overall Scale Down 2

SU1
77.7 ± 8.8
77.6 ± 8.8
77.7 ± 8.8
77.7 ± 8.8
77.5 ± 8.8
78.0 ± 8.8
78.4 ± 8.9
78.4 ± 8.9
79.3 ± 8.9
76.6 ± 8.8
75.2 ± 8.7
77.7 ± 8.8
77.2 ± 8.8
78.7 ± 8.980.1 ± 8.9
77.1 ± 8.8
75.6 ± 8.7

SU3215.9 ± 14.7
216.7 ± 14.7
218.6 ± 14.8
216.6 ± 14.7
213.5 ± 14.6
215.4 ± 14.7
215.4 ± 14.7
218.6 ± 14.8
221.2 ± 14.9
214.3 ± 14.6
210.3 ± 14.5
216.2 ± 14.7
216.2 ± 14.7
218.9 ± 14.8
223.7 ± 15.0
213.0 ± 14.6
208.3 ± 14.4

SU42534.4 ± 50.3
2556.2 ± 50.6
2583.7 ± 50.8
2503.8 ± 50.0
2451.2 ± 49.5
2534.4 ± 50.3
2524.0 ± 50.2
2583.6 ± 50.8
2649.0 ± 51.5
2482.1 ± 49.8
2397.9 ± 49.0
2534.1 ± 50.3
2533.8 ± 50.3
2603.9 ± 51.0
2697.4 ± 51.9
2452.1 ± 49.5
2325.4 ± 48.2

SU6
46.4 ± 6.8
46.4 ± 6.8
46.1 ± 6.8
46.4 ± 6.8
46.5 ± 6.8
46.2 ± 6.8
45.8 ± 6.8
46.9 ± 6.8
47.6 ± 6.9
45.9 ± 6.8
45.2 ± 6.7
46.2 ± 6.8
46.0 ± 6.8
46.7 ± 6.8
46.9 ± 6.8
45.7 ± 6.8
44.6 ± 6.7

SU871.2 ± 8.4
71.4 ± 8.5
71.2 ± 8.4
71.2 ± 8.4
71.4 ± 8.4
71.2 ± 8.4
70.7 ± 8.4
71.8 ± 8.5
72.8 ± 8.5
70.8 ± 8.4
69.7 ± 8.3
70.9 ± 8.4
71.4 ± 8.4
71.4 ± 8.5
72.7 ± 8.5
70.1 ± 8.4
69.1 ± 8.3

Table 12.19: Uncertainty of selection eﬃciency from jet and ETmiss systematics for the
SUSY signals, derived from the numbers in the “Overall Scale” rows in Table 12.18.
Cut
Uncertainty
Uncertainty (pessimistic)

SU1

SU3

SU4

SU6

SU8

+1.3%
−0.8%
+3.1%
−2.7%

+1.4%
−1.3%
+3.6%
−3.5%

+2.7%
−3.2%
+6.4%
−8.2%

+0.6%
−1.5%
+1.1%
−3.9%

+0.3%
−1.5%
+2.1%
−2.9%

236

Number of Events / 200 pb−1

Default
Scale Up 2
Scale Up
Scale Down
Scale Down 2

102

10
SU3
1

0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]

Figure 12.12: Eﬀects of jet and ETmiss systematics on ETmiss distribution of SU3 signals
under the integrated luminosity of 200 pb−1 . The deﬁnition of systematics assumed is
mentioned in the text.
considered here. For QCD background, special consideration is needed to consider theeﬀects from the tails of jet and ETmiss performance, but we will not consider this issue inthis thesis.
For SUSY signals, the uncertainty of selection eﬃciency arising from jet and ETmiss systematics is small. This is due to the fact that many of the signal events are well above
the selection thresholds, and not signiﬁcantly inﬂuenced by the shifts in jets’ transverse
momenta and ETmiss . Thus, for SUSY signals more important uncertainties originate
from the Monte Carlo systematics which directly aﬀect the cross section. Figure 12.12
shows the ETmiss distribution from the default condition and with systematic shifts in the
jet and ETmiss performance.

12.7

Background Subtraction and Signal SigniﬁcanceThe estimation of the Standard Model background was performed in the previous sections with considerations on the systematic eﬀects. Figure 12.13 and 12.14 show ETmiss andeﬀective mass distributions for the Standard Model background, estimated backgroundwith systematic uncertainties considered, and the total background and signals (uncertainties of the signal selection eﬃciency were included). Here, we assumed that the overall
normalization of the background was the same as what we have in the Monte Carlo simulation samples. For SU3, an excess is observed in the tails. On the other hand, for SU4,237

an excess is observed in almost all the range of distributions due to the low mass scaleof SUSY. The background is now estimated, and can be subtracted from the observed
distributions.
Some approaches can be considered for measurements in inclusive analyses; one is themeasurement of the peak of eﬀective mass distribution, and the other is simply counting
the excess of the number of events from the background. Here, we brieﬂy investigate on
both approaches.

12.7.1

Eﬀective Mass Distributions

When an excess is observed, the peak of Mef f distribution will give some indications on
the produced SUSY particles, because the peak is known to be roughly proportional to
MSU SY (= min(g̃, ũR )) [53]. Figure 12.15 shows the eﬀective mass distribution in case of
SU4 signals for a case of an ideal background estimation (perfect background subtraction
with only statistical uncertainty) and more realist background subtraction with datadriven methods and systematic uncertainties considered. The peak of the eﬀective massfor SU4 was 573 ± 6 GeV for ideal case and 591 ± 28 GeV in this study, and thus
reproducing the actual value within the uncertainty (Figure 12.15). For SU3 case, thepeak was measured as 988 ± 135 GeV (929 ± 24 GeV for ideal case). For other benchmark
points, we need more statistics to measure the peak of the eﬀective mass.

12.7.2

Number Counting Approach

An excess from the expected background should ﬁrst of all be considered whether it issigniﬁcant or not. It will eventually allow for the measurement of the cross section oncea model is assumed with a careful treatment on the signal contamination in background,
but we will not investigate this issue here. Comparing the excess in various SUSY searchmodes will allow us to narrow down the possible SUSY models matching the observation.
Here, we will investigate how much signiﬁcance is expected in the no-lepton search mode
for the benchmark SUSY signals for 200 pb−1 with realistic background estimation.
Here, we adopt the deﬁnition of signal signiﬁcance used in the previous studies [23].
Considering the Poisson statistics due to the feature of rare events, the probability p toobserve N events or above can be expressed as, ∞p=A

db G(b; Nb , δNb )0∞
e−b bii=Ni!

,(12.2)

where A is the normalization factor to set the total probability to unity, b is the expected
number of events for the Poisson distribution, G is a Gaussian with mean Nb and standard
deviation of δNb . Finally, the probability can be converted to the signal signiﬁcance Zσ,
which Z is expressed as,√(12.3)
Z = 2 erf−1 (1 − 2p) .
The uncertainty of the signal selection eﬃciency coming from jet and ETmiss systematics
were considered in the calculation as well.
238

Number of Events / 200 pb−1

SM BG+SUSY
SM BG
Est. BG

103

102

10

SU3

1

Number of Events / 200 pb−1

0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]SM BG+SUSY
SM BG
Est. BG

103

102

10

SU3

1
0

500

1000

1500
2000
2500
Effective Mass Meff [GeV]

Figure 12.13: ETmiss and eﬀective mass distribution of SU3 signals and Standard Modelbackground for the integrated luminosity of 200 pb−1 .

239

Number of Events / 200 pb−1

SM BG+SUSY
SM BG
Est. BG

103

102

10

SU4

1

Number of Events / 200 pb−1

0

100 200 300 400 500 600 700 800 900 1000
Missing ET [GeV]SM BG+SUSY
SM BG
Est. BG

103

102

10

SU4

1
0

500

1000

1500
2000
2500
Effective Mass Meff [GeV]

Figure 12.14: ETmiss and eﬀective mass distribution of SU4 signals and Standard Modelbackground for the integrated luminosity of 200 pb−1 .

240

Number of Events / 200 pb−1

800
Ideal Subt.
Realistic Subt.

700
600
500
400

SU4

300
200
100
200

400

600

800

1000
1200
Effective Mass Meff [GeV]

Figure 12.15: Eﬀective mass distribution of SU4 signals for the integrated luminosity of
200 pb−1 .
When calculating the signiﬁcance for each mSUGRA benchmark points, the numberof events were counted after the no-lepton search mode event selection, and an additional
threshold on the ETmiss or eﬀective mass. Figure 12.16 and 12.17 show the signiﬁcance forvarious threshold on ETmiss or eﬀective mass. We adopted the threshold with the maximum
signiﬁcance for each benchmark points. The signal signiﬁcance of benchmark mSUGRA
points are shown in Table 12.20 for ideal conditions (only statistical errors considered),
relatively realistic condition with systematic errors considered but ideal subtraction of
the background (so this may show a realistic limit of the discovery potential with thestandard event selection), and ﬁnally the one from the data-driven methods. The numbersin the parentheses show the signiﬁcance with the K-factor of 1.3 considered for the SUSY
processes. Note that K-factors are always considered for the Standard Model processes
in this section, except for the QCD multi-jet events. The discovery reach is reduced
under realist conditions, but some of the SUSY signals can still be reached or manifest
themselves in a prominent way with the integrated luminosity of 200 pb−1 .

241Signal Significance10
9Stat. Err. Only
Ideal Subtraction
Data-Driven

8
7
6
5
4

SU3

3
2
1
0
100

150

200

250

300

350
400
450
500
Missing E Threshold [GeV]

Signal Significance

T

10
9

Stat. Err. Only
Ideal Subtraction
Data-Driven

8
7
6
5
4

SU3

3
2
1
0

400

600

800

1000
1200
1400
Effective Mass Threshold [GeV]

Figure 12.16: Signiﬁcance of the SUSY signal SU3 scanned over various ETmiss and Mef fthresholds for the integrated luminosity of 200 pb−1 .

242Signal Significance35
Stat. Err. Only
Ideal Subtraction
Data-Driven

30
25
20
15

SU4

10
5
0
100

150

200

250

300

350
400
450
500
Missing E Threshold [GeV]

Signal SignificanceT

35
Stat. Err. Only
Ideal Subtraction
Data-Driven

30
25
20
15

SU4

10
5
0

400

600

800

1000
1200
1400
Effective Mass Threshold [GeV]

Figure 12.17: Signiﬁcance of the SUSY signal SU4 scanned over various ETmiss and Mef fthresholds for the integrated luminosity of 200 pb−1 .

243

Table 12.20: Signiﬁcance of mSUGRA benchmark signals for integrated luminosity of 200
pb−1 . Numbers in the parentheses are after considering the K-factor of SUSY processes.
Cut
SU1
SU3
SU4
SU6
SU8
Statistical Error Only
4.4 (5.4) 8.2 (10.1) 34.1 (—) 3.2 (3.9) 4.2 (5.2)
Sys.Err. & Ideal BG Subtraction 2.4 (2.8) 4.2 (4.8) 6.6 (6.8) 1.8 (2.1) 2.4 (2.9)
Sys.Err. & Data-Driven BG Sub. 1.7 (2.0) 3.5 (4.1) 6.0 (6.2) 1.3 (1.6) 1.9 (2.3)

244Chapter 13
Conclusions
The Large Hadron Collider (LHC) is the ﬁrst collider to probe the physics at the TeVenergy scale. Undiscovered weakly-interacting particles from Supersymmetric or extra
dimensional models are good candidates of the dark matter in the Universe, and they
may be copiously produced in the LHC collisions.
In this thesis, we considered model-independent searches for physics beyond the Standard Model by event topologies with jets and missing transverse energy (ETmiss ). Since
jets and ETmiss mostly depend on the calorimeter performance, the ﬁrst half of this thesis
was devoted to the studies of the ATLAS Tile and Liquid Argon calorimeters with thereal commissioning data. The latter half of this thesis described the search strategies
for the Large Extra Dimensions with monojet signature and R-parity conserving Supersymmetry with “multi-jet + ETmiss ” signature under realistic conditions of early data
analysis.
The commissioning of the ATLAS Tile and Liquid Argon Calorimeters were performed
with the cosmic data recorded from October 2007 to the Fall of 2008.
The electronic noise was measured in-situ with the random stream data for the ﬁrst
time, in 2008. Liquid Argon calorimeters showed consistency with the expectation,
whereas the TileCal showed η-dependent noise and non-Gaussian feature which were
not expected and were not treated in the previous reconstruction algorithms. The strategy to cope with such noise feature was developed in order to achieve good energy andETmiss resolution, and meet the expected performance.
With cosmic ray data, the energy deposits in the calorimeters were investigated.
High energy events were observed even after removing the eﬀects from the identiﬁed
hot channels. TeV-energy deposits in the calorimeters were observed at the rate of afew events per hour. Cosmic Monte Carlo simulation was used to investigate the causeof such high energy events. The energy distribution was well described by the cosmic
Monte Carlo simulation, and the origin of the high energy events was conﬁrmed as mostly
coming from the bremsstrahlung of the cosmic muons.
The eﬀects of cosmic muons on physics measurements were investigated. High energy
deposits in the calorimeters from non-collision sources often originate fake “jets” andETmiss , and will become background to almost all physics measurements using calorime-245ter information. The strategies to remove or reject such background were proposed byintroducing so called the “cleaning cuts” by discriminant variables such as the jet EM
fraction, the number of associated tracks to jets, and number of clusters in jets. For
pure cosmic events, those variables reject more than 90 % of the cosmic background, still
keeping high eﬃciency (more than 90 %) for real collision signals.
The strategies were further validated with the “event-overlay” technique. It was
conﬁrmed that the proposed cleaning techniques were valid even under the condition
when the cosmic particles overlap the collision event such as the minimum bias or the
QCD dijet events.
Finally, strategies to perform model-independent searches with jets and ETmiss wereconsidered using Monte Carlo simulations.
Strategies with monojet signatures were considered. The expected rate of such noncollision backgrounds was investigated for the collision data taking conditions, and theircontributions in various event topologies were estimated. In monojet topology, a signal
from the Large Extra Dimension model was used as a benchmark. Without any cleaning
cuts, cosmic background is far greater than any other background or signals, but withthe use of cleaning cuts, its contribution becomes under control. The eﬀect of cosmic ray
overlapping large cross section events such as minimum bias or QCD dijet events is not
signiﬁcant in dijet or multi-jet event topology, but air shower events can still contributeas a background. However, even those events can be rejected by the proposed cleaning
cuts.
Searches with multi-jet plus ETmiss signatures were considered for R-parity conserving
SUSY models. Data-driven methods to estimate the Standard Model background were
proposed and their performance and uncertainty were investigated. We found that signal
signiﬁcance is much degraded in realistic conditions, but even under such cases, someregions of SUSY parameters can be reached by the use of data-driven methods with the
integrated luminosity of 200 pb−1 .

246

Appendix A
Contributions in ATLAS
Publications and Presentations
All the items are shown from the latest to the oldest except for A.3.4 (from the oldest to
the latest for each research topic).

A.1

Publications

A.1.1

Articles

1. ATLAS Collaboration, “Readiness of the ATLAS Liquid Argon Calorimeter for
LHC Collisions,” submitted to EPJC.
2. ATLAS Collaboration, “The ATLAS Experiment at the CERN Large Hadron Collider”, JINST 3 S08003, 2008
3. ATLAS Collaboration, “‘Expected Performance of the ATLAS Experiment Detector, Trigger and Physics,” CERN-OPEN-2008-020, 2008, arXiv:0901.0512(important contributions in “Jets and Missing Transverse Energy” and “Supersymmetry” chapters)

A.1.2

ATLAS Note

1. H. Okawa et al., “Calorimeter Commissioning With Cosmic Rays: Cells, Clusters,
Jets and Missing ET ,” ATLAS Note, ATL-COM-CAL-2008-007, 2008

A.1.3

Proceedings

1. H. Okawa on behalf of the ATLAS Tile Calorimeter Group, “Commissioning of the
ATLAS Tile Calorimeter with Cosmic Ray & Single Beam Data”, IEEE Nuclear
Science Symposium Conference Record, p.3394, 2008247

2. H. Okawa et al., “Calorimeter Commissioning of the ATLAS Detector with Cosmic
Ray Data,” Proceedings of Physical Society of Japan (JPS) Meeting, Vol. 63, 1-1,
p.30 (2008) (in Japanese)
3. H. Okawa et al., “Studies of Validation Method of Missing ET using Real Data with
the ATLAS Detector,” Proceedings of Physical Society of Japan (JPS) Meeting,Vol. 62, 2-1, p.28 (2007) (in Japanese)
4. H. Okawa et al., “Estimation of Background to Supersymmetric Events at the ATLAS Experiment,” Proceedings of Physical Society of Japan (JPS) Meeting, Vol.
62, 1-1, p.21 (2007) (in Japanese)
5. H. Okawa, “Supersymmetric Particle Search with the ATLAS Detector at the LHC”,
Abstract Collection of 2nd COE RA Young Researcher Exchange Symposium, theUniversity of Tokyo, 21 Century COE QUESTS, p.6, 2007 (in Japanese)

A.2

Public Plots

1. Noise plots in the Approved Tile Calorimeter Plots webpage.
https://twiki.cern.ch/twiki/bin/view/Atlas/ApprovedPlotsTile
2. Jet/Etmiss plots from the cosmic ray runs. All the plots in
https://twiki.cern.ch/twiki/bin/view/Atlas/ApprovedCosmicPlotsJetEtMiss

A.3

Presentations

A.3.1

International Conference Presentations

1. H. Okawa on behalf of the ATLAS Collaboration, “Determination of backgroundfrom W/Z and top. What do we need to ﬁnd SUSY” LHC and Dark Matter Workshop, Ann Arbor, USA, Jan. 6-10, 2009
2. H. Okawa on behalf of the ATLAS Collaboration, “Commissioning of the ATLAS
Tile Calorimeter with Cosmic Ray & Single Beam Data”, IEEE Nuclear Science
Symposium, October 24, 2008 the International Congress Center Dresden, Dresden,
Germany

A.3.2

ATLAS Workshop Presentations

1. H. Okawa, “Jets & Missing ET Quality Studies with Cosmic Ray & Single BeamData,” ATLAS Performance and Physics Workshop - Jet/Missing ET Performance
Session, CERN, Switzerland, Nov. 5, 2008
2. H. Okawa et al., “Jets & Missing ET in Cosmic Ray and Single Beam Data,” ATLAS Performance and Physics Workshop - Supersymmetry Working Group Session,
CERN, Switzerland, Oct. 3, 2008248

3. H. Okawa et al., “Jets & Missing ET Studies with Cosmic Ray & Single BeamData,” ATLAS Jet/EtMiss Reconstruction Performance Workshop, CERN, Switzerland, Sep. 26, 2008
4. H. Okawa, “Calorimeter Commissioning with Cosmic Rays Cells, Cluster, Jets
& Missing ET ,” ATLAS Performance and Physics Workshop - Jet/Missing ET
Performance Session, CERN, Switzerland, Aug. 26, 2008
5. H. Okawa, “Topocluster Performance Studies in M5 Runs”, ATLAS Hadronic Calibration Workshop, March 15, 2008 Double Tree Hotel, Tucson, USA
6. H. Okawa et al., “Missing ET in Early Data”, ATLAS Hadronic Calibration Workshop, March 15, 2008 Double Tree Hotel, Tucson, USA
7. H. Okawa (presented by the convenors), “Calorimeter Noise Measurement & Topocluster Performance,” ATLAS Hadronic Calibration Workshop, June 24, 2009 Lisbon,
Portugal
8. H. Okawa (presented by the convenors), “Commissioning of Jet/ETmiss with Cosmic
Data & Their Clean-up,” ATLAS Hadronic Calibration Workshop, June 26, 2009
Lisbon, Portugal

A.3.3

National Meetings and Symposiums

1. H. Okawa et al., “Calorimeter Commissioning of the ATLAS Detector with Cosmic
Ray Data,” Physical Society of Japan (JPS) Meeting, Kinki University, Osaka,
Japan, Mar. 26, 2008
2. H. Okawa et al., “Studies of Validation Method of Missing ET using Real Datawith the ATLAS Detector,” Physical Society of Japan (JPS) Meeting, Hokkaido
University, Sapporo, Japan, Sep. 24, 2007
3. H. Okawa et al., “Estimation of Background to Supersymmetric Events at the ATLAS Experiment,” Physical Society of Japan (JPS) Meeting, Tokyo Metropolitan
University, Hachioji, Japan, Mar. 27, 2007.
4. H. Okawa, “Supersymmetric Particle Search with the ATLAS Detector at the LHC”,
2nd COE RA Young Researcher Exchange Symposium, the University of Tokyo,
Tokyo, Japan, Nov. 6, 2006 (both oral and poster presentations)

A.3.4

ATLAS Internal Presentations

Standard Model Background Studies for Supersymmetric Particle Search
1. H. Okawa et al., “Z → νν BG Estimations using Real Data”, ATLAS Trigger andPhysics Week SUSY Working Group Meeting, May 31, 2006249

2. H. Okawa et al., “Z → νν BG Estimations with Data (Full Simulation Studies)”,
ATLAS SUSY Working Group Meeting, July 20, 2006
3. H. Okawa et al., “Uncertainties of the Current Estimations of Z → νν BG”, ATLASSUSY Working Group Meeting, September 13, 2006
4. H. Okawa et al., “No Lepton Mode BG Estimations with Data”, ATLAS Week
SUSY Working Group Meeting, October 4, 2006
5. H. Okawa et al., “Z→ll Systematic Errors”, ATLAS SUSY Working Group Phone
Meeting, October 25, 2006
6. H. Okawa et al., “BG Estimation in No Lepton Mode & Dilepton Mode”, ATLAS
Trigger and Physics Week SUSY Working Group Meeting, November 1, 2006
7. H. Okawa et al., “Estimation of Z/W BG in No Lepton Mode”, ATLAS SUSY
Working Group Meeting, January 17, 2007
8. H. Okawa et al., “Summary of Comparison on Dilepton Mode”, ATLAS SUSY
Working Group CSC 1 & 2 Meeting, March 7, 2007
9. H. Okawa et al., “MC Method for No Lepton Mode BG & Summary on Dilepton
Mode Studies”, ATLAS Trigger and Physics Week, March 21, 2007

Missing ET Studies with Minimum Bias and W Events
10. H. Okawa et al., “Missing ET Commissioning using W Events (Template Method)”,
ATLAS JetEtMiss Working Group Phone Conference, July 25, 2007
11. H. Okawa et al., “Etmiss CSC note meeting”, ATLAS JetEtMiss Working Group
Etmiss CSC Note Meeting, August 29, 2007
12. H. Okawa et al., “Missing ET Commissioning using the Minimum Bias”, ATLAS
Minimum Bias Working Group Meeting, September 10, 2007
13. H. Okawa et al., “Missing ET Performance Studies using W Template Method”,
ATLAS Trigger and Physics Week - JetEtMiss Working Group Meeting, November
7, 2007
14. H. Okawa et al., “Missing ET Performance Studies using W (Template Method)”,
ATLAS JetEtMiss Working Group Phone Conference, December 12, 2007

Calorimeter Commissioning with Cosmic and Single Beam Data
15. H. Okawa, “Topocluster Performance Studies in M5 Run”, ATLAS Tilecal Performance Meeting, Jan. 21, 2008250

16. H. Okawa, “Topocluster Performance Studies in the Barrel with M5 Runs”, ATLAS
LAr Detector + Cosmic Analysis Meeting, Jan. 24, 2008
17. H. Okawa, “Topocluster Performance Studies in M5 Runs”, ATLAS Tile Week Data Preparation, Feb. 6, 2008
18. H. Okawa, “PMT, Cell Energy Distributions and Topocluster Performance in M5
Runs,” ATLAS Tilecal Performance Meeting, Mar. 10, 2008
19. H. Okawa (presented by A. Gibson), “Topocluster Performance Studies with M5
runs,” ATLAS LAr Detector + Cosmic Analysis Meeting, Mar. 13, 2008
20. H. Okawa, “PMT Energy Distributions & Noise in M5 runs,” ATLAS Tilecal Performance Meeting, Apr. 14, 2008
21. H. Okawa, “Topocluster performance studies with cosmic runs,” ATLAS LAr Week,
Apr. 16, 2008
22. H. Okawa, “Bad channel search and mysteries of Topoclusters at M7 runs,” ATLAS
Tilecal Performance Meeting, Jun. 9, 2008
23. H. Okawa, “Topoclusters at M7 Runs,” ATLAS LAr Weekly Meeting, Jun. 17,
2008
24. H. Okawa, “Topoclusters at M7 Runs,” ATLAS LAr Weekly Meeting, Jun. 24,
2008
25. H. Okawa, “Calorimeter Commissioning with Cosmic Ray Data & towards the First
Collision,” Minimum Bias Meeting, Aug. 4, 2008
26. H. Okawa, “Preview of TileCal Talks,” ATLAS Tilecal Performance Meeting, Oct.
13, 2008
27. H. Okawa, “TileCal noise in recent cosmic runs,” TileCal Calibration Performanceand Tools Meeting, Jan. 12, 2009
28. H. Okawa, “Electronics Noise,” TileCal Calibration Performance and Tools Meeting, Feb. 6, 2009
29. H. Okawa, “Topoclusters and eﬀects of non-Gaussian noise,” TileCal Data Quality,
Performance and Processing Meeting, Mar. 16, 200930. H. Okawa, “Noise Stability of TileCal in Fall’08 Reprocessed Data,” TileCal Calibration Performance and Tools Meeting, July 27, 2009
31. H. Okawa, “LAr Pedestal & Noise Stability in Fall’08 Reprocessed Data,” LAr
Weekly Meeting, Oct. 13, 2009

Investigations of Rejecting non-Collision BG in Jets and Missing ET
251

32. H. Okawa, “Jet Performance Studies with M5 data,” Jet/EtMiss Phone Conference,
Apr. 30, 2008
33. H. Okawa, “Jets from Calorimeter Noise, Bad Channels and Cosmics,” Jet/EtMiss
Phone Conference, Jun. 25, 2008
34. H. Okawa, “Noise and Jet studies with M7 data,” TileCal Week, Jul. 3, 2008
35. H. Okawa, “Calorimeter Commissioning, Fake Jets & Etmiss Studies with Cosmic
Ray Data,” SUSY Working Group Meeting, Sep. 11, 2008
36. H. Okawa, “Jets and Missing ET from cosmic and single beam data,” ATLAS Week,
Jet/Etmiss performance meeting, Dec. 2, 2008
37. H. Okawa, “Jets and Missing ET in Cosmic and Single Beam Data,” TileCal Week
Performance Session, Dec. 9, 2008
38. H. Okawa, “Cosmic Ray Analysis: Technical Note,” Jet/EtMiss Data Preparation
Task Force Meeting, Jan. 20, 2009
39. H. Okawa, “Jet & MET studies with Event Overlay,” Jet/EtMiss Data Preparation
Task Force Meeting, Feb. 18, 200940. H. Okawa, “Jet/Etmiss studies on cosmics data,” ATLAS Week Combined Performance Session, Feb. 19, 2009
41. H. Okawa, “Preliminary Results from High Energetic Cosmic Rays in the ATLAS
Calorimeter,” Clustered Physics/Performance Meetings - Plenary analysis session,
Mar. 26, 2009
42. H. Okawa, “Simulation for jet/etmiss studies,” Cosmic simulation production meeting, Apr. 1, 2009
43. H. Okawa, “Track & Jet Association in Cosmic Events,” Inner Detector Software
And Performance Weekly Meeting, Apr. 24, 200944. H. Okawa, “Investigations on Track & Jet Association for BG Rejection,” Clustered
Physics/Performance Meetings - Jet/EtMiss Data Preparation Task Force Session,
Apr. 27, 200945. H. Okawa, “Background from cosmics in monojet & multi-jet+MET search,” Clustered Physics/Performance Meetings - SUSY Working Group Meeting, Apr. 29,
2009

Monojet Signature from Large Extra Dimensions
46. H. Okawa, “Sherpa MC for ADD KK-Graviton & Cosmic BG in Monojet Search,”
TeV-scale Gravity Meeting, Aug. 14, 2009252

Appendix B
Optimal Filtering Method
The Optimal Filtering Coeﬃcients (OFC’s) are derived as follows [76][80].
First of all, we deﬁne the shape of the signal as a function of time S(t), which can be
expressed as
S(t) = Ag(t).
(B.1)
Here, A is the amplitude and g(t) is the normalized shape form function. Since the signal
is digitized, the ADC-value in each sampling can be described as
Si = Ag(ti − τ ) + ni ,(B.2)

where i corresponds to the “i”-th sampling, τ is the phase diﬀerence between the digitizer
output, and ni is the noise ﬂuctuation from the average pedestal. Assuming that the phaseshift τ is small, (B.2) can be rewritten by Taylor’s expansion,
Si

Ag(ti ) − Aτ g  (ti ) + ni .

(B.3)

Now, we deﬁne two variables u, v,Nsampleu =ai Si ,

(B.4)

bi Si .

(B.5)

i=1



Nsamplev =i=1Here, ai , bi are free parameters at this point, which will be later be used as OFC’s. Then,
we require the mean values of u, v to equal,
u
v

= A,
= Aτ .

(B.6)
(B.7)

From (B.3)-(B.7), and assuming that the average of noise ﬂuctuation ni is zero (which253

is true for Gaussian noise),

NsampleA = A

Nsampleai gi − Aτ

i=1

i=1



Nsample= A

ai gi − Aτ

i=1
NsampleAτ = A



i=1

i=1



NsampleNsample= A

ai gi ,i=1
Nsamplebi gi − Aτbi gi − Aτ

(B.8)


Nsamplebi gi +

bi ni

i=1



i=1

ai ni

i=1



Nsample

Nsampleai gi +bi gi ,

(B.9)

i=1

where g(ti ) was written as gi for simplicity. From (B.8) and (B.9), we require the parameters ai and bi to satisfy,Nsamplei=1

Nsampleai gi = 1 ,Nsamplebi gi = 0 ,i=1Nsampleai gi = 0 ,i=1bi gi = −1 .

(B.10)

i=1

These set of equations are not enough to determine the OFC parameters (four equationsfor 2Nsample parameters).
On top of the equations above, we require that the set of ai and bi minimize thevariances of u and v,⎛⎞
NsampleNsampleNsample
Var(u) = Var ⎝Aai gi − Aτai gi +ai ni ⎠⎛

i=1

Nsample= Var ⎝⎛

i=1

ai ni ⎠ ,

Nsamplei=1

Nsample= Var ⎝

i=1

(B.11)i=1

Var(v) = Var ⎝A⎛⎞

Nsamplebi gi − Aτ⎞bi ni ⎠ ,i=1

Nsamplebi gi +⎞bi ni ⎠

i=1

(B.12)

i=1

and the equations are simpliﬁed as above due to the fact that ai , bi , gi, gi are constant.

254

The equations can be further rewritten as,NsampleVar(u) =a2i Var(ni ) +i=1

Nsample Nsample
m

2  
ai aj
(ni − ni )(nj − nj )m i=1 j=i+1

Nsample
m

1 
ai aj
(ni − ni )(nj − nj )=m i,j=1
Nsample
m

1 
ai aj
(ni nj ) (∵ ni = 0)=m i,j=1

Nsample=ai aj ni nj ,(B.13)

bi bj ni nj .

(B.14)

i,j=1

NsampleVar(v) =i,j=1

In order to determine ai , bi that satisfy (B.10) and minimize (B.13) and (B.14), we use
the method of Lagrange multipliers,Fu (a1 , · · · , aNsample , λ, κ)Nsample=⎛ai aj ni nj − λ ⎝i,j=1

Nsample

=

⎞

⎛

⎛



Nsamplebi bj ni nj − μ ⎝

i,j=1



Nsampleai gi − 1⎠ − κ ⎝i=1

Fv (b1 , · · · , bNsample , μ, ρ)Nsample⎞ai gi ⎠ ,(B.15)

i=1⎞⎛

bi gi ⎠ − ρ ⎝i=1

Nsample⎞bi gi + 1⎠ .

(B.16)

i=1

Here, λ, κ, μ, ρ are the Lagrange multipliers. To derive the equations to determine theparameters, we require the partial derivatives of Fu and Fv to equal zero,

∂Fu
= 2
aj ni nj − λgi − κgi∂ai
j=1
Nsample

Nsample=aj Rij − λgi − κgi = 0 (∵ Rij ≡ 2 ni nj )(B.17)

bj Rij − μgi − ρgi = 0 .

(B.18)

j=1

∂Fv
∂bi



Nsample=j=1

Thus, we obtain Nsample +2 equations, for ai (i = 1, · · · , Nsample ) and two Lagrange multi-

255

pliers,
⎛

R11
..
.

⎜
⎜
⎜
⎜ RNsample ,1
⎜
⎝
g1
g1

⎞ ⎛ ⎞
⎞⎛
···
R1,Nsample
g1
g1
a1
0
..
..
..
⎟
⎟⎜
⎜
..
.
. ⎟
..
.
⎟ ⎜ .. ⎟
⎟⎜
.
.
.
⎟ ⎜ ⎟
⎟⎜

⎟ ⎜ aNsample ⎟ = ⎜ 0 ⎟· · · RNsample ,Nsample gNsample gN
sample ⎟ ⎜⎟ ⎜ ⎟⎠ ⎝ 1 ⎠⎠⎝···
gNsample
0
0
λ

κ
···
gNsample000(B.19)

which can be solved to have unique solutions. In the same way, bi can be derived,
⎞
⎞⎛
⎞ ⎛
⎛
R11
···
R1,Nsample
g1
g1
b1
0
..
..
..
..
⎟⎜
⎟ ⎜ .. ⎟
⎜
..
..
.
⎟⎜
⎟ ⎜ . ⎟
⎜
.
.
.
.
.
⎟
⎟⎜
⎟ ⎜
⎜

⎜ RNsample ,1 · · · RNsample ,Nsample gNsample gNsample ⎟ ⎜ bNsample ⎟ = ⎜ 0 ⎟ .
⎟
⎟⎜
⎟ ⎜
⎜
⎠⎝
⎠ ⎝ 0 ⎠⎝
g1···
gNsample
0
0
μ

−1
···
gN
0
0
g1
ρ
sample(B.20)As described above, all the components in the matrix (noise correlations, function andits derivative of pulse shape) are determined beforehand in dedicated calibration runs,
and allow to determine the Optimal Filtering Coeﬃcients.

256

Appendix C
Missing ET Algorithm in ATLAS
Calorimeter Term in ETmiss
The x and y components of the calorimeter term are calculated from the vector sum ofenergy in each cell.

miss,Calo
cell
= −
Ex,y
(C.1)
Ex,y
ExcellEycellcell
cell= E sin θ cos φ= E cell sin θ sin φ

(C.2)
(C.3)

The angular directions of cells are deﬁned from the central position of the cell. Eightcategories exist in the ATLAS framework for the calorimeter term. Four of them are atthe EM-scale, and the others are calibrated to the hadronic scale with H1-style or the
Local Hadronic Calibration scheme [23]. Random stream data oﬀers a useful training
ground to validate the calorimeter term of the ETmiss , and will be mentioned in the next
section.
Muon Term in ETmiss
The muon term exploits the information of muon tracks up to the pseudorapidity (η) of
2.7.

miss, muon
muon
Ex,y
=−
Ex,y
(C.4)
muon

When |η| < 2.5, muon tracks with good quality and matching between the Muon Spectrometer and the inner detector are considered. In the region of 2.5 < |η| < 2.7, themuon tracks are solely reconstructed by the Muon Spectrometer, because the region is
outside the ﬁducial volume of the Inner Detector.
Cryostat Term in ETmiss
Cryostat is located between the LAr barrel EM calorimeter and the Tile barrel calorimeter. Its thickness is about half an interaction length. The cryostat term in ETmiss recovers
the energy lost in the cryostat by using the correlation of energy between the last layer257

of the EM calorimeter and the ﬁrst layer of the hadronic calorimeter. A similar strategy
is used in the end-cap region. The x and y components of ETmiss is deﬁned as follows.
 Cryomiss,Cryo= −Ejet, x,y
(C.5)
Ex,y
jet
Cryo
Cryo
Ejet,
x,y = w



EM3
Had1
Ejet,
x,y × Ejet, x,y(C.6)

The cryostat term is calculated from all the reconstructed jets in the event. In (C.6),
EM3
is the energy of the jet in the third layer of the EM
w Cryo is a calibration weight, Ejet
Had1
calorimeter, and Ejet is the energy of the jet in the ﬁrst layer of the hadronic calorimeter. Two categories exist with regards to the type of jets used. Cone4H1TowerJets arereconstructed with the ATLAS-Cone algorithm with the cone size of 0.4, and the combined towers are used as inputs. Cone4H1TopoJets are the same as Cone4H1TowerJets
except that the Topoclusters are used as inputs instead of the towers.
Reﬁned Calibration of ETmiss
Physics analyses will use ETmiss variables with so called the “reﬁned calibration.” The
reﬁnement of the calibration of calorimeter cells is done when those cells are associatedto high pT objects. Calorimeter cells are associated with reconstructed high pT objects
in the following order: electrons, photons, muons, hadronically decaying τ leptons, b-jetsand light ﬂavor jets. Reﬁned calibration of each object is applied and replace the initial
calibration constant of the cells.

258

Table C.1: ETmiss quantities in ATLAS
Component
Calo

Category
MET Base

Calo

MET Base0

Calo

MET Topo

Calo

MET TopoObj

Calo

MET Calib

Calo

MET CorrTopo

Calo

MET LocHadTopo

Calo

MET LocHadTopoObj

Muon

MET Muon

Muon

MET MuonBoy Track

Muon

MET MuonBoy Spectro

Muon
Cryostat
Cryostat
ReﬁnedReﬁnedReﬁnedReﬁnedReﬁnedMET MuonBoy
MET Cryo
MET CryoCone
MET RefEle
MET RefGamma
MET RefTau
MET RefJet
MET RefMuon

ReﬁnedMET RefMuon TrackReﬁnedMET CellOut

All
All

MET Final
MET RefFinal

Deﬁnition
ETmiss from all calorimeter cells above 2 σ fromthe noise. Energy is at the EM scale.
ETmiss from all calorimeter cells without any
threshold. Energy is at the EM scale.
ETmiss from all calorimeter cells belonging to the
Topoclusters. Energy is at the EM scale.
ETmiss from the Topoclusters.
Energy is at the EM scale.
ETmiss from all calorimeter cells above 2 σ from thenoise. Energy is calibrated with the H1-style.
ETmiss from all calorimeter cells belonging to the
Topoclusters. Energy is calibrated with the H1-style.
ETmiss from all calorimeter cells belonging to the
Topoclusters. Energy is calibrated with the Local
Hadronic Calibration style.
ETmiss from the Topoclusters.
Energy is calibrated with the Local Hadronic
Calibration style.
ETmiss from all the STACO-algorithm muons.
Muon Spectrometer tracks used.
ETmiss from all the isolated STACO muons andmuons in gaps. ID and Muon Spectrometer combined
tracks used.
ETmiss from all the non-isolated STACO muons
Muon Spectrometer tracks used.
MET MuonBoy Track + MET MuonBoy Spectro
Cryostat term calculated from Cone4H1TopoJets
Cryostat term calculated from Cone4H1TowerJets
ET from calorimeter cells inside electrons
ET from calorimeter cells inside photons
ET from calorimeter cells inside taus
ET from calorimeter cells inside jets
ET from calorimeter cells belonging to
non-isolated muons
ET from calorimeter cells belonging to isolatedmuons.
ET from calorimeter cells in Topoclusters notbelonging to any reconstructed objects
MET CorrTopo + MET Muon + MET Cryo
ETmiss for physics analyses.
Sum of all Ref categories + MET MuonBoy +MET Cryo259

Appendix D
Functions in KK-Graviton Processes
Functions used in Section 11.1.1 are explicitly described [142].
1[−4x(1 + x)(1 + 2x + 2x2 ) + y(1 + 6x + 18x2 + 16x3 )x(y − x − 1)
(D.1)
−6y 2x(1 + 2x) + y 3 (1 + 4x)] ,xy
F2 (x, y) = −(y − x − 1)F1,
y−x−1 y−x−11[−4x(1 + x2 ) + y(1 + x)(1 + 8x + x2 ) − 3y 2(1 + 4x + x2 )=x(y − x − 1)
+4y 3(1 + x) − 2y 4] ,(D.2)
1
[1 + 2x + 3x2 + 2x3 + x4 − 2y(1 + x3 ) + 3y 2(1 + x2 )F3 (x, y) =x(y − x − 1)
−2y 3(1 + x) + y 4] .
(D.3)

F1 (x, y) =260

Appendix E
Monte Carlo Simulation Samples
Used
E.1

Samples for Monojet Studies
Table E.1: Cross section and proﬁle of Standard Model samples.

MC Sample
Z(→ ν ν̄)+ jets
Z(→ e+ e− )+ jets
Z(→ μ+ μ− )+ jets
Z(→ τ + τ − )+ jets
W (→ τ ντ )+ jets
W (→ μνμ )+ jets
W (→ eνe )+ jetstt̄Single Top (t-chan)Single Top (Wt)
QCD dijet (J1)
QCD dijet (J2)
QCD dijet (J3)
QCD dijet (J4)
QCD dijet (J5)
QCD dijet (J6)
QCD dijet (J7)
QCD dijet (J8)

Generator
PythiaPythiaPythiaPythiaPythiaPythiaPythiaMC@NLO
AcerMC+Herwig
AcerMC+Herwig
PythiaPythiaPythiaPythiaPythiaPythiaPythiaPythiaxsec [nb]
3.48
0.610
0.610
0.604
5.97
5.97
5.97
0.203
0.04110.01448.59e55.62e43241.6
150.9
5.18
0.112
1.07e-3
1.11e-6

261

Generation Cut
parton pT >10 GeV
Me+ e− >60 GeV, parton pT >10 GeV
Mμ+ μ− >60 GeV, parton pT >10 GeV
Mτ + τ − >60 GeV, parton pT >10 GeV
parton pT >10 GeV
parton pT >10 GeV
parton pT >10 GeV
not all hadronic decay

17 GeV < 1st parton pT < 35 GeV
35 GeV < pT < 70 GeV
70 GeV < pT < 140GeV
140 GeV < pT < 280 GeV
280 GeV < pT < 560 GeV
560 GeV < pT < 1120 GeV
1120 GeV < pT < 2280 GeV
pT > 2280 GeV

E.2

Samples for Multi-jet Studies
Table E.2: Cross section and proﬁle of Monte Carlo samples for Z events.

MC Sample
Z(→ ν ν̄)+ 0 parton
Z(→ ν ν̄)+ 1 parton
Z(→ ν ν̄)+ 2 partons
Z(→ ν ν̄)+ 3 partons
Z(→ ν ν̄)+ 4 partons
Z(→ ν ν̄)+ 5 partons
Z(→ e+ e− )+ 0 parton
Z(→ e+ e− )+ 1 parton
Z(→ e+ e− )+ 2 partons
Z(→ e+ e− )+ 3 partons
Z(→ e+ e− )+ 4 partons
Z(→ e+ e− )+ 5 partons
Z(→ μ+ μ− )+ 0 parton
Z(→ μ+ μ− )+ 1 parton
Z(→ μ+ μ− )+ 2 partons
Z(→ μ+ μ− )+ 3 partons
Z(→ μ+ μ− )+ 4 partons
Z(→ μ+ μ− )+ 5 partons
Z(→ τ + τ − )+ 0 parton
Z(→ τ + τ − )+ 1 parton
Z(→ τ + τ − )+ 2 partons
Z(→ τ + τ − )+ 3 partons
Z(→ τ + τ − )+ 4 partons
Z(→ τ + τ − )+ 5 partons

Generator
ALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMY

262

xsec [pb]52.54
587.57326.74112.5332.98
9.50
898.18206.57
72.50
21.08
6.00
1.73
900.21205.21
69.35
21.63
6.08
1.70
902.71209.26
70.16
21.07
6.04
1.71

Generation Cut
1 jet with pT > 30 GeV
1 jet with pT > 30 GeV
1 jet with pT > 30 GeV
1 jet with pT > 30 GeV
1 jet with pT > 30 GeV
1 jet with pT > 30 GeV
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlterTable E.3: Cross section and proﬁle of Monte Carlo samples for Z events.
MC Sample
W (→ eνe )+ 0 parton
W (→ eνe )+ 1 parton
W (→ eνe )+ 2 partons
W (→ eνe )+ 3 partons
W (→ eνe )+ 4 partons
W (→ eνe )+ 5 partons
W (→ μνμ )+ 0 parton
W (→ μνμ )+ 1 parton
W (→ μνμ )+ 2 partons
W (→ μνμ )+ 3 partons
W (→ μνμ )+ 4 partons
W (→ μνμ )+ 5 partons
W (→ τ ντ )+ 0 parton
W (→ τ ντ )+ 1 parton
W (→ τ ντ )+ 2 partons
W (→ τ ντ )+ 3 partons
W (→ τ ντ )+ 4 partons
W (→ τ ντ )+ 5 partons

Generator
ALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMYALPGEN+JIMMY

xsec [pb]10184.7
2112.4
676.0
203.3
56.1
16.6
10125.72155.5
682.3
202.0
55.5
16.3
10178.3
2106.9
672.8
202.7
55.3
17.0

Generation Cut
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlter
No ﬁlterTable E.4: Cross section and proﬁle of Monte Carlo samples for Top and QCD events.
MC Samplett̄
QCD dijet (J1)
QCD dijet (J2)
QCD dijet (J3)
QCD dijet (J4)
QCD dijet (J5)
QCD dijet (J6)
QCD dijet (J7)
QCD dijet (J8)

Generator
MC@NLO
PythiaPythiaPythiaPythiaPythiaPythiaPythiaPythiaxsec [pb]0.203
8.59×108
5.62×107
3.24×106
1.51×105
5.18×103112
1.07
1.11×10−3

263

Generation Cut
not all hadronic decay
17 GeV < leading parton pT < 35 GeV
35 GeV < pT < 70 GeV
70 GeV < pT < 140GeV
140 GeV < pT < 280 GeV
280 GeV < pT < 560 GeV
560 GeV < pT < 1120 GeV
1120 GeV < pT < 2280 GeV
pT > 2280 GeV

Appendix F
Monte Carlo-based Estimation ofZ(→ ν ν̄) BG for SUSY Searches
A data-driven method to estimate the Z(→ ν ν̄) background in no-lepton search modefor SUSY was mentioned in Section 12.5.1.
Here, a Monte Carlo-based estimation method for Z(→ ν ν̄) background is brieﬂymentioned [A.3.4.6] [A.3.4.7] [A.3.4.9]. The method fully relies on the shape of distributions from a Z(→ ν ν̄) Monte Carlo simulation, in order to cope with the limited statisticsof Z(→ l+ l− )+jets data, which leads to the main limitation of precision of the data-driven
estimation method. However, in the Monte Carlo-based method, the major concern isthe uncertainty of the normalization, especially when we consider multi-jet signatures.
In the Monte Carlo-based estimation, the normalization should be derived from data.
The normalization factor will be extracted with the use of Z(→ l+ l− )+jets events, so
the Z(→ l+ l− )+jets events also play important roles in the Monte Carlo-based method.
The diagramatical overview of this estimation scheme is shown in Figure F.1. The normalization factor extracted from Z(→ l+ l− )+jets are also applied to Z(→ ν ν̄)+jets
Monte Carlo samples, and the samples are used for the estimation.
The advantage of this method is that the statistical uncertainty only contributes
to the uncertainty of the normalization. On the other hand, the major challenges forthis method would be the validation of the Monte Carlo simulation with data and theunderstanding of detector performance (e.g. jet energy scale and resolution, ETmiss scaleand resolution).
Here, an example is shown for the case center-of-mass energy 14 TeV and 1 fb−1 integrated luminosity. Pseudo-data was produced with√ diﬀerent Alpgen parameters. Here,the selection cut is slightly diﬀerent to the case of s =10 TeV,
• No isolated electrons and muons• At least four jets with pT > 50 GeV and |η| <5.0, and at least one of them withpT > 100 GeV• Transverse sphericity > 0.2• ETmiss > 100 GeV and ETmiss /Mef f > 0.2
264

Figure F.1: Overview of quasi-date-driven estimation scheme for Z(→ ν ν̄) background.
The normalization factor is determined by comparing Monte Carlo and data with Z(→l+ l− ) events. The same normalization factor is applied to the Z(→ ν ν̄) distributions to
reproduce the data from Monte Carlo.
In order to have fully enough statistics of Monte Carlo samples, fast simulation should
be used with appropriate corrections applied to recover the detector eﬀects. Here, fast
simulation samples with corrections applied to taken into account the lepton eﬃciency,
jet reconstruction eﬃciency, jet energy scale, and ETmiss scale were used.
The normalization factor can be determined within about 20 % (Figure F.2), andthis uncertainty is the dominant source of the overall uncertainty of the estimation, sinceenough statistics is provided from the Monte Carlo. This factor is applied to the originalMonte Carlo distributions. Figure F.3 shows the distributions of ETmiss , eﬀective mass,and the leading jet pT for Z(→ ν ν̄) background. The estimation is consistent with thepseudo-data within the statistical uncertainty.

265

Figure F.2: Normalization factor determined by comparing Monte Carlo and pseudodata.

Figure F.3: Estimation of Z(→ ν ν̄) background using quasi-data-driven method. Eﬀective mass (left), ETmiss (center), and leading jet pT (right) distributions are shown.

266

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