Search for Charginos Nearly Mass-Degeneratewith the Lightest Neutralino
Based on a Disappearing-Track Signature√in pp Collisions at s = 8 TeV
Ph. D. thesis

Shingo Kazama
Department of Physics
Faculty of Science, The University of Tokyo
December 2013

Abstract
A search is presented for direct chargino production based on√ a disappearing-track
signature using 20.3 fb−1 of proton–proton collisions at s = 8 TeV collectedwith the ATLAS experiment at the Large Hadron Collider. In anomaly-mediatedsupersymmetry breaking (AMSB) models, the lightest chargino is nearly massdegenerate with the lightest neutralino and its lifetime is long enough to be detected in the tracking detectors by identifying decays that result in tracks with noassociated hits in the outer region of the tracking system. Other supersymmetricmodels with the pure neutral wino being the lightest supersymmetric particle also
predict the same signature. This analysis attains sensitivity for charginos with alifetime between 0.1 and 10 ns, and significantly surpasses the reach of the LEP
experiments due to an enhanced track reconstruction efficiency for charginos having short decay length and a dedicated topological trigger to attain a higher signal
efficiency. No significant excess above the background expectation is observed forcandidate tracks with large transverse momentum, and constraints on chargino
properties are obtained. In the AMSB scenarios, a chargino mass below 270 GeV is
excluded at 95% confidence level, which also directly constrains the mass of wino
dark matter.

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Contents
1 Introduction

1

2 Theoretical Framework and Motivation
2.1 The Standard Model . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.1.1 Outstanding issues in the Standard Model . . . . . . . . . .
2.2 Supersymmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Minimal Supersymmetric Standard Model . . . . . . . . . .
2.2.2 R-parity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.3 SUSY breaking model . . . . . . . . . . . . . . . . . . . . . .
2.3 Wino LSP scenarios and their phenomenology . . . . . . . . . . . .
2.3.1 Anomaly-Mediated Supersymmetry Breaking model . . . .
2.3.2 Pure Gravity Mediation model . . . . . . . . . . . . . . . . .
2.3.3 Phenomenology . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.4 Theoretical and experimental indications . . . . . . . . . . .
2.3.5 Previous searches and constraints . . . . . . . . . . . . . . .

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3 LHC and ATLAS Detector
3.1 Large Hadron Collider . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 ATLAS detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.1 ATLAS coordinate . . . . . . . . . . . . . . . . . . . . . . . .
3.2.2 Magnet system . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3 Inner detector . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.4 Calorimeter system . . . . . . . . . . . . . . . . . . . . . . . .
3.2.5 The electromagnetic calorimeter . . . . . . . . . . . . . . . .
3.2.6 Hadronic calorimeter . . . . . . . . . . . . . . . . . . . . . . .
3.2.7 Muon spectrometer . . . . . . . . . . . . . . . . . . . . . . . .
3.2.8 Luminosity detector . . . . . . . . . . . . . . . . . . . . . . .
3.2.9 Trigger system . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 Data and Monte Carlo Simulation
4.1 Data sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1 Trigger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.2 Luminosity and pile-up . . . . . . . . . . . . . . . . . . . . .

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4.2

Simulated sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2.1 Signal Monte Carlo sample . . . . . . . . . . . . . . . . . . .
4.2.2 Background Monte Carlo sample . . . . . . . . . . . . . . . .

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5 Object Reconstruction
5.1 Track reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.1.1 Track parametrization . . . . . . . . . . . . . . . . . . . . . .
5.1.2 Inside-out track reconstruction . . . . . . . . . . . . . . . . .
5.1.3 Outside-in track reconstruction . . . . . . . . . . . . . . . . .
5.1.4 Re-tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Vetex reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 Jet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.1 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.2 Clustering of calorimeter cells . . . . . . . . . . . . . . . . . .
5.3.3 Jet finding algorithm . . . . . . . . . . . . . . . . . . . . . . .
5.3.4 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3.5 Jet energy scale uncertainty . . . . . . . . . . . . . . . . . . .
5.4 Electron . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4.1 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . .
5.4.2 Identification criteria . . . . . . . . . . . . . . . . . . . . . . .
5.5 Muon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.1 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . .
5.5.2 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.6 Missing transverse energy . . . . . . . . . . . . . . . . . . . . . . . .

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6 Tracking Performance
6.1 Tracking performance for decaying charginos . . . . . . . . . . . . .
6.2 Tracking performance in QCD multi-jet events . . . . . . . . . . . .

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7 Properties of Signal Events
7.1 Production process . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7.2 Finding decaying chargino tracks . . . . . . . . . . . . . . . . . . . .
7.3 Properties of chargino tracks . . . . . . . . . . . . . . . . . . . . . . .

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8 Event Selection
93
8.1 Online selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
8.1.1 Trigger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
8.2 Offline selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
8.2.1 Primary vertex selection . . . . . . . . . . . . . . . . . . . . . 99
8.2.2 Overlap removal . . . . . . . . . . . . . . . . . . . . . . . . . 99
8.2.3 Event cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . 100
8.2.4 Kinematic selection criteria . . . . . . . . . . . . . . . . . . . 100
8.2.5 Disappearing track selection . . . . . . . . . . . . . . . . . . 100
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9 Background Estimation
9.1 Interacting-hadron tracks . . . . . . . . . . . . . . . . . . . . . . . .
9.1.1 Control sample for interacting-hadron tracks . . . . . . . . .
9.1.2 Extraction of pT shape . . . . . . . . . . . . . . . . . . . . . .
9.2 Leptons failing to satisfy identification criteria . . . . . . . . . . . .
9.2.1 Probability of identifying a lepton as a disappearing track .
9.2.2 pT spectrum of lepton background tracks . . . . . . . . . . .
9.2.3 Uncertainty of the lepton background yield . . . . . . . . . .
9.3 Tracks with mismeasured pT . . . . . . . . . . . . . . . . . . . . . . .
9.3.1 Control data sample of pT -mismeasured tracks . . . . . . . .
9.3.2 Further check on the impact-parameter dependence . . . . .
9.3.3 Systematic uncertainty on the track-pT shape . . . . . . . . .

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10 Systematic Uncertainties
129
10.1 Uncertainty on the signal events . . . . . . . . . . . . . . . . . . . . 129
10.2 Uncertainty on the background events . . . . . . . . . . . . . . . . . 130
11 Extraction of Signal Yield
11.1 Unbinned extended maximum likelihood fit . . . . . . . . . . . . .
11.2 Validation of the signal extraction method . . . . . . . . . . . . . . .
11.3 Null-hypothesis testing in the high-pT region . . . . . . . . . . . . .
11.4 Full pT -range fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11.5 Properties of the excess in the track-pT spectrum . . . . . . . . . . .
11.5.1 Instrumental background . . . . . . . . . . . . . . . . . . . .
11.5.2 Possibility of underestimating the muon background . . . .
11.5.3 Signature of decaying charged massive particles . . . . . . .

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12 Interpretations and Discussion
12.1 Constraints on the AMSB model . . . . . . . . . . . . . . . . . . . .
12.2 Constraints on the Pure Gravity Mediation model . . . . . . . . . .
12.3 Constraints on wino dark matter . . . . . . . . . . . . . . . . . . . .
12.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

147
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13 Conclusions

155

Acknowledgments

157

A Profile Likelihood and CLs
A.1 Profile likelihood ratio . . . . . . . . . . . . . . . . . . . . . . . . . .
A.2 Confidence level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Chapter 1

Introduction
The Standard Model (SM) represents currently the most accurate and predictive
description of nature, at least up to the scale of the weak interaction. It provides an almost complete picture of the interactions of matter through three outof four fundamental interactions in nature: the electromagnetic, the weak, andthe strong interactions. Numerous precision measurements have validated its accuracy. However, there are still many outstanding issues that the SM does notprovide the answers. This indicates that it is a low-energy approximation of afundamental theory, and new physics beyond the SM does exist.
Supersymmetry (SUSY) is one of the most promising theories providing asolution to the problems in the SM. The SUSY is a symmetry between bosons andfermions: it transforms bosons into fermions, and vice versa. The fact that noneof these particles are yet discovered indicates that the SUSY must be a broken
symmetry, making the supersymmetric particles heavier than their SM partners.
The SUSY breaking is assumed to occur in the hidden sector, which consists ofparticles that are completely neutral with respect to the SM gauge group, and its
effects are mediated to the visible sector by some mechanisms.
Anomaly-mediated supersymmetry breaking (AMSB) model [1, 2], where the
SUSY breaking is caused by loop effects, provides a constrained mass spectrum of
SUSY particles. One prominent feature of this model is that the lightest supersymmetric particle (LSP) is the nearly pure neutral wino that is mass-degenerate with±the charged wino. The lightest chargino (χ̃1 ) is slightly heavier than the lightest
0
neutralino (χ̃1 ) due to radiative corrections involving electroweak gauge bosons.
0±
The typical mass splitting between χ̃1 and χ̃1 (∆mχ̃1 ) is ∼ 160 MeV, which implies
0±that χ̃1 has a considerable lifetime and predominantly decays into χ̃1 plus a low±
momentum π± . The mean lifetime of χ̃1 (τχ̃± ) is expected to be typically a fraction
1of a nanosecond. Therefore, some charginos could have decay length exceedinga few tens of centimeters at the Large Hadron Collider (LHC). When decaying inthe sensitive volume, they are expected to be observed as “disappearing tracks”
that have no more than a few associated hits in the outer region of the tracking
system, and the softly emitted π± is not reconstructed.
12

1.0 I

This dissertation explores the AMSB model by searching for charginos with−1
their subsequent decays that result√ in such disappearing tracks using 20.3 fb ofproton–proton (pp) collisions at s = 8 TeV collected with the ATLAS experimentat the LHC. Several theoretical and experimental indications motivate this search:• The large mass of Higgs boson ∼ 126 GeV can be easily realized due to large
sfermion masses [3, 4, 5, 6].
• The neutral wino can be a plausible candidate for dark matter. The appropriate relic abundance can be obtained either through thermal freeze-out [7]or a non-thermal history with a late-decaying modulus [8].
• The problems of flavor-changing neutral currents (FCNCs) and CP violation
are not serious due to the large sfermion masses and a flavor-blind mediationof SUSY breaking.
Previously, ATLAS explored charginos that have significant lifetimes via thegluino-pair production process [9, 10, 11], which sets a limit of > 800 GeV on
the gluino mass. Furthermore, inclusive searches for colored supersymmetricparticles [12] give a stringent limit of > 1.4 TeV on the gluino mass. Given thesefacts and the mass of gluino being much larger than that of wino (mg̃ /mχ̃0 ∼ 8 in the1
AMSB model), a chargino with a mass of a few hundred GeV cannot be addressed
via strong productions at the current LHC energy. However, the electroweak√
production of charginos has a sizable cross-section in pp collisions at s = 8 TeV,
and a large amount of data allows to explore the AMSB model via the electroweak
production.
Chargino-pair and chargino–neutralino associated production processes dominate in the electroweak production, and they are experimentally collected andidentified based on the existence of a jet with large transverse momentum (pT )from initial-state radiation (ISR). The signal topology is then characterized by ahigh-pT jet, large missing transverse momentum due to LSPs escaping the detector, and a high-pT disappearing track. The search presented in this dissertation
increases the sensitivity compared to the previous ATLAS searches [13] due to various analysis improvements. The most significant improvements are achieved byenhancing the track reconstruction efficiency for charginos having short decay
length and the trigger efficiency by adopting a dedicated topological trigger.
This dissertation is organized as follows:• Chapter 2
An overview of the SUSY models with the pure neutral wino being the LSP
and their theoretical motivations.
• Chapter 3
The experimental setup of the LHC and the ATLAS experiment.

1.0 I

3• Chapter 4
The data and the simulated samples used in this dissertation.
• Chapter 5
Various reconstruction algorithms to identify tracks, jets, electrons, muons,and missing transverse momentum.
• Chapter 6
Tracking performances especially for short-length tracks.
• Chapter 7
A strategy of finding chargino tracks and their properties.
• Chapter 8
Signal selection criteria and studies on the trigger efficiency.
• Chapter 9
Details on the background estimation.
• Chapter 10
Systematic uncertainties on signal and background events.
• Chapter 11
Details of the signal extraction and results.
• Chapter 12Interpretations of the results in the context of several SUSY models and wino
dark matter scenarios.
• Chapter 13
Concluding remarks.

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1.0 I

Chapter 2Theoretical Framework andMotivation
The Standard Model is currently the most precise theoretical framework to describe the sub-atomic particles and their behavior, and a large number of precision
measurements have validated its accuracy. However, there are still many problems
that the SM leaves unsolved. This indicates that it is a low-energy approximationof a more general underlying theory, and new physics beyond the SM does exist.
Supersymmetry, which is a symmetry that relates bosons and fermions, is one of
the most promising theories providing a solution to the problems in the SM. This
chapter gives an overview of the SUSY models explored in this dissertation andtheir theoretical motivations.

2.1 The Standard Model
The SM is a relativistic quantum field theory that incorporates the basic principles
of quantum mechanics and special relativity. The SM incorporates successfully
three out of four fundamental interactions in nature: the electromagnetic, the weak
and the strong interactions. The SM Lagrangian describes a non-Abelian gauge
symmetry that refers to the group SU(3)C ⊗ SU(2)L ⊗ U(1)Y , where the SU(3)Cgroup refers to the quantum chromodynamics (QCD), the theory describing theinteractions of quark and gluons by the color charge, while the SU(2)L ⊗ U(1)Ygroup refers to the electroweak interactions. The interactions are mediated bygauge bosons: gluons (strong interactions), photons (electromagnetic interaction),and the W and Z bosons (weak interactions).
All matter in nature is made up of elementary particles, called fermions. Figure 2.1 summarizes the elementary particles that appear in the SM. Their internal
quantum numbers are also shown in the figure. The fermions can be subdivided
by the interactions in which they participate. Quarks take part in the strong,electromagnetic and weak interactions, while leptons do not take part in strong
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2.1 T F  M

interactions. Leptons and quarks are arranged in three families, each family containing one charged lepton, one neutrino, and one up- and one down-type quark.
The masses of all elementary particles are given by the interaction with the
Higgs field [14, 15]. The last piece of the SM, the Higgs boson, had been searched
for at many research facilities around the world, but it was not discovered until
2012. In 2012, the ATLAS and CMS collaborations reported the strong evidenceof a SM-like Higgs boson with a mass of ∼ 126 GeV [16, 17]. So far, the measured
couplings of the boson to the SM particles are consistent with those of the SM
Higgs boson.

Figure 2.1: List of elementary particles in the Standard Model [18].

2.1.1 Outstanding issues in the Standard Model
The SM has succeeded in explaining the wide experimental results. In spite ofits success, it cannot be the ultimate description of the universe. As previously
mentioned, the SM only describes three out of four fundamental interactions innature. The SM is therefore not a theory of everything. In addition to this, the SMraises three fundamental problems without providing the answers: the hierarchy
problem, the grand unification and the existence of dark matter.
Hierarchy problem
One of the most problematic aspects of the SM is the numerical value of the Higgs
boson mass. The Higgs boson receives large radiative corrections to its mass, whichcomes from loop diagrams. For example, given a Dirac fermion f that receives its

2.1 T F  M

7

mass from the Higgs boson, the mass squared of the Higgs boson (m2h ) is given bym2h ≈ m2h,bare + m2h,1−loopZ
λ2f f Λ d4 p
2
= mh,bare − 2 Nc
8πp2
≈ m2h,bare +

λ2f
8π2

Nfc Λ2 ,

(2.1)
(2.2)
(2.3)

where mh is the physical Higgs boson mass that is measured to be ∼ 126 GeV,
mh,bare is the bare Higgs boson mass, λ f is the Yukawa coupling constant, Nfc is thenumber of colors of fermion f and Λ is the largest energy scale where the standard
model is valid. For large Λ, the bare mass and the 1-loop correction must cancel
to a large degree to give the physical Higgs boson mass. If Λ is close to the Planckscale ≈ 1019 GeV, Eq. 2.3 implies N0 ≡ m2h,bare /m2h ∼ 1030 , i.e., a fine-tuning of 1 partin 1030 .
SUSY moderates this fine-tuning [19]. If SUSY is exact and an unbroken symmetry, the Higgs boson mass receives no perturbative corrections. Due to SUSY
breaking, the Higgs boson mass becomesm2h ≈ m2h,bare +

λ2f
8π2

Nfc (m2f̃ − m2f )ln(Λ2 /m2f̃ ),(2.4)

where f̃ is the superpartner of fermion f . The quadratic dependence on Λ isreduced to a logarithmic one, and the Higgs boson mass is natural if mf̃ is not too
far above mf . The upper bound on sfermion masses is given by
 12
!1 
!1
 N0 21 3 2 
70


,
mf̃ 5 800 GeV

λf Ncf  ln(Λ2 /m2 )  100

(2.5)

f̃

where λf and Nfc are normalized to values for toq quark and the logarithm term(N0 ) is normalized to the value for Λ ∼ 1019 GeV (1 % fine-tuning), respectively.

Grand unification
In the SM, the strong, weak, and electromagnetic gauge couplings do not unify at
any scale. However, in the minimal supersymmetric standard model (MSSM) [20],which is described later, the gauge coupling renormalization group equations are
modified above the superpartner mass scale. If the superpartners are roughly at the
weak scale, the gauge couplings unify at ∼ 1016 GeV as shown in Fig. 2.2 [20]. This
fact further motivates both the grand unification and weak-scale supersymmetry.

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Figure 2.2: Predictions for the running of the inverse gauge couplings by therenormalization group equations. The evolution of coupling constants is shown by
the dashed lines for the SM and by the solid lines for the MSSM [20]. The difference
between the blue and red lines originates from changing the supersymmetricparticle masses between 500 GeV and 1.5 TeV and from changing α(MZ ) between
0.117 and 0.121.

Dark matter

The bulk of observable matter in the universe is constituted by baryons and electrons. However, predictions on the total mass of baryonic matter in the universe
from cosmic ray measurements are in contrast to those of gravitational effects.
Therefore, the matter described by the SM makes up only approximately 5 % of
the universe from cosmological observations, and a large part (∼22 %) is considered to consist of neutral, only weakly interacting, dark matter. The SM gives
no explanation for the amount of dark matter present in the universe. There arevarious hypotheses on dark matter depending on the mass and velocity of theparticles: hot, warm, and cold dark matter. Since the velocity of warm, hot dark
matter is quite high, they cannot explain the the structure of the universe, and are
not regarded as the main composition of dark matter. The most popular choice iscold dark matter. According to the latest measurements of the anisotropy of thecosmic microwave background and of the spatial distribution of galaxies [21], thedensity of cold, non-baryonic matter is calculated to be Ωnbm h2 = 0.112 ± 0.006,
where h is the Hubble constant in units of 100 km/(s·Mpc). SUSY provides an
excellent dark matter candidate when the neutralino is the LSP.

2.2 T F  M

9

Gravity
The SM does not include any description of gravity. Any attempts to incorporate
the theory of general relativity into the SM have not been successful so far. Whilegravitational effects at the electroweak scale are negligible, they become significant
at the Planck scale and are of equal strength as the other forces. Since two successive
supersymmetric transformations give rise to a translation in space-time, gravity
would automatically be a part of supersymmetric models.

2.2 Supersymmetry
As described in the previous section, the SM is not a complete theory, and new
physics beyond the SM exists. Supersymmetry is one of the most promisingtheories providing a solution to the problems in the SM. It establishes a symmetry
between fermions and bosons. The SUSY must be a broken symmetry since nosupersymmetric particles with the same mass as their SM partners have been
observed. Therefore, the masses of the superpartners must be heavier than thoseof their SM partners.

2.2.1 Minimal Supersymmetric Standard Model
The MSSM is the simplest supersymmetric extension of the SM. Its particle content
is summarized in Table 2.2.1
As the left- and right-handed states transform differently under the gauge
group, individual sparticles are introduced for left- and right-handed fermions.
These left- and right-handed supersymmetric states refer to not their own helicity
but that of their SM partner. The superpartners of fermions are called squarks
and sleptons while the superpartners of gauge bosons are called gluino, wino,
and photino. The gravitino is the only particle having a spin of 3/2. The gaugeinteractions and couplings of the sparticles are the same as those of their SM
partners.
In the SM, the electroweak symmetry is broken by introducing the Higgs
mechanism, and one Higgs doublet gives masses to all particles. In the MSSM,
the electroweak gauge symmetry suffers from a gauge anomaly, therefore, a Higgs
field must be a weak isodoublet. One of the two Higgs supermultiplets has the
Yukawa coupling that gives masses to the up-type quarks with the hypercharge
Y = 1/2, and the Yukawa coupling of the other doublet (Y = −1/2) gives masses tothe down-type quarks and the charged leptons. They are denoted by Hu and Hd ,respectively, and the vacuum expectation values (VEVs) of Hu0 and Hd0 are writtenasvu = hHu0 i,

vd = hHd0 i.

(2.6)

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2.2 T F  M

These VEVs are related to the SM parameters by
v2u + v2d = v2 =2m2Z
g21 + g22

,

(2.7)

where v, g1 and g2 are VEVs of the SM Higgs boson, the gauge coupling constantsof U(1)Y and SU(2)L gauge groups, respectively. The ratio of the VEVs is an
important parameter and set to be free in the theory, and it is traditionally writtenasvu
tan β ≡ .
(2.8)
vd
These two doublets have eight degrees of freedom. Three are absorbed by thegauge bosons of the weak interaction as in the SM, leaving five physical Higgs
bosons. These five Higgs scalar mass eigenstates consist of two CP-even neutral
scalars h0 and H0 , one CP-odd neutral scalar A0 , and charged scalars H± .
The superpartners of the Higgs bosons are called Higgsinos, denoted by H̃u andH̃d for the SU(2)L -doublet left-handed spinor fields, with weak isospin components(H̃u+ , H̃u0 ) and (H̃d0 , H̃d− ). The mass of Higgsinos is given by µH .
The superpartners of the SM gauge bosons, arranged in gauge supermultiplets,
are not the mass eigenstates of the MSSM. The Higgsinos and electroweak gauginos mix with each other due to the effects of the electroweak symmetry breaking.
The neutral Higgsinos (H̃u0 and H̃d0 ) and the neutral gauginos (B̃0 and W̃ 0 ) combine
to form four mass eigenstates called neutralinos χ̃0i (i = 1,2,3,4). The charged Higgsinos (H̃u+ and H̃d− ) and winos (W̃ + and W̃ − ) mix to form two mass eigenstates withcharge ±1 called charginos χ̃±i (i = 1, 2). The neutralinos are Majorana fermions in
the MSSM.
Name

Spin

Squarks0

Sleptons

0

Higgs bosons
Neutralinos
Charginos
GluinoGravitino

0
1/2
1/2
1/2
3/2

Gauge eigenstates Mass eigenstatesũL , ũR , d˜L , d˜Rc̃L , c̃R , s̃L , s̃Rt̃L , t̃R , b̃L , b̃Rt̃1 , t̃2 , b̃1 , b̃2ẽL , ẽR , ν̃eµ̃L , µ̃R , ν̃µτ̃L , τ̃R , ν̃ττ̃1 , τ̃2 , ν̃τh0 , H0 , A0 , H±
H0u , H0d , H+u , H−dχ̃01 , χ̃02 , χ̃03 , χ̃04
B̃0 , W̃ 0 , H̃u0 , H̃d0
χ̃±1 , χ̃±2
W̃ ± , H̃u+ , H̃d−g̃G̃

Table 2.1: Supersymmetric particles in the MSSM.

2.3 T F  M

2.2.2

11

R-parityIn the SM, the baryon number and lepton number are conserved since no possible
renormalizable Lagrangian terms can introduce the violation of such numbers. In
the MSSM, the most general gauge-invariant renormalizable superpotential contains the terms that violate the conservation of the baryon number and lepton
number, inducing the rapid proton decay. Since this decay has not been confirmed
experimentally, these terms need to be suppressed. Therefore, an additional quantum number called R-parity, is defined as
R = (−1)2S+3(B−L) ,(2.9)

where S, B and L is the spin, the baryon number, and the lepton number, respectively. Employing this definition results in R = 1 (R = 1) for all SM particles (SUSY
particles). Assuming exact R-parity conservation indicates several significant consequences:• SUSY particles can only be produced in pairs, resulting in two decay chains.
• Eventually, every sparticle decays into the LSP.
• The LSP is stable. It behaves like neutrinos and escapes from detection,
resulting in a noticeable amount of missing transverse momentum (and its
magnitude Emiss) in collider experiments. If the LSP is neutral and has noTcolor, it can be considered as a viable dark matter candidate.
The phenomenology of R-parity violating models is quite different because the
LSP decays into SM particles. Throughout this dissertation, R-parity is assumed
to be conserved.

2.2.3 SUSY breaking model
Since it is difficult to construct a realistic model of spontaneous SUSY breaking fromthe interaction between the particles in the MSSM, a hidden sector is introduced,
which consists of particles that are completely neutral with respect to the SM gauge
group. The SUSY breaking is assumed to occur in the hidden sector, and its effects
are mediated to the visible sector by some mechanisms. In the general MSSM,
105 free parameters are added to the 19 of the SM. Assuming a specific breaking
mechanism reduces the number of free parameters that determine the masses of
all SUSY particles and their mixing.

2.3 Wino LSP scenarios and their phenomenology
In this analysis, one of the most prominent models called Anomaly-MediatedSupersymmetry Breaking is explored.

12

2.3 T F  M

2.3.1 Anomaly-Mediated Supersymmetry Breaking model
Anomaly-Mediated Supersymmetry Breaking Model has no direct tree level coupling that mediates the SUSY breaking in the hidden sector to the observable sector.
A conformal anomaly in the auxiliary field of the supergravity multiplet mediates
the SUSY breaking to the observable sector, and the AMSB model does not require
any singlet SUSY breaking fields (i.e. the Polonyi fields), and hence, are free fromthe cosmological Polonyi problem [22, 23].
In the AMSB model, the masses of gauginos are generated at one-loop, while
those of the scalar bosons are generated at two-loop level, because of the conformal
anomaly that breaks scale invariance. The bino, wino, and gluino masses (M1 , M2
and M3 ) have a special property that they are proportional to the coefficients of
the renormalization group equations for their corresponding gauge groups:

M1 =
M2 =
M3 =

g21
16π2
g22
16π2
g23
16π2

33
m3/2 ),
5(2.10)(m3/2 ),

(2.11)(−3m3/2 ),(2.12)(

where g3 and m3/2 are the gauge coupling constants of SU(3)C gauge groups andthe gravitino mass, respectively. The mass relation between the gauginos becomes
M1 : M2 : M3 ≈ 3 : 1 : 8. The masses of squarks and sleptons [24] are obtained as

M2ũL

=

M2d̃

=

L

M2ũR

=

M2d̃

=

R

M2ẽL

=

M2ν˜e

=

M2ẽR

=


 m2

1 2 2
11 4 3 4
3/2
24− sin θW MZ cos2β + − g1 − g2 + 8g3, (2.13)2 3502(16π2 )2



 m2
1 1 2
11 4 3 4
3/2
22
4
m0 + − + sin θW MZ cos2β + − g1 − g2 + 8g3(, 2.14)2 3502(16π2 )2

 m2
2
88
3/2
m20 + sin2 θW M2Z cos2β + − g41 + 8g43,
(2.15)
325(16π2 )2

 m2
1 2
22 4
3/2
2
2
4
m0 − sin θW MZ cos2β + − g1 + 8g3(2.16),
325(16π2 )2



 m2
99
3
1
3/2
m20 + − + sin2 θW M2Z cos2β + − g41 − g42,(2.17)
2502(16π2 )22


99 4 3 4 m3/2
1 2
2,
(2.18)
m0 + MZ cos2β + − g1 − g22502
(16π2 )22


198 4 m3/2222
m0 − sin θW MZ cos2β + −g,(2.19)
25 1 (16π2 )2
m20 +



2.3 T F  M

13

Mass / GeV

where m0 and θW are the universal scalar mass at the grand unification scale andthe Weinberg angle, respectively.
In the AMSB model, m0 must be large otherwise the scalar leptons becometachyons. Then all sfermions become too heavy to be produced in hadron collider
experiments. Figure 2.3 shows a sparticle mass spectrum for a certain parameterset of the AMSB model.
1400
1200

H0
A0

q̃R
q̃L

H±

ℓ̃L
ℓ̃R
ν̃L

1000

t̃2
b̃1
τ̃2
ν̃τ
τ̃1

g̃
χ̃04
χ̃03

800

b̃2

t̃1
χ̃2±

600
400

χ̃02

200
χ̃01

h0

χ̃1±

0

Figure 2.3: Sparticle mass spectrum for m3/2 = 32 TeV, m0 = 1000 GeV, tan β = 5and µH > 0.

2.3.2 Pure Gravity Mediation model
There is another well-motivated SUSY model that predicts the sparticle mass spectrum similar to that in the AMSB model, called Pure Gravity Mediation (PGM)model [3]. In this model, the gauginos obtain their masses by one-loop contributions in supergravity as in the AMSB model. The difference between the AMSB
and PGM models is that the gauginos in the PGM model obtain their masses
from additional contributions from threshold effects of the heavy Higgsinos. The
gaugino masses are given by

M1 =
M2 =
M3 =

g21 331(m3/2 + L),
2
11
16π 5
2
g2
(m3/2 + L),
16π2
g23(−3m3/2 ),16π2

where L denotes the Higgsino threshold contribution given by(2.20)(2.21)(2.22)

14

2.3 T F  M

L ≡ µH sin2βm2A|µH |2ln.
|µH |2 − m2A
m2A(2.23)The size of the L parameter is expected to be of the order of the gravitino mass.
In the limit for the L parameter to zero, the gaugino masses become the same onesin the AMSB model. The gluino mass is about eight times higher than the winomass for L = 0 as in the AMSB model, while the gluino-to-wino mass ratio getssmaller for positive values of L.

2.3.3 Phenomenology
One prominent feature of these models is that the LSP is the pure neutral wino±that is mass-degenerate with the charged wino. The lightest chargino χ̃1 is slightly
0
heavier than the lightest neutralino χ̃1 due to radiative corrections involving elec0±
troweak gauge bosons. The typical mass splitting between χ̃1 and χ̃1 is ∼ 160 MeV±
as shown in Fig. 2.4, which implies that χ̃1 has a considerable lifetime and predom0
inantly decays into χ̃1 plus a low-momentum (∼ 100 MeV) π± . Figure 2.5 shows0±τχ̃± as a function of ∆mχ̃1 on the assumption that χ̃1 decays into χ̃1 + π± . The decay
1width for this process is given as

1
2 2
 ±
 2G2F

m
0 ±
π 
 ,
Γ χ̃1 → χ̃1 π =cos2 θc fπ2 ∆m3χ̃1 1 −π∆m2χ̃ (2.24)

1

where GF , θc , fπ and mπ are the Fermi coupling constant, the Cabbibo angle, thepion decay constant (' 130 MeV), and the pion mass, respectively, resulting in aconsiderably long lifetime of the chargino:cτχ̃± ∼ O(cm) (τχ̃± ∼ 0.2ns).
1

1(2.25)

Therefore, a fraction of decaying charginos could be reconstructed as tracks in the0collider experiments. The χ̃1 escapes from detection and the softly emitted π± is
not reconstructed due to its low momentum as shown in Fig. 2.6 . A track arising±from a χ̃1 with these characteristics is classified as a disappearing track that has few
associated hits in the outer part of the tracking volume.

2.3.4 Theoretical and experimental indications
There are several theoretical and experimental indications that motivate exploring
wino LSP scenarios.

15

175

1

∆mχ∼ [MeV]

2.3 T F  M

170

165

160

155
one-loop

150

two-loop145
100 150 200 250 300 350 400 450 500 550 600mχ∼± [GeV]
1

1

10

χ

τ ∼± (ns)Figure 2.4: Wino mass splitting ∆mχ̃1 as a function of the chargino mass (mχ̃± ) [25].
1
The dark green band shows ∆mχ̃1 at the one-loop level with the uncertainty inducedby the renormalization scale dependence and the red band shows ∆mχ̃1 at twoloop.

1

1
10-1

10-1

10-2

0.14 0.16 0.18

0.2

10-3
10-4
10-5
10-6
10-7 -110

1

10∆ m∼χ (GeV)
1

Figure 2.5: The τχ̃± as a function of ∆mχ̃1 .
1

2.3 T F  M
Events (a.u.)

16
1mχ∼± = 100 GeV, τχ∼± = 0.2 ns
1

1

1

1

1

1

mχ∼± = 200 GeV, τχ∼± = 0.2 ns

10-1mχ∼± = 300 GeV, τχ∼± = 0.2 ns

10-2

10-3

10-40

0.2

0.4

0.6

0.8

1

1.2

1.4 1.6 1.8
2
True π± p [GeV]T

Figure 2.6: The pT spectrum of π± in the charagino decays.
Higgs mass
The mass of the lightest Higgs boson (h0 ) in the MSSM [26] is obtained by
m2h0 ≤ m2Z cos2 2β +

m2t̃
A4t
A2t
3 2 2 2
y
m
sin
β(log
−
).
+
4π2 t tm2t
m2 12m4t̃

(2.26)t̃

The Higgs mass is required to be less than the Z boson mass at tree level. While
loop corrections increase this upper bound, the logarithmic term implies that thestop mass should be at least O(10-100) TeV to realize the Higgs boson mass ∼ 126
GeV [16,17]. Figure 2.7 shows a contour plot of the Higgs boson mass as a functionof m̃ and tanβ, where m̃ is a typical sfermion mass [4]. This figure also indicatesthat the Higgs boson mass of ∼ 126 GeV is realized if m̃ is ∼ O(10-100) TeV and thetanβ is ∼ O(1). These features are consistent with the fact that any sfermions have
not yet been observed at the LHC. The masses of sferimons in the AMSB model
are generally large as stated before, therefore, the observed Higgs boson mass canbe easily explained in the AMSB model.
Flavor Changing Neutral Current
The SUSY breaking terms for squark and slepton masses can induce too large
FCNCs [27] since squark and slepton mass matrices can be new sources of flavor
mixings and CP violation that are not related to the Cabibbo-Kobayashi-Maskawa
matrix. With generic mass matrices and O(1) CP violating phases, K0 -K̄0 mixing
requires the squark masses to be greater than 1000 TeV. However, the problemsof FCNCs and CP violation is not serious in the AMSB model since the soft SUSY
breaking parameters are given by flavor-blind radiative corrections and the massesof squarks and sleptons are large.

2.3 T F  M

17

130 GeVµ =10 TeV50.4

µ =100 TeV
4

0.6

3
120 GeVsin 2βtan β

0.5
125 GeV

0.7
0.8

2

0.9
1

102

103

104

1

m̃ [TeV]Figure 2.7: Values of the Higgs boson mass in the m̃–tanβ plane, where m̃ issfermion mass [4]. The solid (red) curves represent ones with µH = 10 TeV, while
the dashed (blue) curves µH = 100 TeV. The shaded region around each curveshows uncertainty from the top quark mass.

Wino dark matter scenario
The neutral wino can be the most natural candidate for dark matter. The thermal
relic density of neutral wino can be the present mass density of dark matter if its
mass is about 3.0 TeV [7]. For light winos with masses about a few hundred GeV,
the measured relic abundance requires a non-thermal history with a late-decaying
modulus [8] due to their large tree-level annihilation rate to W bosons. It has
been argued that moduli with a mass of O(100) TeV are consistent with Big Bang
nucleosynthesis (BBN) and lead to the right relic abundance for light winos with
masses about a few hundred GeV.

2.3.5 Previous searches and constraints
There are several constraints on wino LSP scenarios from collider experiments andindirect dark matter searches. Collider experiments can explore wino LSP scenarios and provide results that are largely independent of the model parameters.
Indirect dark matter searches can provide complementary results and set limits on
the wino dark matter especially in the non-thermal scenario, although they havea large uncertainty due to ambiguities of the dark matter profile, magnetic fields,
and interstellar radiation density.

18

2.3 T F  M

LEP2 experiment
Searches for the charginos nearly mass-degenerate with the lightest neutralino
were performed by experiments at LEP2 [28, 29, 30]. These analyzes are based on
the events with a photon from ISR and the missing energy due to LSPs escaping
the detector. Figure 2.8 shows the constraint on the ∆mχ̃1 –mχ̃± space of the AMSB
1model by combining the LEP2 results, excluding the chargino having a mass up
to 92 GeV [31].

Figure 2.8: Constraint on the ∆mχ̃1 –mχ̃± space derived by the results of the LEP2
1experiments [31].

ATLAS experiment
Prior to the search described in this dissertation, a search for charginos nearlymass-degeneratewith the lightest neutralino was also performed in pp collisions√
at s = 7 TeV with the ATLAS detector [13]. The analysis is dedicated to the direct
chargino production process and is based on the disappearing track signature.
Figure 2.9 and 2.10 show the constraint on the τχ̃± –mχ̃± space and ∆mχ̃1 –mχ̃± of
1
1
1
the AMSB model, respectively. The previous result excludes the chargino having
a mass up to 130 GeV.
Indirect dark matter searches
There are a number of constraints on wino dark matter [32,33] from indirect detection experiments of Fermi Gamma-Ray Space Telescope (Fermi) experiment [34]and High Energy Spectroscopic System (H.E.S.S.) experiment [35].

2.3 T F  M

191τχ∼± [ns]tanβ = 5, µ > 0
10

1

ATLAS

∫ L dt = 4.7 fb , s = 7 TeV-1Observed 95% CL limit (±1 σtheory )SUSYExpected 95% CL limit (±1 σexp)
ATLAS (7 TeV, 1.02 fb-1, strong prod.)
LEP2 exclusion±‘Stable’ ∼
χ

10-1

1

100

150

200

250

300

mχ∼± [GeV]11∆mχ∼ [MeV]

Figure 2.9: Constraint on the τχ̃± –mχ̃± space in pp collisions at
1
1
ATLAS detector [13].

√s = 7 TeV with thetanβ = 5, µ > 0

200

Observed 95% CL limit (±1 σtheory )SUSYExpected 95% CL limit (±1 σexp)

190

LEP2 exclusion±‘Stable’ ∼
χ

180

ATLAS

1

∫ L dt = 4.7 fb , s = 7 TeV
-1

170

160

150

140
100

150

200

250

300

mχ∼± [GeV]1

Figure 2.10: Constraint on the ∆mχ̃1 –mχ̃± space in pp collisions at
1
the ATLAS detector [13].

√s = 7 TeV withThe Fermi result is derived by 24 months of data for ten satellite galaxies, andsets the limits on the annihilation cross-section of the wino dark matter by a search
for the continuum photons generated mostly from the fragmentation of hadronic

20

2.3 T F  M

0 0
final states in the tree-level processes χ̃1 χ̃1 → W+ W− . This is roughly comparable
to that obtained from the antiproton flux measurement by PAMELA [36]. The
antiproton measurement is subjected to uncertainties related to their propagation
models and dark matter profiles, therefore, PAMELA results are not shown in thisdissertation.
The H.E.S.S. collaboration investigated gamma-ray lines expected to arise from0 0
0 0
the one-loop processes χ̃1 χ̃1 → γ γ and χ̃1 χ̃1 → Z γ in a 1◦ radius circle at the
Galactic Center, in which the Galactic plane is excluded by restricting the Galactic
latitude to |b| > 0.3◦ .
Figure 2.11 and 2.12 show the theoretical calculations of annihilation crosssection into W+ W− , γ γ and γ Z final states and the constraints on the winoannihilation cross-section as a function of wino mass [32]. The wino relic density
is assumed to equal to its thermal abundance in Fig. 2.11, while it is assumed
to equal to the measured value due to a non-thermal history with a late-decaying
modulus in Fig. 2.12. In the thermal scenario, the data from H.E.S.S. rules out wino
dark matter in the mass range 1.6 TeV – 3.1 TeV. In the non-thermal scenario, thedata from Fermi and H.E.S.S. exclude wino dark matter in the ranges 100 GeV –500 GeV and 500 GeV – 3.0 TeV, respectively.

Direct dark matter searches
The direct detection of dark matter can take place through their interaction with
nuclei inside a detector. However, couplings of neutral wino to a Higgs bosonor a Z boson are highly suppressed at the tree-level, therefore, there has been no
direct detection limits on wino LSP scenarios. The elastic scattering of a wino off
a nucleon occurs at one-loop where wino couples to quarks and two-loop wherewino couples to gluons in the nucleon. The associated spin-independent crosssection is O(10−47 ) cm2 for winos [37, 38] having masses 50 GeV – 3 TeV, which iswell below the strongest direct detection limits from the Xenon100 experiment [39].

21

10-22
-21

10

-23

10

10-22

σγ γ + γ Zv [cm3/s]

σWWv [cm3/s]

2.3 T F  M

10-24
-23

10

-25

10
10-24

-26

-25

10

ATLAS ( s = 7 TeV,

∫ L dt = 4.7 fb )H.E.S.S.

Annihilation cross-section: σWWv

Fermi

Annihilation cross-section: σγ γ + γ Zv

10

-1

10-27

-26

10

0.5

1

1.5

2

2.5

3

mχ∼0 [TeV]1Figure 2.11: Constraints on the annihilation cross-section as a function of thewino mass [32]. The theoretical cross-section of annihilation into W+ W− , γ γ
and γ Z final states are also indicated by the dashed red line and the solid blueline, respectively. The excluded regions by the Fermi and H.E.S.S. experiments
are indicated by the shaded red region and the shaded blue region, respectively.
These exclusion contours are obtained on the assumption that the wino abundanceis set by thermal freeze-out. The shaded yellow region between the dotted lines
corresponds to Ωh2 = 0.12 ± 0.06. In the black shaded region, a thermal wino
exceeds the observed relic density.

10-22

10-21
-23

10
-22

10

10-24

σγ γ + γ Zv [cm3/s]

2.3 T F  M

σWWv [cm3/s]

22

-23

10

-25

10
10-24

10-26
-25

10

ATLAS ( s = 7 TeV,
-26

10

10-27

0.5

∫ L dt = 4.7 fb )

10-27

-1

H.E.S.S.

Annihilation cross-section: σWWv

Fermi

Annihilation cross-section: σγ γ + γ Zv

1

1.5

2

2.5

3

mχ∼0 [TeV]1Figure 2.12: Constraints on the annihilation cross-section as a function of thewino mass [32]. The theoretical cross-section of annihilation into W+ W− , γ γ
and γ Z final states are also indicated by the dashed red line and the solid blueline, respectively. The excluded regions by the Fermi and H.E.S.S. experiments
are indicated by the shaded red region and the shaded blue region, respectively.
These exclusion contours are obtained on the assumption that the wino comprises
all dark matter due to a non-thermal history.

Chapter 3

LHC and ATLAS Detector
3.1 Large Hadron Collider
The LHC is a two-ring-superconducting-hadron accelerator and collider built by
the european organization for nuclear research (CERN). It lies between 45 m and
170 m below the surface on a plane inclined at 1.4 %. It was installed in theexisting 26.7 km tunnel that was built for the LEP experiment. The LHC has been
designed to collide proton beams with a center-of-mass energy of 14 TeV and an
instantaneous luminosity of 1034 cm−2 s−1 . It can also collide heavy lead (Pb) ionswith an energy of 2.8 TeV per nucleon and a peak luminosity of 1027 cm−2 s−1 .
The protons are obtained from a duoplasmatron source. It ionizes hydrogenatoms and uses electric fields to accelerate the protons away from the electrons,
resulting in a 92 keV proton beam. The protons are then formed into bunches andinjected into the linear particle accelerator (LINAC) where they are accelerated to
50 MeV before being fed into the first circular accelerator in the chain, the proton
synchrotron booster (PSB). The PSB accelerates the protons to 1.4 GeV and injects
them into the proton synchrotron (PS), which accelerates the protons to 25 GeV
before being injected into the super proton synchrotron (SPS), which accelerates
the protons to 450 GeV. These protons are finally injected into the LHC, which
accelerated each beam of protons to 4 TeV in 2012, resulting in a center-of-massenergy of 8 TeV. The proton beam parameters of the LHC and CERN accelerator
complex are shown in Table 3.1 and Fig. 3.1, respectively.
Four main experiments have been installed in four of the eight interaction
points (IPs) of the LHC ring: ATLAS [40, 41, 42] and CMS [43] are general-purpose
detectors for studying pp collisions at the highest luminosity and at the maximum
center-of-mass energy. The LHCb [44] experiment is dedicated to heavy flavor
physics. It is designed to search for indirect evidences of new physics in CP
violation and rare decays of beauty and charm hadrons, which can help to explain
the matter-antimatter asymmetry of the universe. ALICE [45] is an experimentfocused on the study of heavy ion collisions at the LHC. It is designed to study thephysics of strongly interacting matter and the quark-gluon plasma at an extreme
2324

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Proton energy [GeV]
Bunch spacing [ns]Number of bunches
Number of particles per bunch
Peak luminosity [cm−2 s−1 ]Magnetic field on the dipoles [T]Beam current [A]Bunch length [cm]Transverse beam size at IP [µm]
Crossing angle at IP [µrad]

Design2012 Run

7000
25
2808
1.15×1011
1.0×1034
8.33
0.582
7.55
16.7
285

4000
50
1380
∼ 1.7 × 1011
7.7 × 1033
4.76
0.369
≥9
19
290

Table 3.1: The LHC proton beam parameters.

Figure 3.1: The LHC accelerator complex. The protons are accelerated by the
LINAC, the PSB, the PS, and the SPS accelerators prior to injection into the LHC.

3.2 LHC  ATLAS D

25

energy density and temperature.

3.2 ATLAS detector
The ATLAS (A Toroidal LHC ApparatuS) detector is a general purpose detector
at the LHC. The ATLAS detector covers nearly 4π around the collision point
and is 45 m long, 25 m in diameter, and weighs about 7000 tons. It is designed toaccommodate a wide spectrum of possible physics signatures such as the discovery
of Higgs boson and beyond the SM.
The ATLAS detector consists of an ensemble of sub-detectors with a cylindricalshape placed around the IP: the inner detector (ID), which is used for tracking of
charged particles, the calorimeters, which are used for measuring the energies ofthe particles interacting with them, and the muon spectrometer, which is used for
identifying muons and measuring their momenta. A magnet system is used tobend the charged particles to measure their momenta. The layout of the ATLAS
detector is shown in Fig. 3.2. A trigger system is also implemented, which is used
to decide which events should be recorded in a very short time.

Figure 3.2: Three dimensional view of the ATLAS detector.

3.2.1 ATLAS coordinate
ATLAS uses a right-handed Cartesian coordinate system (x,y,z) with its origin at
the nominal IP in the center of the detector and the z-axis coinciding with the axis

26

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of the beam pipe. The x-axis points from the IP to the center of the LHC ring,and the y-axis points upward. A cylindrical coordinate system (r, φ, z) and a polar
coordinate system (r, θ, φ) are defined in the transverse plane asq
x2 + y 2 ,yφ = arctan( ),xrθ = arctan( ).
z
r =

(3.1)
(3.2)
(3.3)

The rapidity (yrap ) is defined as!
E + pz1
yrap = ln,2
E − pz(3.4)

where E and pz are the energy and the momentum along the beam direction,
respectively. The pseudo-rapidity (η), which is equal to the rapidity in the ultrarelativistic approximation, is also often used in collider physics. It is defined interms of the polar angle θ as
η = − ln tan

 
θ
.
2

(3.5)

The distance in the pseudorapidity-azimuthal angle space (∆R) is defined as∆R =q(∆η)2 + (∆φ)2 ,(3.6)

where ∆η and ∆φ are the distance in the pseudorapidity and azimuthal angle
space, respectively.

3.2.2 Magnet system
The ATLAS magnet system consists of four superconducting magnets. A central
solenoid aligned on the beam axis provides the ID with an axial magnetic field,
and three large air-core toroids located outside the calorimeters provide the muon
spectrometer with a toroidal magnetic field. This configuration is shown in Fig. 3.3.
The solenoid magnet system
The central solenoid [46] is aligned on the beam axis and designed to provide
the ID with a 2 T axial magnetic field. The solenoid is a layer coil made of a
Al-stabilized NbTi conductor. It has an inner (outer) diameter of 2.46 (2.56) m withan axial length of 5.8 m. The flux of magnetic field is returned by the steel of thehadronic calorimeter.

3.2 LHC  ATLAS D

27

Since the solenoid magnet lies in front of the calorimeter system, its presence
must have a small impact on the energy measurement in the calorimeters. Therefore, it is designed to keep the material thickness as low as possible, contributing
with a total of ∼ 0.66 radiation length (X0 ). For this requirement, the solenoid
windings and the electromagnetic calorimeter share a common vacuum vessel toeliminate two vacuum walls.
The toroidal magnet system
The toroidal magnet system [47] consists of one barrel and two endcap magnets.
It provides a toroidal magnetic field of 0.5 T and 1 T for the muon spectrometer inthe central and endcap regions, respectively. It lies outside the calorimeters andwithin the muon system. The conductor and coil-winding technology is based on
winding a pure Al-stabilized Nb/Ti/Cu conductor.
The barrel toroid consists of eight coils encased in individual racetrack-shaped
stainless-steel vacuum vessels. It provides a peak magnetic field of 3.9 T. The coils
are assembled radially and symmetrically around the beam axis. It has an inner(outer) diameter of 9.4 (20.1) m with a length of 25.3 m.
The endcap toroids are placed inside the barrel toroid at both ends of the central
solenoid. They have an inner (outer) diameter of 1.65 (10.7) m with an axial lengthof 5 m. Each endcap is rotated by 22.5◦ with respect to the barrel toroid in order toprovide the radial overlap and optimize the bending power. Each endcap toroid
consists of a single cold mass built from eight coils in an aluminum alloy housing.
The endcap toroids provide a peak magnetic field of 4.1 T.

3.2.3 Inner detector
The inner detector [48, 49] is designed to reconstruct the trajectories of charged
particles in the region |η| < 2.5. Figure 3.4 (a) and (b) show an overview and alayout of the ID, respectively. The ID is contained within a cylindrical envelope oflength ±3512 mm and of radius 1150 mm. The ID is surrounded by the solenoid
magnet with the 2 T axial magnetic field in order to measure the momentum of
charged particles.
The ID consists of pixel and silicon microstrip (SCT) detectors inside a strawtube transition radiation tracker (TRT). The pixel detector consists of three barrel
layers and four endcap disks on each side, providing an average three measurement points for charged tracks. The SCT is composed of four cylindrical layersof double-sided silicon microstrip modules, with nine disk layers in each endcap
region; eight silicon microstrip sensors are typically crossed by each track. The
TRT, of particular importance to this search, covers |η| < 1.0 with its barrel detector,
0.8 < |η| < 2.0 with the endcaps, and the radial range 563–1066 mm. The average
number of TRT hits on a track traversing the ID in the central region is about 32.
The first two sub-detectors use semiconductor technology and provide a small
number of hits on a track, while the TRT uses straw tubes providing a continuous

28

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Figure 3.3: The ATLAS magnet system. The solenoid lies at the innermost radiusof the calorimeter system, and the steel of the hadronic calorimeter act as a returnyoke for the solenoids field. The eight barrel toroid coils are interleaved with thetwo endcap toroid coils.
tracking up to large radii. Each of the sub-detectors is divided in a barrel region,
where they are arranged in concentric cylinders around the beam line, and an
endcap region, where the detecting elements are mounted on disks orthogonal tothe beam axis.
Since all material between the IP and the calorimeters degrade the resolutionof the measured energy, the three sub-detectors must be designed so that theyminimize the quantity of matter to be crossed by the particles, and also to support
the high-radiation conditions.
Pixel detector
The innermost tracking subsystem of the ID is the semiconductor pixel detector [50]. The layout of the pixel detector and an assembly view of a pixel module
are shown in Fig. 3.5. The detector is designed to provide at least three precise
measurement points for tracks with |η| < 2.5. The pixel detector consists of three
concentric barrel layers placed at radii of 50.5, 88.5, and 122.5 mm around the beamaxis covering the central region up to |η| = 1.9 and three endcap disks placed at |z|
positions of 495, 580, and 650 mm in each direction along the beam axis from theIP, extending the total coverage up to |η| = 2.5.
Each module has an active region with an area of 16.4 mm × 60.8 mm and 250µm thickness. The sensor is composed of an oxygen-rich n-type bulk segmented
into 47232 n+ -in-n pixels whose typical size is 50 µm × 400 µm. The direction

3.2 LHC  ATLAS D

29

(a)

(b)

Figure 3.4: Overview (a) and a layout (b) of the ATLAS inner detector.

30

3.2 LHC  ATLAS D

defined by the shorter pitch is the local x-coordinate on the module and corresponds to the high precision measurement in the r−φ plane. The longer pitch isoriented approximately along the z direction in the barrel and radially in the endcaps. A module is read out by 16 radiation-hard front-end chips bump-bonded tothe sensor. In order to cover the clearance region between front-ends, some pixels
have a longer size of 600 µm or are ganged in pairs to a single read-out channel.
The charge collected in each pixel is measured with 8-bit dynamic range using
the Time over Threshold (ToT) technique: the width of the discriminator output ismeasured by counting the number of 25 ns clock cycles during which the signal
is above a threshold. Since the ToT is a sub-linear function of the charge, it gives
precise information about the ionization losses of charged particles crossing pixel
modules.
The pixel detector is the most important detector used for the identification andreconstruction of secondary vertices. Moreover, it provides an excellent spatial
resolution for reconstructing primary vertices even in the presence of multiple
interactions in pp collisions. Its intrinsic spatial resolution is 10 µm (r-φ) and 115µm (z) in the barrel, and 10 µm (r-φ) and 115 µm (r) in the disks.

Figure 3.5: Schematic view of the active region of the ATLAS pixel detector (left)
and an assembly view of a pixel module (right)

Semiconductor tracker
The SCT [51,52,53] is composed of a barrel and two endcap sections. The barrel has
four coaxial cylindrical layers at radii 299, 371, 443, and 514 mm around the beamaxis in the radial direction, covering the central region up to |η| = 1.1. Each endcap
region has nine disks of different sizes located between 854 and 2720 mm from the
IP along the beam axis, providing a total coverage up to |η| = 2.5. The detector issegmented in 4088 modules: there are 2112 (1976) rectangular (trapezoidal) shaped
modules in the barrel (endcap) region, covering a total surface of 63 m2 . Figure 3.6
shows a photograph of the SCT barrel detector and an assembly view of an SCT
module.

3.2 LHC  ATLAS D

31

The modules are made of silicon-strip devices that arrange the strips parallelto the beam axis in the barrel and radially in the endcap, providing a precise
measurement in the r-φ plane. The modules consist of one or two pairs of singlesided p-in-n microstrip sensors. The sensors are divided into 768 AC-coupled
strips with a constant pitch of 80 µm in the barrel, while in the endcap it varies
from 56.9 to 94.2 µm due to the trapezoidal shape. Two layers of sensors arerotated with respect to one another by a 40 milliradian stereo angle. This ensuresthat a charged particle coming from the IP typically crosses eight strip sensors,
contributing to the precise measurement of momentum, impact parameter andvertex position. The position resolution is ∼ 17 µm in lateral plane (r-φ) and ∼ 580µm in longitudinal direction.
A hit is registered only if the pulse height exceeds a preset threshold, which
normally corresponds to a charge of 1 fC. The charge measured in the strip is stored
for a period of ∼ 3.2 µs and used for tracking if the trigger indicates that the event
should be considered in more detail. Each module is read out by 12 identical 128channel front-end ASICs, resulting approximately in 6.3 million readout channels.

Figure 3.6: Photograph of the SCT barrel detector (left) and an assembly view ofan SCT module (right).

Transition radiation tracker
The TRT [54, 55, 56] is a straw-tube tracker. Figure 3.7 shows photographs of theTRT barrel and endcap modules.
Transition radiation is a phenomenon that a relativistic particle emits photons
when it crosses a boundary between two media with different dielectric constants.
When a relativistic particle passes through the boundary between vacuum and the
medium, it emits photons with the energy (WTR ),1(3.7)
WTR = αωp γ,
3where α, γ and ωp are the fine structure constant, the Lorentz boost factor of theparticle and a plasma frequency, respectively. The plasma frequency ωp is defined

32

3.2 LHC  ATLAS D

Figure 3.7: Photographs of the TRT barrel (left) and endcap (right) modules.
by
r
ωp =

Ne e2
,
0 me

(3.8)

where Ne , 0 and me are the electron density of the medium, the dielectric constant,and the electron mass, respectively. As shown in Eq. 3.7, an emitted energy
is proportional to a Lorentz boost factor of a charged particle. Therefore, thetransition radiation is used for the identification of electrons to distinguish themfrom heavier charged particles.
The diameter of drift tube is 4 mm, and it is made from wound Kapton andreinforced with thin carbon fibers. The anode wire is formed by a gold-plated
tungsten whose diameter is 31µm diameter. The cathode is a tube made form
carbon, aluminum and Kapton, and it has an approximately 60 µm thickness. The
tubes are filled with Xe/CO2 /O2 gases in the proportions of 70/27/3 %. The xenon
gas is used for a good X-ray absorption, and CO2 and O2 are used to increase theelectron drift velocity and for a photon-quenching.
When a charged particle traverses the TRT, it ionizes the gas inside the tubes.
The electrons generated by the ionization drift towards the wire, where they areamplified and read out. The front-end electronics samples the incoming signal in
24 time bins of 3.12 ns and compare it against a threshold corresponding to 300
eV as shown in Fig. 3.8. The TRT is designed so that charged particles with pT >0.5 GeV and |η| < 2.0 cross at least 32 straws, except in the transition region (0.8
< |η| < 1.2) where this number decreases to at least 22 straws 1 . This large numberof tracking points allows almost continuous tracking that improves the precisionof the momentum measurement.
The spaces between the straws are filled with polymer fibers (barrel) and foils(endcaps) to create transition radiations. Typical photon energies are 5–30 keV.
These soft X-rays can be absorbed by the xenon gas, depositing additional energy
in the gas and leading to significantly higher readout signals. Such signals are
1

See Fig. 8.10.

3.2 LHC  ATLAS D

33

detected by comparing them against an additional high threshold of 6 keV that is
sampled in three 25 ns time bins alongside the pattern described above.

Figure 3.8: Signal pulse generated by ionized electrons and transition radiation
photons.
The TRT detector is composed of a barrel and two endcap sections. The TRTbarrel consists of three layers (Type-1, Type-2 and Type-3) of 32 modules each.
The straws are parallel to the beam axis, and they occupy the region between
563 < r < 1066 mm and |z| < 712 mm, corresponding to a coverage of |η| < 0.7.
The innermost nine layers in |z| < 400 mm of the Type-1 module are not active to
reduce the occupancy. The wires are electrically split in the center and are read out
at both ends, thus reducing the occupancy but doubling the number of electronic
channels. There are two types of detectors for the endcap TRT, called Type-A and
Type-B. There are total 160 layers of straws in each TRT endcap along the z-axis,
and each layer contains 768 radially oriented straws of 37 cm length with uniform
azimuthal spacing but stepped, layer to layer, in φ.

3.2.4 Calorimeter system
The ATLAS calorimeter system [57] is placed outside the solenoid magnet. It consists of several non-compensating sampling calorimeters. It has a φ-symmetriccoverage around the beam axis spanning the region |η| < 4.9 with a varying granularity in η × φ. The electromagnetic (EM) calorimeter has a high granularity that isnecessary for precise measurements of the energy of electrons and photons. It also

34

3.2 LHC  ATLAS D

contributes to measure the energy of hadrons in jets. The hadronic calorimeter hasa coarser granularity, but it is sufficient to reconstruct hadronic jets and missing
transverse momentum.
There are four calorimeters in ATLAS detector: the liquid argon (LAr) EM
calorimeter [58] (|η| < 3.2), the tile hadronic calorimeter [59] (|η| < 1.7), the LAr
hadronic endcap calorimeters [60] (1.5 < |η| < 3.2), and the LAr forward calorimeter [61] (3.1 < |η| < 4.9). The overall system is illustrated in Fig. 3.9.

Figure 3.9: Cut-away view of the ATLAS calorimeter system.

3.2.5 The electromagnetic calorimeter
Figure 3.10 shows a cross-section of the EM calorimeter. It is a lead-LAr detector
with accordion-shaped Kapton electrodes and lead absorber plates. The accordion
geometry provides a full coverage in the φ direction without azimuthal cracks anda fast extraction of the signal at the rear or at the front of the electrodes.
The accordion geometry allows it to have several active layers. The EM
calorimeter is therefore segmented into three longitudinal layers in the region|η| < 2.5. Figure 3.11 shows a schematic view of a barrel module of the EM
calorimeter. The first layer is equipped with narrow strips in η with a pitch of ∼ 4mm, and has a depth of ∼ 6 X0 including dead material and the pre-sampler described below. It works as a pre-shower detector, helping to identify particles andproviding a precise position measurement in η. The second layer is transversely
segmented into square towers with a total depth of ∼ 24 X0 . The last layer has a3.2 LHC  ATLAS D

35

coarser granularity in η with a depth between 2 X0 and 12 X0 . For the region |η| >2.5, the EM calorimeter is segmented into two longitudinal layers with a coarse
lateral granularity. For the region |η| < 1.8, the EM calorimeter is preceded bya pre-sampler detector with a depth of ∼ 2X0 installed behind the cryostat. The
pre-sampler consists of an active LAr layer of depth 1.1 (0.5) cm in the barrel (endcap) region. The pre-sampler is designed to estimate and correct energy losses by
the interaction with material upstream of the EM calorimeter. A large amount ofmaterial is situated in front of the EM calorimeter in the region 1.37 < |η| < 1.52,
resulting in reduced energy and degraded position resolutions for electrons andphotons. Table 3.2 summarizes the parameters of the EM calorimeter and Fig. 3.12
shows the cumulative amounts of material in units of X0 and as a function of |η| infront of and in the EM calorimeters.
The energy resolution of the EM calorimeter (∆E) is given by
11%
∆E
= √
⊕ 0.4%,
E
E GeV

(3.9)

where E is an energy measured in the EM calorimeter. The first term describes
the statistical fluctuations in the sampling material and the constant second term
describes systematic uncertainties from an inhomogeneous material distribution.

Figure 3.10: Cross-section of the LAr
electromagnetic calorimeter.

Figure 3.11: Schematic view of a barrel
module of the LAr EM calorimeter.

3.2.6 Hadronic calorimeter
The hadronic calorimeter mainly contributes to reconstruct jets and missing transverse momentum. The ATLAS hadronic calorimeter consists of three types ofdetectors: the tile calorimeter (TileCal), the LAr hadronic endcap (HEC), and theforward calorimeter (FCal). They cover different η ranges and have granularity

36

3.2 LHC  ATLAS D

BarrelEnd-cap

Number of layers and |η| coverage
EM pre-sampler
EM calorimeter

LAr hadronic end-cap calorimeter
LAr forward calorimeter
Tile calorimeter1
3
2|η| < 1.52
|η| < 1.35
1.35 < |η| < 1.475

3|η| < 1.0

1
2
3
2
4
3
3

1.5 < |η| < 1.8
1.375 < |η| < 1.5
1.5 < |η| < 2.5
2.5 < |η| < 3.2
1.5 < |η| < 3.2
3.1 < |η| < 4.9
0.8 < |η| < 1.7

0.025 × 0.1
0.050 × 0.1
0.025 × 0.1
0.025/8 × 0.1
0.025/6 × 0.1
0.025/4 × 0.1
0.025 × 0.1
0.1 × 0.1
0.050 × 0.025
0.025 × 0.025
0.1 × 0.1
0.050 × 0.025
0.1 × 0.1
0.2 × 0.2
3.0 × 2.6
0.75 × 0.65
0.75 × 0.65
3.3 × 4.2
0.825 × 1.05
0.825 × 1.05
5.4 × 4.7
1.35 × 1.175
1.35 × 1.175
0.1 × 0.1
0.2 × 0.1

1.5 < |η| < 1.8
1.375 < |η| < 1.425
1.425 < |η| < 1.5
1.5 < |η| < 1.8
1.8 < |η| < 2.0
2.0 < |η| < 2.4
2.4 < |η| < 2.5
2.5 < |η| < 3.2
1.375 < |η| < 1.425
1.425 < |η| < 2.5
2.5 < |η| < 3.2
1.5 < |η| < 2.5
1.5 < |η| < 2.5
2.5 < |η| < 3.2
3.15 < |η| < 4.30
3.10 < |η| < 3.15
4.30 < |η| < 4.83
3.24 < |η| < 4.50
3.20 < |η| < 3.24
4.50 < |η| < 4.81
3.32 < |η| < 4.60
3.29 < |η| < 3.32
4.60 < |η| < 4.75
0.8 < |η| < 1.7
0.8 < |η| < 1.7

Granularity ∆η × ∆φ in η range
EM pre-sampler
EM calorimeter 1st layer

0.025 × 0.1
0.025/8 × 0.1
0.025 × 0.025|η| < 1.52
|η| < 1.40
1.40 < |η| < 1.475

EM calorimeter 2nd layer

0.025 × 0.025
0.075 × 0.025|η| < 1.40
1.40 < |η| < 1.475

EM calorimeter 3rd layer
LAr hadronic end-cap calorimeter

0.050 × 0.025|η| < 1.35

0.1 × 0.1
0.2 × 0.1

|η| < 1.0
|η| < 1.0

LAr forward calorimeter 1st layer

LAr forward calorimeter 2nd layer

LAr forward calorimeter 3rd layer

Tile calorimeter 1st, 2nd layer
Tile calorimeter 3rd layer

Table 3.2: Main parameters of the ATLAS calorimeter system.

3.2 LHC  ATLAS D

37

Figure 3.12: Cumulative amounts of material in units of X0 as a function of |η| infront of and in the EM calorimeters. The left (right) figure shows the cumulative
amounts of material in the barrel (endcap) region.
suited to each regions. The granularity and coverage of the hadronic calorimeters
are shown in Table 3.2. Figure 3.13 shows the cumulative amount of material inunits of interaction length as a function of |η| in front of and in the electromagnetic(hadronic) calorimeters.

Figure 3.13: Cumulative amounts of material in units of interaction length as afunction of |η| in front of and in the electromagnetic (hadronic) calorimeters. The
total amount of material in front of the first active layer of the muon spectrometeris also shown by a light-blue filled area.

Tile calorimeter
The TileCal is a sampling calorimeter that uses 14 mm thick steel plates as absorber material and 3 mm thick plastic scintillators as active medium. It is placed
directly outside of the EM calorimeter in the central barrel region. Figure 3.14 and38

3.2 LHC  ATLAS D

3.15 show a schematic view of the TileCal and a photograph of scintillating tiles,
respectively. The central barrel part covers the region |η| < 1.0 and two extended
barrels cover the region 0.8 < |η| < 1.7. It is longitudinally segmented in threelayers with approximately 1.5, 4.1, and 1.8 interaction length for the barrel and 1.5,
2.6, and 3.3 interaction length for the extended barrel. Two sides of the scintillatingtiles are read out by wavelength shifting fibers into two separate photomultipliertubes (PMTs). This provides a very fast signal and is used in the trigger system.

Figure 3.14: Schematic view of the TileCal.

Figure 3.15: Photograph of scintillating tiles.

LAr hadronic end-cap calorimeter
The hadronic calorimeter is extended in the region 1.5 < |η| < 3.2 by the HEC thatis a copper/LAr sampling calorimeter with a flat-plate design. The copper-plateacts as absorber, while the LAr works as active medium. The LAr is used due toits high radiation tolerance, which is needed in the forward regions.
The HEC consists of two wheels in each end-cap: a front wheel (HEC1) and arear wheel (HEC2). The wheels are cylindrical with an outer radius of 2030 mm.
Each of four HEC wheels consists of 32 identical modules. Figure 3.16 shows aschematic view of a module of the HEC. The modules of the front wheels are madeof 24 copper plates of 25 mm thick. In the rear wheels, the module consists of 16
copper plates of 50 mm thick. The gap between the plates is 8.5 mm thickness.
LAr forward calorimeter
The FCal is integrated into the end-cap cryostats covering the region 3.1 < |η| < 4.9.
The FCal has approximately ten interaction length and consists of three modules
in each endcap: the electromagnetic calorimeter with copper as its absorbing
material (FCal1) and the hadronic calorimeters with tungsten as their absorbing

3.2 LHC  ATLAS D

Figure 3.16: Schematic view of the
HEC.

39

Figure 3.17: Schematic view of theFCal.

material (FCal2 and FCal3). Figure 3.17 shows a schematic view of the FCal. In
each module, the calorimeter consists of a metal matrix with regularly spaced
longitudinal channels filled with the electrode structure. The gaps between therod and matrix are filled with LAr, resulting in a faster signal with a drift time of
60 ns, while for the hadronic ones it scales with the gap size.

3.2.7 Muon spectrometer
The muon spectrometer (MS) [62] is the outermost part of the ATLAS detector. It is
designed to detect charged particles that traverse the calorimeters and to measure
their momenta in the region |η| < 2.7. The MS can identify muons with momenta> 3 GeV, and the momentum resolution ranges between 3 and 4 % for 10 < pT <500 GeV and 10 % for pT ∼ 1 TeV. The measurement is based on the magnetic
deflection of muon tracks in the large superconducting air-core toroid magnets.
The chambers are arranged such that particles emerging from the IP can traverse
three stations of chambers. The magnetic field in the region |η| < 1.0 is generated
solely by the barrel toroids, while it is served by the endcap magnets in the region
1.4 < |η| < 2.7. The MS is also designed to trigger on events with muons traversing
the calorimeters in the region |η| < 2.4. The region 1.0 < |η| < 1.4 is referred to astransition region where the magnetic field is a combination of the fields from boththe barrel and endcap toroids.
The MS is formed by a barrel and two endcap regions. The barrel chambers
form three cylinders concentric with the beam direction, placed at radii of approxi-

40

3.2 LHC  ATLAS D

mately 5, 7.5, and 10 m away from the beam axis, covering the region |η| < 1. In thetwo endcap regions, the chambers are arranged in four disks located at |z| ∼ 7.4,
10.8, 14, and 21.5 m from the IP and concentric with the beam axis, covering theregion 1 < |η| < 2.7. The MS provides a nearly full coverage except in the region η∼ 0 where a gap is left open for the passage of cables and services of the ID, central
solenoid and the calorimeters.
The MS is equipped with four different kind of chambers: monitored drift tube
chambers (MDT), cathode strip chambers (CSC), resistive plate chambers (RPC),
and thin gap chambers (TGC). The first two are used for precise measurementsof muon tracks, while the other two are used for the trigger and data acquisition
system. They are illustrated in Fig. 3.18.

Figure 3.18: Cut-away view of the ATLAS muon system.

The high-precision tracking chambers
The high-precision tracking chambers are built out of two kinds of detectors:
MDT and CSC. The MDT chambers cover the region |η| < 2.0, while the CSC
chambers cover the region 2.0 < |η| < 2.7. The precise measurement of the muontracks is made in the r-z projection. The z coordinate is measured in the barrel,

3.2 LHC  ATLAS D

41

while the radial coordinate is measured in the transition and endcap regions. A
measurement of φ is also obtained by the CSC in the forward region.
The MDT chambers are made of aluminum drift tubes of 30 mm diameter filledwith Ar/CO2 gases in the proportions of 93/7 % at a pressure of 3 bar. A 50 µm
diameter tungsten-rhenium (W-Re) wire is used as an anode. The wire is kept
at 3080 V, and the maximum drift time from the wall to the wire in the tube isabout 700 ns. There are 1150 MDT chambers; 592 of them are located in the barrel
and 558 at the endcap regions. The chambers consist of two groups of tube layers(multilayers) that are separated by a mechanical spacer. In the innermost layer,
each multilayer consists of 4 tube layers, while in the middle and outer layer, itconsists of three tube layers. The mechanical structure of a MDT chamber is shownin Fig. 3.19. The average resolution in the z direction is about 80 µm per tube, or∼ 35 µm per chamber.
The CSC system is formed by two disks with eight chambers each, segmented
in φ into large and small chambers as shown in Fig. 3.20. It has 32 chamberswith a total area of 65 m2 . The chambers are operated at 900 V and take up theradial space between 881 mm and 2081 mm. The CSC is a multiwire proportional
chamber filled with Ar/CO2 gases in the proportions of 80/20 %. The anode wires
are made of gold-plated tungsten with 3 % rhenium and have a diameter of 30µm. The cathode planes are segmented into strips in orthogonal directions, whichallows both coordinates to be measured: the cathode with the strips perpendicular
to the wires provides the precise coordinate, while the one parallel to the wires
provides the transverse coordinate. The resolution of a chamber is 40 µm in thebending plane and about 5 mm in the transverse plane. Each chamber has a similar
configuration to the multilayer of the MDT system with four wire planes but witha finer granularity, resulting in four independent measurements in η and φ alongeach track. A four layer chamber consists of five flat rigid panels, each made ofsheet of polyurethane foam and two 0.82 mm thick copper-clad laminates, where
the 17 µm thick copper cladding forms the cathodes. The electron drift times areless than 40 ns, which results in a timing resolution of about 7 ns per plane.
The trigger chambers
The trigger chambers are constructed out of two kinds of detectors: RPC in the barrel and TGC in the endcap region. They complement the high-precision tracking
chambers with a system of fast trigger providing information within a few tensof nanoseconds on muon tracks traversing the detector. Three layers of trigger
chambers are arranged in the barrel, while a fourth layer is added in the endcap
region. In the barrel, the middle layer of the MDT is sandwiched by two layersof RPC, while the third RPC layer is placed close to the outer MDT layer. In theendcap region, one TGC layer is in front of the second MDT wheel, while twolayers are behind it. The fourth TGC layer of the endcap region is placed in frontof the innermost tracking layer. The whole system is illustrated in Fig. 3.21.
The RPC is a gaseous parallel electrode-plate detector covering the region

42

Figure 3.19: Mechanical structure ofa MDT chamber showing the multilayers and the alignment system.

3.2 LHC  ATLAS D

Figure 3.20: Photograph of a CSC
endcap showing the eight small andeight large chambers.

|η| < 1.05. A RPC chamber is formed by two rectangular units contiguous toeach other. Each unit is composed of two independent detector layers filled withC2 H2 F4 /Iso-C4 H10 /SF6 gases in the proportions of 94.7/5/0.3 %, and is readout
by two orthogonal sets of pick-out strips. A detector consists of two resistive
plates made of phenolic-melaminic plastic laminate, kept at a distance of 2 mm byinsulating spacers. The electric field between the plates is 4.9 kV/mm. The signal
is read out by two metallic strips that provide a measurement in both the z and φcoordinates, with a spatial resolution of 10 mm and a timing resolution of 1.5 ns.
The TGC is a multiwire proportional chamber covering the region 1.05 < |η| <2.4. Seven layers of TGC arranged in one triplet and two doublets complement the
middle layer of the MDT in the endcap region, while only two layers complement
the inner layer. A chamber is formed by a detector layer containing a wire plane
and two cathodes, and it is filled with CO2 /n-pentane gases in the proportions of
55/45 %. The wire and the cathode are separated at a distance of 1.4 mm, while
the distance between wires is 1.8 mm. The wires have a diameter of 50 µm andare kept at 2900 V, while the cathode planes consist of 1.6 mm thick FR4 plates,
grafite coated on the inside and copper cladding on the other side. The wires areplaced parallel to the MDT tubes, while the cathode strips are arranged radially.
The spatial resolution of the TGC is 2 to 6 mm in the radial direction, and 3 to 7
mm in the φ direction, and the timing resolution is ∼ 4 ns.

3.2 LHC  ATLAS D

43

Figure 3.21: Schematic view of the muon trigger system showing the positions of
the RPC layers in the barrel and the TGC layers in the endcap region.

3.2.8 Luminosity detector
Figure 3.22 shows a schematic view of the ATLAS luminosity detectors. There
are two primary detectors to measure the luminosity [63]: the luminosity measurement using Cerenkov integrating detector (LUCID) and the beam conditions
monitor (BCM). Both LUCID and BCM can measure the bunch-by-bunch luminosity.

The LUCID detector
The LUCID detector is a Cerenkov detector, located at a distance of |z| = 17 m fromthe IP and at a radial distance of 10 cm from the beam line, covering the range
5.61 < |η| < 5.93. The LUCID detector measures the integrated luminosity andprovides online monitoring of the instantaneous luminosity and beam conditions
by detecting inelastic pp scattering. In order to determine the luminosity, thenumber of interactions per beam-crossing has to be well known, therefore, the
LUCID is designed so that the number of interactions in a bunch-crossing can beproportional to the number of particles detected.
The LUCID detector is composed of twenty aluminum tubes filled with C4 F10gas. The tubes are 1.5 m long and 15 mm in diameter. They surround the beampipe and point toward the IP. When a particle traversing the tube emits Cerenkov
light, it is reflected in the tube walls and measured by the PMTs. The charge fromthe PMTs is used to measure the number of particle per tube, while the timing
response provides a measurement of each bunch crossing.

44

3.2 LHC  ATLAS D

The BCM detector
The BCM comprises one detector station on either side of the ATLAS detector
at |η| = 4.2. The stations are located 1.84 m away from the IP and are made offour modules, which are arranged in a cross pattern around the beam axis ata radius of about 55 mm. The BCM is designed for bunch-by-bunch luminosity
measurements and monitoring the beam background conditions within the ID and
to protect the ATLAS detector from damage due to severe beam losses. The BCM
can distinguish particles created in pp collisions from beam-background particles
based on their detection time. The BCM modules are made of diamond sensors
that are placed between two electrodes. The sensor material was chosen due to its
radiation hardness and its fast signal rise time. A charged particle traversing thesensor ionizes the diamond and generates a signal that is amplified on the moduleand then transmitted for digitization to the readout electronics.

Figure 3.22: Schematic view of the ATLAS luminosity detectors.

3.2.9 Trigger system
The ATLAS experiment is designed to receive data at a rate of ∼ 40 MHz, but thedata acquisition system can only commit data to a permanent storage at a rate ofa few hundreds Hz. In order to select interesting data from a large number ofincoming events, the ATLAS trigger system is divided into three levels: level−1(L1), level−2 (L2) and event filter (EF). The last two are also called as high-level
triggers (HLT). The L1 trigger is implemented in the detector and works with the3.2 LHC  ATLAS D

45

hardware, and the HLT use computers and network hardwares. Figure 3.23 shows
the three-level trigger and data acquisition systems of the ATLAS experiment.
The L1 trigger is required to make a decision within a fixed time of less than
2.5 µs and to lessen the trigger rate to ∼ 75 kHz. The L2 trigger uses the L1
information as a seed. The L2 trigger is required to reduce the trigger rate to ∼ 3.5
kHz within ∼ 40 ms. Finally, the information is passed to the EF trigger. The EF
trigger uses offline analysis procedures to further select events down to a rate thatcan be recorded for subsequent offline analysis. It reduces the event rate to ∼ 200
Hz with an average event processing time of ∼ 4 s.
The HLT algorithms use the full granularity and precision of the data from thecalorimeter, the MS and the ID to refine the trigger selection. The track informationby the ID significantly improves the particle identification. The event selection at
both L1 and L2 primarily uses inclusive criteria such as high−pT objects above
defined thresholds.

Figure 3.23: Three-level trigger and data acquisition systems of the ATLAS experiment.

Level−1 trigger
The L1 trigger [64] mainly searches for isolated high−pT electrons, photons, muons,and τ−leptons decaying into hadrons to separate the desired rare processes fromthe dominant QCD multi-jet events. It also selects events with high−pT jets, large
missing transverse momentum.

46

3.2 LHC  ATLAS D

As summarized in Fig. 3.24, the L1 trigger performs the initial event selection
based on the information from the calorimeters and muon detectors. The level-1
muon trigger (L1Muon) is based on signals in the muon trigger chambers: RPC
in the barrel and TGC in the end-caps. The trigger searches for patterns of hits
consistent with high−pT muons originating from the IP. The level-1 calorimeter
trigger (L1Calo) uses the information of energy deposition in the calorimeter to
identify various types of high−ET objects. The L1Calo is a pipelined digital systemdesigned to work with about 7000 analogue trigger towers of reduced granularities(0.1 × 0.1 in ∆η × ∆φ in most regions, and larger at higher η regions). Resultsfrom both systems are processed by the central trigger processor (CTP). The CTP
implements a trigger menu based on logical combinations of results from L1Caloand L1Muon. When an event is passed in the L1 trigger, the information for thegeographical coordinate in η and φ of trigger objects (Region of Interest, RoI) aresent to the L2 trigger.

Figure 3.24: Block diagram of the L1 Trigger.

Leve−2 trigger
The L2 trigger [65] is a software-based system running on ∼ 500 computing nodes,
and can refine the event selection using the full granularity of the ATLAS detector.
This refinement can be done for a restricted amount of data due to the limited
processing speed and bandwidth. Therefore, the L2 trigger uses the L1 RoI as aseed. The L2 trigger can treat the full granularity of data from the calorimeter andmuon systems around the RoI. In addition, the L2 trigger can include data from

3.2 LHC  ATLAS D

47

the ID in its trigger decision. The L2 trigger can reduce the event rate from ∼ 75
kHz to ∼ 3.5 kHz by requiring isolation from surrounding environments to thetrigger objects and tighter requirements on the pT thresholds.
Event filter
The EF trigger [65] is an entirely software-based system running on ∼ 1800 computing nodes, reducing the event rate from ∼ 3.5 kHz to ∼ 200 Hz. It uses the thefull granularity of the data from all sub-detectors, and it can reconstruct physics
objects and analyze events with algorithms similar to those used in an offline
analysis.

48

3.2 LHC  ATLAS D

Chapter 4

Data and Monte Carlo Simulation
4.1 Data sample√
The analysis presented is based on the pp collision data at s = 8 TeV collectedwith the ATLAS detector at the LHC between March and December 2012.

4.1.1 Trigger
The data in the signal search sample were collected by a dedicated topologicaltrigger named EF j80 a4tchad xe70 tclcw dphi2j45xe10. The data collected byunprescaled single electron or muon triggers are also used for the study of background events. Table 4.1 shows the summary of triggers used in this analysis. The
values in the name of each trigger stands for the cut-threshold.
Luminosity
20.3 ± 0.6 fb−1

Signal search sample
EF j80 a4tchad
xe70 tclcw dphi2j45xe10

ElectronEF e24vhi medium1
orEF e60 medium1

Muon
EF mu24i tightorEF mu36 tight

Table 4.1: Summary of triggers used in this analysis
The trigger for the signal search sample requires:• At least one jet with pT > 80 GeV.
• Emiss> 70 GeV.
T
jet−Emissjet−Emiss• ∆φmin T > 1, where ∆φmin T indicates the smallest azimuthal separation
between the missing transverse momentum and either of the two highest-pT
jets with pT > 45 GeV.
The application of these requirements significantly suppresses the SM backgroundprocesses, especially QCD multi-jet events, without loosing the signal acceptance49

50

4.2 D  M C S

for the direct chargino production with a jet from ISR. Details on the signal trigger
are described in Sec 8.1.1.

4.1.2 Luminosity and pile-up

Total Integrated Luminosity fb-1

The peak instantaneous luminosity reached 7.73 × 1033 cm−2 s−1 with a mean number of interactions per bunch crossing of 20.7 in the 8 TeV run. The cumulative
luminosity recorded by the ATLAS detector and delivered by the LHC is shownin Fig. 4.1. The raw recorded data corresponds to an integrated luminosity of 21.3
fb−1 , falling to 20.3 fb−1 after application of basic data quality requirements. The
6% loss in term of integrated luminosity is a consequence of requiring all subdetectors systems to be in good quality. For the 8 TeV data, the uncertainty on the
integrated luminosity is ± 2.8 % based on the calibration procedure described in
Ref [66, 67].
25
ATLAS Preliminary

20

LHC Delivered
ATLAS Recorded

15

Total Delivered: 22.8 fb -1
Total Recorded: 21.3 fb -1

s = 8 TeV

10
5
0
1/4

1/6

1/8

1/10
1/12
Day in 2012

Figure 4.1: Cumulative luminosity versus time delivered by LHC (green),√ andrecorded by ATLAS (yellow) during stable beams and for pp collisions at s = 8
TeV.
When the luminosity gets increased, the detector performances and physics
analyses are affected by the following pile-up effects:• In-time pileup : Additional pp interactions occurring in the same bunch
crossing, which contributes to distributing energy uniformly throughout thedetector.
• Outof-time pileup : Additional pp interactions from other bunch crossings.
Figure 4.2 shows the luminosity-weighted distribution of the mean number ofinteractions per crossing for the 2011 and 2012 data. During 2011, the numberof pp collisions per bunch crossing increased from 5 to 15, and during 2012 thenumber increased from 10 to almost 35.

Recorded Luminosity [pb-1/0.1]

4.2 D  M C S

180

51

ATLAS Online Luminosity

∫
∫

160

s = 8 TeV, Ldt = 21.7 fb-1, <µ> = 20.7

140

s = 7 TeV, Ldt = 5.2 fb-1, <µ> = 9.1

120
100
80
60
40
20
0
0

5

10

15

20

25

30

35

40

45

Mean Number of Interactions per Crossing

Figure 4.2: Luminosity-weighted distribution of the mean number of interactions
per crossing for the 2011 and 2012 data.

4.2 Simulated sample
In order to develop and validate the analysis, Monte Carlo (MC) simulation
samples have been used. The simulated samples were generated either with
CTEQ6L1 [68], MSTW2008LO [69] or CT10 [70] parton distribution functions (PDF).
The generation of the simulated samples includes the effect of multiple pp interactions per bunch crossing, and also the effect of the detector response to collisions
from bunch crossings before and after the one containing the hard interaction.
Minimum bias events are generated with PYTHIA8 [71] configured with the AM2tune [72] and the leading-order PDF set (MSTW2008LO). Events are reweighted sothat the distribution of the number of pp collisions occurring in addition to the hard
scatter process matches that observed in data. The detector response is modeledusing the GEANT4 [73, 74] simulation These samples are then reconstructed withthe same algorithms used as for data. The simulated response is corrected for thesmall differences in efficiencies, momentum scales, and momentum resolutions
between data and simulation, using scale factors and smearing techniques.

4.2.1 Signal Monte Carlo sample
Several benchmark signal points are prepared and used for this dissertation. The
minimal AMSB model is characterized by four parameters: m3/2 , m0 , tan β, andµH . A large value of 1 TeV is used for m0 in order to prevent the appearance ofa tachyonic slepton. The SUSY mass spectrum, the branching ratios and decay
widths are calculated using the Isasusy Ver.7.80 [75]. The signal MC samples areproduced using HERWIG++ [76] with CTEQ6L1 PDFs.
The mean lifetime of the chargino τχ̃± is set to a given value; charginos are
1
0
forced to decay into χ̃1 + π± in the Geant4 simulation by following the exponential

52

4.2 D  M C S

decay with τχ̃± in the rest frame. Two samples with τχ̃± = 0.3 and 1.0 ns are
1
1
generated for each signal point. Signal MC samples with different mean lifetimes
are also obtained by applying event weights so that the distribution of the proper
lifetime follows that for a given value of the mean lifetime. The event weight w is
given by
nχ̃±

 
1
Y

  11
τ0w(τχ̃± ) =exp −ti −  ,
(4.1)
1
τχ̃±
τχ̃± τ0i11where nχ̃± , τ0 and ti are the number of charginos in the event, the chargino mean
1lifetime set in the simulation and the proper lifetime of i–th chargino, respectively.
The signal samples with τχ̃± < (≥)0.6 ns are obtained by reweighting the sample
1of τχ̃± = 0.3(1.0) ns.
1
All the signal parameters and corresponding chargino masses mχ̃± and cross1
sections are summarized in Table 4.2 and Fig. 4.3, respectively. Chargino–pair andchargino–neutralino associated production processes are identified using a jet withlarge transverse momentum from ISR. Corresponding diagrams are illustrated inFig. 4.4. Signal samples are generated by requiring at least one truth–level jetwith pT > 70 GeV and |η| < 5. The selection efficiency after the application of thisrequirement is also shown in Table 4.2.
Signal cross-sections are calculated to next-to-leading order (NLO) in the strong
coupling constant using Prospino2 [77]. An envelope of cross-section predictions
is defined using the 68% confidence level (CL) ranges of the CTEQ6.6 [78] (including
the αS uncertainty) and MSTW2008LO PDF sets, together with variations of thefactorization and renormalization scales by factors of two or one half. The nominal
cross-section value is taken to be the midpoint of the envelope and the uncertainty
assigned is half the full width of the envelope, closely following the PDF4LHC
recommendations [79].

4.2.2 Background Monte Carlo sample
For the development of the analysis, MC samples of the SM background processes
are also used. Table 4.3 and Fig. 4.5 shows the SM background processes with theircross-sections and their diagrams, respectively. The MC samples are generated asfollows:
Z/W+jets production
A set of Z/W+jets MC samples has been produced with ALPGEN [80]. The HERWIG [81]is used for the parton shower (PS), fragmentation and hadronization, and JIMMY [82]is used to simulate the underlying events. The ALPGEN samplesq
use the CTEQ6L1
P
PDF set and the factorization and normalization scales are set to M2W/Z + |pT |2 ,P
where |pT |2 is the scalar sum of the pT of the outgoing partons. W (→ lν̄)+jets and
Z (→ νν̄, l+ l− )+jets samples are generated with up to six outgoing partons with LO

4.2 D  M C S

m3/2 [ TeV]
29.0
35.0
43.5
52.0
69.5
78.0
86.5
95.0
104.0
122.0
139.0
157.0
175.0

mχ̃± [ GeV]
1
80.5
98.9
124.5
149.8
201.0
225.6
250.0
274.4
300.0
351.1
399.1
449.5
499.6

53

Cross-section [pb]
38.3
16.9
6.91
3.37
1.04
0.654
0.423
0.284
0.192
0.094
0.051
0.028
0.016

Filter efficiency
0.052
0.069
0.091
0.108
0.136
0.147
0.155
0.163
0.169
0.183
0.195
0.205
0.216

Table 4.2: Summary of AMSB signal parameters, chargino masses, and their NLO
cross-sections with m0 = 1000 GeV, tan β = 5 and µH > 0. The filter efficiencies foreach sample are also shown.

AMSB: tanβ=5, µ >0

Cross section [pb]Prospino22+ 0
χpp → ∼
χ∼

10

1 1

10

+ χ
pp → ∼
χ∼

1 1

- 0
χ
pp → ∼
χ∼
1 1

Total

1

10-1
10-2

s = 8 TeV
10-3

40

60

80

100 120 140 160 180 200m3/2 [TeV]

100 150 200 250 300 350 400 450 500 550mχ∼± [GeV]1

Figure 4.3: Cross-section of electroweak chargino production atfunction of m3/2 .

√s = 8 TeV as a

54

4.2 D  M C S

± 0(a) pp → χ̃1 χ̃1 j+ −(b) pp → χ̃1 χ̃1 j
± 0

+ −

Figure 4.4: Diagrams of χ̃1 χ̃1 (a) and χ̃1 χ̃1 (b) production processes.

Production process

σ× Branching Ratio

Generator

QCD multi-jet events
W(→ lν)+jets
Z(→ νν̄)+jets
Z/γ∗ (→ l+ l− )+jets
tt̄
single top

73 mb
37 nb
6.7 nb (Mνν̄ > 40 GeV)
3.7 nb (Ml+ l− > 40 GeV)
240 pb116 pbPYTHIA
ALPGENALPGENALPGENPOWHEG
AcerMC and MC@NLO

Table 4.3: The SM background processes with their cross-section times branchingratio. Generators used for each process are also shown.

4.2 D  M C S

55(a) Z → νν̄ + j(b) Z → l+ l− + j(c) W → lν̄ + j(d) tt̄(e) single top quark(f) QCD multi-jet events

Figure 4.5: Diagrams of the SM background processes.

56

4.2 D  M C S

matrix elements. The MLM [83] matching scheme is adopted to remove overlap
between the the n and n + 1 parton samples from the matrix element (ME) and thePS.
Top quark production
Top quark pair (tt̄) production process is simulated with POWHEG [84] interfaced to
PYTHIA for the fragmentation and hadronization processes. The top quark massis fixed at 172.5 GeV, and the CT10 PDF set is used. Single top quark production
process for the s-channel and Wt processes is simulated with MC@NLO [85] interfaced
to Herwig and Jimmy, while the t-channel process is simulated with AcerMC [86]interfaced to PYTHIA and using the CTEQ6L1 PDF set.
QCD multi-jet events production
The events are generated with PYTHIA6 [87]. The samples are generated separately
for different slices of the hard process momentum to ensure enough statistics inthe large jet pT region.
The background predictions are normalized to theoretical cross-sections, calculated including higher-order QCD corrections where available. The inclusive cross-sections for Z/W +jets processes are calculated with DYNNLO [88] with
the MSTW2008LO PDF set. Approximate next-to-next-to-leading-logarithm crosssections are used in the normalization of the tt̄ and single top quark samples.

Chapter 5

Object Reconstruction
The analysis presented in this dissertation relies on the identification and reconstruction of tracks, jets, electrons, muons, and missing transverse momentum
produced in the pp collisions and collected by the ATLAS detector. In this chapter,
general reconstruction criteria and algorithms used in the ATLAS experiment arepresented.

5.1 Track reconstruction
A large number of charged particles are produced in pp collisions at the LHC.
When particles go through the detector, a lot of hits are generated. The aim of
track reconstruction is to distinguish the hits originating from the different charged
particles and determine trajectories that best matches the measurements.
Two algorithms are employed in the ATLAS experiment as the standard trackreconstruction [89]: “inside-out” and “onside-in” reconstruction. The first algorithm is optimized to find primary tracks coming from the interaction point,
while the second one is optimized for the reconstruction of electrons from photon
conversions in the ID volume.
In addition to the standard track reconstruction, an extra extended-track reconstruction called “Re-tracking” has been adopted in this analysis in order to increase
the acceptance of short-length tracks, which especially enhances the sensitivity for
decaying charginos that have short proper lifetimes.

5.1.1 Track parametrization
A track is parametrized at the point of the closest approach with the global z-axis
using five perigee parameters as summarized in Table 5.1 and Fig. 5.1. The sign of
d0 is defined as positive, if φ − φ0 = π2 + 2nπ, where n is an integer.
57

58

5.1 O R

d0: Transverse impact parameterqd0 = ± x2P + y2P

z0
φ0
θ

: Longitudinal impact parameter: Azimuthal angle at perigee: Polar angle at perigee

z0 = zPpy
φ0 = arctan( px )p
θ = arccot( q xqp: Charge of the particle divided by momentumqQ
p = |p|p2x +p2y)

Table 5.1: Track parameters at perigee

p

track

R

pT

y

θ
φ0

d0

track

d0

φ
x

z0

z

Figure 5.1: Illustration of the perigee parameters of a track in the transverse plane(left) and r-z plane (right).

5.1 O R

59

5.1.2 Inside-out track reconstruction
The inside-out track reconstruction starts by finding track candidates in the silicondetectors and then extends the trajectories of successfully fitted tracks to the TRT
detector to form full inner detector tracks.
1. Space-Point Formation : The first step in the inside-out track reconstructionis the creation of three-dimensional space-points of the silicon detector measurements. The pixel modules provide two-dimensional information, therefore the space-points are created in a simple way using the three-dimensional
information from the surface representing the detector element. Single SCT
clusters can not be transformed directly into space-points, since the precise measurement on an SCT module can only be given orthogonally to thesilicon strip direction. However, two silicon microstrip detectors are constructed with a sandwich module structure that is rotated by a stereo angle
with each other. Therefore, the three-dimensional space-point is formed byusing two strip measurements together with a beam spot constraint. In contrast to the pixel detector, the SCT space-point formation leads to an intrinsic
noise suppression at the very first pattern stage because it requires measurements from two different modules with separate readout for the creation of
one single space-point object.
2. Track Finding : Space-points formed by the silicon detectors are collected
to seed track candidates. This procedure is divided into two different tasks;
one is the track seed finding and the other is the track candidate creation
based on the seeds found in the first task.
• Track seed finding : Space-point pairs from the pixel detector are foundin the first step, and z vertices are built from these pairs. The primaryvertex is used to further constrain the seeds with three or more spacepoints. Seeds are selected if they are in the tolerance region for predictedvertices.
• Track candidate creation : The seeds provide directional information
enough to construct roads of detector elements, and the track candidates
are associated with extra hits. A Kalman fitter smoother formalism [90]is employed to follow trajectories and include successive hits simultaneously. The Kalman filter progressively updates the track informationand predicts precisely the track representation on the next detector measurement.
3. Ambiguity solving : The seeded-track finding results in a large number oftrack candidates. Many of these track candidates share hits and are not originated from real particle trajectories. Therefore, tracks are ranked in theirlikelihood to describe the real trajectories of particles. The ambiguity solving
process starts by refitting tracks using the refined reconstruction geometry

60

5.1 O R
that has a detailed material description. However, this track fit only gives
a reduced χ2 and it is not appropriate to decide whether tracks are goodor fake tracks that do not originate from real particle trajectory. The track
scoring strategy is therefore adopted for the classification of tracks. For theclassification of tracks, a different characteristics of a track is represented bya beneficial or penalty track score, which all together form an overall track
score. The measurements of different sub-detectors are weighted with different scores by downgrading measurements from less precise detector parts.
In general, each hit associated with a track leads to a higher score. On theother hand, holes, defined as absent hits on the passage of the track through a
detector element, lower the track score. Track candidates remaining after theambiguity solving process and having not more than six hits are neglected
for further processing.
4. TRT track extension : Tracks reconstructed by the silicon detectors are extended to the TRT detector by associating extra hits in the TRT detector.
Tracks are fitted again with the additional measurements in the TRT detector and compared with the original silicon-only tracks by the track scoringmechanism. In the case that the track score of a silicon track is higher thanthat of the extended track, the silicon track is kept and the extended track isneglected.

5.1.3 Outside-in track reconstruction
The inside-out track reconstruction sequence is complemented with an outside-in
strategy. The outside-in strategy starts from unassigned TRT segments and looks
for matching hits in the silicon detectors. This type of track reconstruction ismainly aimed at detecting late decays of neutral particles and photon conversions
to e± pairs.
The outside-in reconstruction starts with a TRT segment finding algorithm.
This algorithm consists of two-step procedures: a global pattern recognition and asubsequent local pattern recognition. The TRT measurements do not provide theinformation about the coordinate along to the straw direction, therefore the spacepoints can not be formed. A global pattern recognition using the Hough transform
technique [91] is thus performed in the projective plane: r-φ and r-z planes are
adopted for the TRT in the barrel region and the end-cap region, respectively.
The TRT track segments obtained by the global pattern recognition are further
processed in a local pattern recognition algorithm. This algorithm uses the information of drift time, while the global pattern recognition employs only the straw
center as the z-position. Eventually, the track segments are obtained via a Kalmanfilter-smoothing formalism. The track segments are extended inward in this algorithm. If the extended tracks have no associated hits in the silicon detector, tracks
are categorized as the TRT-standalone tracks.

5.2 O R

61

5.1.4 Re-tracking
The inside-out sequence is of particular interest for finding long-lived chargino
trajectories, although it is optimized for the reconstruction of stable particles thatleave long tracks in the ID, and in particular, only reconstructs tracks with aminimum of seven space points (Nclus= 7). An extended track reconstruction thatminprovides pixel-seeded reconstructed tracks is developed, especially for this search,
to make use of tracks having short length. In order to increase the acceptanceof the track reconstruction and chargino track reconstruction efficiency at low
radius, a third sequence (Re-tracking) is applied by following the standard trackreconstruction algorithm. This sequence proceeds using leftover pixel and SCT
hits from the two previous tracking sequences and reconstructs tracks with apixelminimum of three pixel hits (Nmin = 3), while no SCT or TRT hits are required.
The outward extension then follows; SCT and TRT hits are attached if they lie
along the track trajectory. Figure 5.2 and Table 5.2 show a flowchart of the Retracking and details of the input parameters of the reconstruction algorithm for theRe-tracking, respectively. The input parameters of the reconstruction algorithm
for the standard tracking are also shown in Table 5.2. The number of holes (Nhole
max )
share
and shared hits (Nmax ) are allowed up to three and one in the standard trackreconstruction, respectively, while they are both required to be zero in the Retracking in order to suppress fake tracks that are originating from combinations ofwrong space-points in the procedure of track-seed finding or outward-extensionof trajectories. The Re-tracking extends the sensitive decay volume inwards from
Rdecay > 370 mm to Rdecay > 150 mm, where Rdecay is the chargino transverse decay
radius, as shown in Fig. 5.3 and 5.4. The performances of the Re-tracking are given
in Chapter 6.
Parameter[Track Finding]usePixel
useSCT
Nclus
min
Nhole
max
[Ambiguity Solving]
pT,min
Nclus
min
pixel
Nmin
Nshare
max
Nhole
max
pixelhole
NminStandard

Re-tracking

True
True
7
3

True
False
3
0

0.1 GeV
7
0
1
3
2

1 GeV
3
3
0
0
0

Table 5.2: Configuration for the track reconstruction algorithm.

62

5.2 O R

Reconstruction Efficiency

Figure 5.2: Flowchart of the Re-tracking.

1.4
1.2
1
0.8
0.6
0.4
0.2
0

New tracking (|η| < 2.0)

ATLAS standard tracking (|η| < 2.0)

Exponential Decay (τ = 0.2 ns)

100 200 300 400 500 600 700 800 900 1000
Decay Radius [mm]Figure 5.3: Tracking efficiency for decaying charginos as a function of Rdecay .

63

1
TRT

1000

0.9
0.8

800

Efficiency

Radius (mm)5.2 O R

0.7
0.6

600

SCT

0.5
0.4

400

0.3
200

0.2

Pixel

0.1
0

-2

-1

0

1

2

0
η

1
TRT

1000

0.9
0.8

800

Efficiency

Radius (mm)(a) ATLAS standard track reconstruction

0.7
0.6

600

SCT

0.5
0.4

400

0.3
200

0.2

Pixel

0.1
0

-2

-1

0

1

2

0
η

(b) Extended track reconstruction (= standard track reconstruction + Retracking)

Figure 5.4: Two-dimensional Rdecay –η tracking efficiency map for decaying
charginos with the standard (a) and extended (b) track reconstruction algorithms.

64

5.3 O R

5.2 Vetex reconstruction
Primary vertices are reconstructed using an iterative vertex finding algorithm [92,93]. Vertex seeds are obtained from the z-position of the reconstructed tracks at thebeamline. An iterative χ2 fit is performed using a seed and nearby tracks. Eachtrack carries a weight that is a measure of its compatibility with the fitted vertex
depending on the χ2 of the fit. Tracks displaced by more than 7 σ from the vertex
are used to seed a new vertex and the procedure is repeated until no additionalvertices can be found. The beam spot position is used as a three-dimensional
constraint in the fit. Resulting reconstructed vertices are required to have at least
two associated tracks.

5.3 Jet5.3.1 Reconstruction
The aim of reconstruction of jets is to estimate the momentum of the coloredpartons that undergo fragmentation and hadronization processes before reaching
the detector. The reconstruction of jets are performed by the following three steps:
1. Clustering signals from the calorimeter cells,
2. Combination of spatially related calorimeter cells into a jet by jet finding
algorithm,
3. Calibration of reconstructed jets to the particle level.

5.3.2 Clustering of calorimeter cells
A cluster is seeded by a cell having a signal to noise ratio (≡ Ecell /σnoise ) above athreshold of Ecell /σnoise > 4. The noise σnoise is measured for each cell independently and is defined as the expected RMS of the electronic noise in the cell. The
contribution from pile-up is also added in quadrature. Cells neighboring to theseed whose Ecell /σnoise is greater than two are added to the cluster. Finally, all cells
surrounding the cluster are added to the cluster without any energy threshold, andthen a three-dimensional topological cluster (topo-cluster) is formed. The positionof the cluster is calculated by the energy weighted cell positions.

5.3.3 Jet finding algorithm
Jets are reconstructed by anti-kT algorithm [94] with the distance parameter of
0.4 using topo-clusters as an input. The anti-kT algorithm is an iterative sorting
algorithm applied to topo-clusters, aiming to associate nearby topo-clusters into a
jet using a distance defined below.

5.3 O R

65

di = p−2
T,idi j =−2
min(p−2
T,i , pT, j )≡−2min(p−2
T,i , pT, j )(5.1)(ηi − η j )2 + (φi − φ j )2
R2
∆2i j
R2

(5.2)
(5.3)

where pT,i is the transverse momentum of each constituents, and R is the distanceparameter controlling the size of the jet, which is set to be 0.4 in the anti-kT
algorithm. The algorithm starts by calculating the minimum value (dmin ) among
all di and di j . If the smallest distance is found to be di j between two topo-clusters,then those two topo-clusters are combined to form a larger topo-cluster: their fourmomenta are summed, the distances are recalculated, and the iterative processcontinues. If the smallest distance is found to be a di , then that topo-cluster isdefined as a jet and removed from the collection of topo-clusters. This algorithm
continues until no more topo-clusters remain.

5.3.4 Calibration
Jets are reconstructed from the calorimeter clusters at the electromagnetic (EM)energy scale. The EM energy scale provides an energy calibration for electromagnetically interacting particles and was determined using test-beam measurements
for electrons [95]. The measurement of jet transverse momentum at the EM scale
jet,EM(pT) underestimates that of hadron-level jets due to the nature of the noncompensating calorimeters and the dead material. Thus, an average correction isjet,EMapplied to obtain the correct transverse momentum depending on η and pT.
For this purpose, a scheme to calibrate energy depositions from hadrons based on
calorimeter signals and the observed shower shape (local cluster weighting; LCW)
is used. LCW calibration starts by classifying topo-clusters as electromagnetic or
hadronic based on their shower shapes. The energy of the hadronic topo-clusters
is then corrected for the lower response of the calorimeters to hadronic deposits
by applying multiplicative calibration weights to their constituent cells. These
weights are derived from Geant4 simulations of charged single pions.

5.3.5 Jet energy scale uncertainty
The jet energy scale (JES) uncertainty [96] comes mainly from the uncertainty on
the single particle response in the calorimeter. Other leading contributions comefrom the limitation on the knowledge for the detector such as the amount of deadmaterial and the uncertainty on the effects of pile-up. Figure 5.5 shows the overall
jet energy scale uncertainties as a function of pT,jets and ηjets5.4 O R

0.1
0.09
0.08

anti-kt R = 0.4, LCW+JES + in situ correction

Fractional JES uncertainty

Fractional JES uncertainty

66

ATLAS Preliminary

Data 2012, s = 8 TeV
η = 0.0

Total uncertainty
Absolute in situ JES
Relative in situ JES
Flav. composition, inclusive jets
Flav. response, inclusive jets
Pileup, average 2012 conditions

0.07
0.06
0.05
0.04

0.1
0.09
0.08

ATLAS Preliminary

Data 2012, s = 8 TeV
jetp T = 40 GeV

Total uncertainty
Absolute in situ JES
Relative in situ JES
Flav. composition, inclusive jets
Flav. response, inclusive jets
Pileup, average 2012 conditions

0.07
0.06
0.05
0.04

0.03

0.03

0.02

0.02

0.01

anti-kt R = 0.4, LCW+JES + in situ correction

0.01

0
20

30 40

102

2×102

2×10
p jet
[GeV]
T

3

10

3

0

-4

-3

-2

-1

0

1

2

3

4
η

(a) Jet energy scale uncertainty as a function of (b) Jet energy scale uncertainty as a function ofpT,jets at |ηjets | = 0.0
pseudorapidity at pT,jets = 40 GeV

Figure 5.5: Fractional jet energy scale systematic uncertainty components. The
total uncertainty, which is calculated by summing all components in quadrature,is shown as a filled blue region topped by a solid black line.
These systematic uncertainties are assigned using in-situ techniques that exploit the transverse momentum balance between a jet and a well measured reference object: a direct transverse momentum balance between a jet and a photon or
Z boson decaying to electron or muon pairs, and a balance between a high-pT jet
recoiling against one or more lower-pT jets. Figure 5.6 shows the jet response ratioof the data to the MC simulation as a function of pT for three in-situ techniques.

5.4 Electron
5.4.1 Reconstruction
Electron reconstruction starts with the formation of seed clusters in the middle
sampling of the electromagnetic calorimeter, and then they are required to havecorresponding reconstructed tracks.
1. Clustering : A sliding-window clustering algorithm [97] is used to reconstruct electron clusters. The sliding-window algorithm scans a fixed-size
window over the η − φ grid of calorimeter cells, searching for a local maxima
of energy contained in the window. The reconstruction starts by clustering
seeds with a window size 3 × 0.025 units in η and 5 × 0.025 units in φ. The
seed clusters are required to have transverse energies above a threshold of
2.5 GeV.
2. Track-Cluster matching : Once the seed clusters are formed, they are required to have corresponding reconstructed tracks that are extrapolated fromthe last measurement point in the ID to the middle sampling of the electromagnetic calorimeter. The η-distance between the track impact point and theResponse

Data/ ResponseMC

5.4 O R
1.1
1.08

67
ATLAS Preliminarys = 8 TeV, |η | < 0.8

anti-kt R = 0.4, LCW+JES
Data 2012

1.06
1.04
1.02
1
0.98
0.96
0.94
0.92

γ +jetZ +jetMultijet

0.9
20 30 40

Total in situ uncertainty
Statistical component

102

2×102

3

10
p jet [GeV]T

Figure 5.6: Jet response ratio of the data to the MC simulation as a function ofpT for three in situ techniques combineded to determine the in-situ energy scalecorrection: Z+jet (squares), gamma+jet (full triangles), and QCD multi-jet (empty
triangles) events. The error bars indicate the statistical and the total uncertainties.
The black line shows the combination of all in-situ techniques, and the outerband indicates the total uncertainty resulting from the combination of all in-situ
techniques, while the inner band shows the fraction from statistical uncertainties
only.
cluster position is required to fulfill ∆η < 0.05. To account for energy losses
by bremsstrahlung, the size of φ-window is set to 0.1 on the side where theextrapolated track bends as it traverses the solenoidal magnetic field, and0.05 on the other side. An electron is reconstructed if at least one track ismatched to the seed cluster. If multiple tracks are matched to the cluster,
tracks with larger silicon hits are preferred, and the track with the closest
in the η-φ plane is chosen. In 8 TeV data, in order to recover the track reconstruction efficiency for electrons that undergo significant bremsstrahlungin the detector, a Gaussian Sum Filter (GSF) algorithm [98] that re-fit tracks
associated to the seed cluster is developed. This algorithm consists of a modified pattern recognition that allows for energy loss at each material surface,
and improves the reconstruction efficiency by 7-8 % as shown in Fig. 5.7
After the track-cluster matching, the electron clusters are rebuilt using 3 × 7(5 × 5) longitudinal towers of cells in the barrel (endcap) region. The electronenergy is then determined by adding four different components: (a) the energy
deposit measured in the cluster, (b) the energy estimated to have been lost in thematerial in front of the electromagnetic calorimeter, (c) the energy estimated tohave leaked laterally outside of the cluster, and (d) the energy estimated to haveleaked longitudinally behind the cluster. These components are parametrized
as a function of the energies measured in the different longitudinal layers of the5.5 O R

1.05
ATLAS
Preliminary

Reconstruction Efficiency

Reconstruction Efficiency

68

15<ET<50 GeV

1
0.95
0.9

Reconstruction and track quality efficiency
2011 data s=7 TeV
2011 MC

0.85

2012 data s=8 TeV∫ L dt = 4.7 fb-1∫ L dt = 20.3 fb

1.05
1
0.95
0.9
Reconstruction and track quality efficiency

0.85

2011 data s=7 TeV
2011 MC-10.82012 data s=8 TeV

2012 MC

0.8

-2

-1.5

-1

-0.5

 η<2.47

ATLAS
Preliminary∫ L dt = 4.7 fb-1∫ L dt = 20.7 fb-1

2012 MC

0

0.5

1

1.5

2
Cluster η

0.75

20

30

40

50

60

70

80

Cluster ET [GeV](a) Electron reconstruction efficiency as a func- (b) Electron Reconstruction efficiency as a function of η for electrons with 15 < ET < 50 GeV.
tion of ET for electrons with |η| < 2.47.

Figure 5.7: Electron Reconstruction efficiency for data (filled markers) and MC(open markers) for the 2011 (triangles) and the 2012 (circles) data samples.
electromagnetic calorimeter based on MC.

5.4.2 Identification criteria
The purity of real electrons is small at the stage described above. Reconstructed
electrons suffer large backgrounds originating from (a) mis-identified hadrons,(b) photon conversions, and (c) semi-leptonic decays of heavy flavor hadrons.
Therefore, electrons are identified by the application of selection criteria. Threereference sets of selection requirements (Loose, Medium and Tight) are defined withincreasing background rejection power as summarized in Table 5.3. Figure 5.8
shows the identification efficiency of electrons estimated with a “tag and probe”
method using Z → ee decays as a function of ET or the number of primary vertices. In this analysis, the loose (tight) identification criteria is adopted for theidentification of electrons in the signal search (background control) sample. The
dependency of pile-up on the identification efficiency is negligibly small, and theaverage efficiency for the loose (tight) criteria is about 95 (80) %.

5.5 Muon
5.5.1 Reconstruction
Muon candidates are reconstructed by the ID and the MS. Several algorithms are
adopted to reconstruct muons according to the available information from the ID
and the MS:• Stand-alone muon : The trajectory of muon is reconstructed only in the MS.
The direction and the impact parameter of the muon at the interaction point
are determined by extrapolating the track reconstructed in the MS back to5.5 O R
Type

69

Description

Variable name
Loose

Acceptance of the detector•|η| < 2.47

Hadron leakage•

Ratio of ET in the first layer of thehadronic calorimeter to ET of the EM cluster(in the range |η| < 0.8 and |η| > 1.37)
Ratio of ET in the hadronic calorimeter to ET of the EM cluster(in the range |η| > 0.8 and |η| < 1.37)

Rhad1

•

Rhad

First layerof EM calorimeter.

••

Total shower width.
Ratio of the energy difference associated withthe largest and second largest energy deposit
over the sum of these energies

wstot
EratioSecond layerof EM calorimeter••

Ratio in η of cell energies in 3 × 7 versus 7 × 7 cells.
Lateral width of the shower.

Rηwη2

Track quality••

Number of hits in the pixel detector ≥ 1.
Number of hits in the pixel and SCT detectors ≥ 7.

NpixelNSi

Track matching•∆η between the cluster and the track < 0.015(< 0.005 for Medium and Tight selection)∆η1

Medium = Loose +Third layerof EM calorimeter•

Track quality••

Ratio of ET in the third sampling to ET of the EM cluster

Number of hits in the innermost pixel layer
Ratio of the number of high-thresholdhits to the total number of hits in the TRT.
• Transverse impact parameter with respect tothe primary vertex < 5 mm ( < 1 mm for Tight selection)f3

Nb−layer
RTRT
d0

Tight = Medium +Track quality•

Number of hits in the TRT.

NTRT

Track matching••∆φ between the cluster and the track.
Ratio of the cluster energy to the track momentum.

∆φ2
E/p

Conversions•

Electron candidates matched to reconstructed
photon conversions are rejected

Table 5.3: Definition of variables used for loose, medium, and tight electron identification requirements for the central region of the detector (|η| < 2.47).

5.5 O R

1

Efficiency

Efficiency

70

0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6

1
0.95
0.9
0.85

|η| < 2.47
Loose
Loose, MC
Multilepton
2012
Multilepton, MC
ATLAS Preliminary Medium
Medium, MC-1
L dt = 20.3 fb
Tight
Tight, MCs = 8 TeV Z→ ee∫

10 20 30 40 50 60 70 80 90 100
ET [GeV]

0.8
0.75
0.7
0.65

ET > 15 GeV
Loose
LooseLLH
Multilepton
MediumTight
VeryTightLLH

2012
ATLAS Preliminary= 20.3 fb∫sL= dt8 TeV
Z→ ee
-1

0.6
5

10

15

20

25

30

number of primary vertices(a) Identification efficiency of electrons as a func- (b) Identification efficiency of electrons as a function of ET .
tion of the number of reconstructed primary vertices.

Figure 5.8: Identification efficiency of electrons estimated with a “tag and probe”
method using Z → ee decays.
the point of closest approach to the beam line. The extrapolation takes into
account both multiple scatterings and energy losses in the calorimeter.
• Combined muon : A muon candidate is reconstructed independently in the
ID and MS. A combined track is then formed from the successful combinationof a track made by the stand-alone algorithm with a track reconstructed in
the ID.
• Segment-tagged muon : A track in the ID is reconstructed as a muon if at
least one track segment in the MDT or CSC is associated with the extrapolated
track to the MS.
In order to reject fake muons originating mostly from semi-leptonic decays ofheavy flavor hadrons, tracks formed by the ID are required to pass the quality
criteria based on the number of hits in the pixel, SCT and TRT detectors:• Nb−layer ≥ 1, if crossing an active module of the innermost pixel layer.
• Npixel ≥ 1.
• NSCT ≥ 5.
• The number of associated holes in the silicon detectors < 3.
• (NTRT + NTRToutliers ) > 5 and NTRToutliers < 0.9(NTRT + NTRToutliers ) for 0.1 < |η| <1.9.

5.6 O R

71• If (NTRT + NTRToutliers ) > 5, then NTRToutliers < 0.9(NTRT + NTRToutliers ) for |η| < 0.1
or |η| > 1.9.
where NSCT and NTRToutliers denote the number of SCT hits and the number of TRT
outliers on the muon track, respectively.

5.5.2 Performance
The reconstruction efficiency of muons is measured with a “tag and probe” method
using di-muon decays of Z bosons [99]. Figure 5.9 (a) and (b) show the reconstruction efficiencies for combined muons measured with the data and MC simulation
as a function of η and the number of reconstructed primary vertices, respectively.
The average reconstruction efficiency is estimated to be about 97 % and the difference between data and MC simulation is less than 2 %. The dependency of pile-up
on the reconstruction efficiency is negligibly small and calculated to be less than 2%.

(a) Reconstruction efficiency of muons as a func- (b) Reconstruction efficiency of muons as a function of η.
tion of the number of reconstructed primary vertices.

Figure 5.9: Reconstruction efficiency of muons estimated with a “tag and probe”
method using di-muon decays of Z bosons.

5.6 Missing transverse energy
The missing transverse energy is the energy imbalance in the transverse plane.
The physical source of such an imbalance is the presence of unseen particles suchas neutrinos or LSPs escaping the detector without leaving any signal. The Emiss
is
x(y)
→
−
calculated from the negative vector sum of calibrated p of reconstructed physics
x,yobjects. The topo-clusters not associated with any such objects are also taken into
account as the Emisssoft term.
T72

5.6 O R

miss,jets

miss,µEmiss= Emiss,e+ Ex(y)+ Ex(y)
x(y)
x(y)
q
=
Emiss
(Emiss)2 + (Emiss
)2 .
x
y
T

+ Emiss,SoftTerm
.
x(y)

(5.4)
(5.5)

In Eq. 5.5, electrons reconstructed by loose criteria described in Sec. 5.4, jetswith pT > 20 GeV and |η| < 4.5, and muons reconstructed by the selection criteria
described in Sec. 5.5 are used. The soft term is calculated from topo-clusters andtracks not associated to high-pT objects. The topo-clusters are calibrated using the
LCW technique, and any overlap between tracks and topo-clusters are resolved.
The energy scale uncertainties of jets, electrons, and muons are propagated toscale uncertainty.
calculation and used to assign the Emiss
the EmissTTChapter 6

Tracking Performance
The extended track reconstruction, introduced in Sec. 5.1.4, significantly improves
the tracking efficiency for decaying charginos at low radius. This chapter summarizes the studies that validate the performances of the extended track reconstruction.

6.1 Tracking performance for decaying charginos
As described in Sec. 5.1.4, the Re-tracking requires a minimum of three pixel
hits and reconstruct momenta with smaller track stubs, which may result in a
decline in the performances especially on the pT -scale and resolution. The track
pT is a discriminant variable between signal and background tracks, therefore, its
performance is critical to this analysis.
χ̃±

χ̃±

χ̃±

Figure 6.1 shows the two-dimensional distributions of (prec−pT1 )/pT1 vs. pT1 forT±rec(χ̃ )different numbers of SCT hits NSCT , where pT 1 is the reconstructed (chargino)
track pT . In the case that a chargino decays before reaching the first SCT layer
and the corresponding track have no associated SCT hits, the reconstructed pT isbiased and has a poor resolution. Therefore, reconstructed tracks are required tohave NSCT ≥ 2. Although this requirement reduces the efficiency for charginos
having decay radii below 300 mm (the first SCT layer), it significantly suppresses
fake tracks that could have largely mismeasured pT (pT -mismeasured tracks) andcontribute to the background in this analysis.
Figure 6.2 shows the relation between the number of silicon hits associated to±reconstructed chargino tracks and the true decay radius of χ̃1 , ensuring that the
decay vertex and track length are well reconstructed.

6.2 Tracking performance in QCD multi-jet events
When charged hadrons interact with material in the silicon detectors, the standard
track reconstruction can hardly find their tracks, while the Re-tracking enhances736.2 T P

0
150
100

-0.4
-0.6

50

200

0.2
0.1

150

0

-0.1

100

-0.2
-0.3

-0.8
-1
0

250

0.3

T

T

-0.2

0.5
0.4

T

200

(Track p - True p ) / True p

0.2

T(Track p - True p ) / True p

T

250
0.4

T

74

50

-0.4
100

200

300

400
500
True p [GeV]

0

-0.5
0

100

200

300

400
500
True p [GeV]

T

χ̃±

χ̃±

χ̃±

χ̃±

T

0.2

140

0.1

120

T

160

(Track p - True p ) / True p

180

0.3

0.4

1400

0.3

1200

0.2
0

800

-0.1

600

-0.2

60

-0.3

40

-0.3

-0.4

20

-0.4

0

-0.5
0

300

400
500
True p [GeV]

T

80

T

100

200

1000

0.1

0

100

χ̃±

0.5

-0.1

-0.5
0

χ̃±

− pT1 )/pT1 vs. pT1 for NSCT = 2(b) (prec
T
200

0.4

T

(Track p - True p ) / True pT− pT1 )/pT1 vs. pT1 for NSCT = 0(a) (prec
T
0.5

-0.2

400
200
100

200

300

400
500
True p [GeV]

T

χ̃±

χ̃±

χ̃±

(c) (prec− pT1 )/pT1 vs. pT1 for NSCT = 4
T

0

T

0

T

χ̃±

χ̃±

χ̃±

(d) (prec− pT1 )/pT1 vs. pT1 for NSCT ≥ 6
T
χ̃±

χ̃±

χ̃±

Number of silicon hits (NPixel+NSCT)

Figure 6.1: Two-dimensional distribution of (prec− pT1 )/pT1 vs. pT1 with differentTrequirements on NSCT .

14
1200

12
10

1000

8

800

6

600

4

400

2

200

0

100 200 300 400 500 600 700 800 900 1000
Decay radius (mm)0Figure 6.2: Relation between the number of silicon hits associated to the recon±
structed chargino track and the decay radius of χ̃1 .

6.2 T P

75

the potential for reconstructing their primary tracks. The tracking performance,
especially for tracks reconstructed by the Re-tracking, is validated using hadron
tracks in a QCD multi-jet enriched sample, by comparing to that for MC events.
Events with isolated hadron tracks are selected as follows:
Event selection• Dataset : QCD multi-jet enriched sample collected by single-jet triggers.
• Jet quality requirements1 .
• At least one reconstructed primary vertex with a minimum of five tracks
with pT > 0.4 GeV.
• No identified electrons and muons with pT > 10 GeV.
• Leading jet pT > 90 GeV.
• Emiss< 90 GeV, which is orthogonal to the event selection in the signal searchTsample2 .
• At least one hadron track.
Selection criteria for hadron tracks• pT > 15 GeV.
• |d0 | < 1.5 mm.
• |z0 sin θ| < 1.5 mm.
• Nb−layer ≥ 1, if crossing an active module of the innermost pixel layer.
• Npixel ≥ 1.
• 2 ≤ NSCT < 4.
• ∆R between the track and the nearest jet with pT > 20 GeV should be smaller
than 0.1.
where z0 is the longitudinal impact parameters with respect to the primary vertex.
The requirement 2 ≤ NSCT < 4 enhances pixel-seeded tracks reconstructedby the Re-tracking. The variables of track hit information and track quality aresummarized in Table 6.1.
Figure 6.3–6.5 show various distributions of the reconstructed tracks for theobserved data in the QCD multi-jet enriched sample and MC predictions. The
performance of Re-tracking in the observed data reasonably agrees with that in
the MC simulation, even though the number of hits in silicon detectors are fewer
than that of ATLAS standard tracks [100].
1
2

Described in Sec. 8.2.3.
Described in Sec. 8.2.4.

6.2 T P

1

Tracks (a.u.)Tracks (a.u.)

76

DATA, QCD enriched sample

10-1
MC, QCD multi-jets

1
DATA, QCD enriched sample
MC, QCD multi-jets

10-1

10-2
10-2

10-4

10-3

2.5
2
1.5
1
0.5
0

2.5
2
1.5
1
0.5
0

0

50

100

150

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250
Track pT [GeV]DATA / MCDATA / MC

10-3

-2

-1.5

-1

1
DATA, QCD enriched sample

10-1

0

0.5

1

1.5
2
Track η(b) Track η

Tracks (a.u.)Tracks (a.u.)(a) Track pT-0.5

MC, QCD multi-jets

1
DATA, QCD enriched sample

10-1
MC, QCD multi-jets

10-2
10-3

10-3

10-4

2.5
2
1.5
1
0.5
0

2.5
2
1.5
1
0.5
0

0

5

10

15

20

25

30

35

40
45
pT Cone40 / pT

DATA / MCDATA / MC

10-2

0

5

10

1
DATA, QCD enriched sample

10-1

20

25

30

35
40
45
ET Cone40 / pT(d) Econe40/ptrack
T
T
Tracks (a.u.)Tracks (a.u.)(c) pcone40/ptrack
T
T

15

MC, QCD multi-jets

1
DATA, QCD enriched sample

MC, QCD multi-jets

10-1

10-2
10-2

0

5

10

Tracks (a.u.)

(e)

15

P

20

25

30

35
40
45
ET (dR<0.4) / pT

DATA / MC

2.5
2
1.5
1
0.5
0

2.5
2
1.5
1
0.5
0

-0.4

-0.2

0

clus
track
∆R<0.4 ET /pT

0.2

0.4
d0 w.r.t PV [mm](f) d0

1

Tracks (a.u.)

DATA / MC

10-3

DATA, QCD enriched sample

MC, QCD multi-jets

10-1

10
DATA, QCD enriched sample

1
MC, QCD multi-jets

10-1

2.5
2
1.5
1
0.5
0

10-2

-20

-10

0

10

20
d0 / σ(d )DATA / MCDATA / MC

10-2

2.5
2
1.5
1
0.5
0

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0

(g) d0 significance

0.4

0.6 0.8
1z0 sinθ [mm](h) z0 sin θ

Figure 6.3: Reconstructed track pT , η, isolation information, and impact parameter variables for hadron tracks in the QCD multi-jet enriched sample and MC
predictions.

77

10
DATA, QCD enriched sample

1
MC, QCD multi-jets

Tracks (a.u.)Tracks (a.u.)

6.2 T P

10-1

1
10-1
10-2
10-3

10-2

DATA, QCD enriched sample

10-4

MC, QCD multi-jets

10-3

10-5

2.5
2
1.5
1
0.5
0

2.5
2
1.5
1
0.5
0

0

0.5

1

1.5

2

2.5

3

3.5
4
N(b-layer)

DATA / MCDATA / MC

10

2

10
DATA, QCD enriched sample

1

MC, QCD multi-jets

10-1

10-3
10-4

-5

10-5

2.5
2
1.5
1
0.5
0

2.5
2
1.5
1
0.5
0

10

2.5
3
N(PixelDeadSenor)

DATA / MCDATA / MC

10

2

0

DATA, QCD enriched sample

1
MC, QCD multi-jets

10-1

5.5

6

6.5
7
N(Pixel)MC, QCD multi-jets

0.5

1

1.5

2

2.5

3

3.5
4
N(PixelHoles)(d) Number of pixel holes

Tracks (a.u.)Tracks (a.u.)(c) Number of dead pixel sensors

105DATA, QCD enriched sample

-4

1.5

4.5

1

10-3

1

4

10-1
10-2

0.5

3.5

10

10-2

0

3

(b) Number of pixel hits

Tracks (a.u.)Tracks (a.u.)(a) Number of b-layer hits

2.5

10
DATA, QCD enriched sample

1

MC, QCD multi-jets

10-1

10-2
10-3

10-2

-4

2.5
2
1.5
1
0.5
0

0

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2

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3
N(PixelOutliers)

DATA / MCDATA / MC

10

2.5
2
1.5
1
0.5
0

0

10
DATA, QCD enriched sample

1
MC, QCD multi-jets

10-1

1

1.5

2

2.5
3
N(GangedPixel)(f) Number of ganged pixel hits

Tracks (a.u.)Tracks (a.u.)(e) Number of pixel outliers

0.5

10-2

10
DATA, QCD enriched sample

1

MC, QCD multi-jets

10-1

-3

10-4

10-2

2.5
2
1.5
1
0.5
0

2.5
2
1.5
1
0.5
0

0

0.5

1

1.5

2
2.5
3
N(GangedFlaggedFakes)(g) Number of ganged pixel hits flagged as fake

DATA / MCDATA / MC

10

0

0.5

1

1.5

2

2.5
3
N(SpoiltPixels)(h) Number of spoilt pixel hits

Figure 6.4: Pixel-hit variables for hadron tracks in the QCD multi-jet enrichedsample and MC predictions.

2.5
2
1.5
1
0.5
0

1

DATA, QCD enriched sample

MC, QCD multi-jets

10
DATA, QCD enriched sample

1
MC, QCD multi-jets

10-1
10-2
10-3

1.5

2

2.5

3

3.5

4

4.5
5
N(SCT)DATA / MCDATA / MC

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

Tracks (a.u.)

6.2 T P

Tracks (a.u.)

78

2.5
2
1.5
1
0.5
0

0

10
DATA, QCD enriched sample

1
MC, QCD multi-jets

DATA, QCD enriched sample

MC, QCD multi-jets

10

10-2

10-2

10-3

10-3

2.5
2
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1
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0

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2
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1
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0

0.5

1

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2

2.5

3

3.5

4
4.5
5
N(SCTOutliers)

0

2.5
2
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1
0.5
0

2.5
2
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1
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0

DATA, QCD enriched sample

MC, QCD multi-jets

10-2

0.5

1

1.5

2

2.5

3

(e) χ2 /ndf

3.5

4

4.5
5
χ2 / ndf

Tracks (a.u.)

10-3

0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0

1

10-1

0.5

1

1.5

2

2.5
3
N(SpoiltSCT)(d) Number of spoilt SCT hits

DATA / MC

Tracks (a.u.)

5
6
N(SCTHoles)

1(c) Number of SCT outliers

DATA / MC

4

10

10

0

3

-1

DATA / MCDATA / MC

-1

0

2

(b) Number of SCT holes

Tracks (a.u.)Tracks (a.u.)(a) Number of SCT hits

10DATA, QCD enriched sample

MC, QCD multi-jets

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8 0.9
1
probability(χ2,ndf)(f) χ2 probability

Figure 6.5: SCT-hit and track quality variables for hadron tracks in the QCD multijet enriched sample and MC predictions.

6.2 T P

Variable
Number of deadsensors
Number of holes
Number of outliers
Number of spoilt hits
Number of ganged
pixelspcone40
T
Econe40
TP

clus∆R<0.4 ET

79

Description
The number of dead sensors on the track
The number of pixel layers or SCT sensors on the trackwith the absence of hits
The number of hits with bad track-fit qualities
The number of hits with broad position errors
The number of hits formed in a single pixel rowof which the readout is shared with another pixel
The sum of pT of all tracks with pT > 400 MeV,
|d0 | < 1.5 mm, and |z0 sin θ| < 1.5 mm
that lie within a cone of ∆R = 0.4 around the track
The transverse calorimeter energy depositedin a cone of ∆R < 0.4 around the track
excluding ET of its representing calorimeter cluster
The sum of cluster energies
in a cone of ∆R = 0.4 around the track

Table 6.1: Definition of variables for track hit information and track quality.

80

6.2 T P

Chapter 7

Properties of Signal Events
7.1 Production process
The AMSB model can be explored in the following two production processes atthe LHC.
• Strong production process
Production cross-sections for gluinos via strong interactions are predicted tobe large in pp collisions. The typical cross-section is ∼ 0.1 pb for a gluinowith a mass of ∼ 1 TeV. In the AMSB model, the masses of other sparticles
with color charge are predicted to be O(10-100) TeV as indicated in Sec. 2.3.4.
Therefore, only the gulino among colored sparticles can be produced at
LHC energies. The event topology of the gluino-pair production process ischaracterized by multiple high−pT jets emitted from the cascade decay of thegluino and a large Emissdue to the LSPs escaping the detector.
T• Direct electroweak gaugino production process
The lightest charginos can be directly produced via electroweak interactions
with a sizable cross-section at LHC energies as shown in Fig. 4.3. The dom±
± 0
+ −inant channels of the χ̃1 electroweak production are χ̃1 χ̃1 and χ̃1 χ̃1 . The
typical cross-section is ∼ 1 pb for a chargino with a mass of ∼ 200 GeV, and20000 events are expected with the integrated luminosity used in this search(∼ 20.3 fb−1 ).
Previously, ATLAS explored charginos that have significant lifetimes via thegluino-pair production process [9,10,11]. However, a stringent limit of < 1.4 TeV [12]has been already set on the gluino mass by inclusive searches for squark and gluino
production as shown in Fig. 7.1. Given the ratio M3 /M2 of ∼ 8 in the AMSB model,
the mass of gluino is comparatively large. These facts indicate that the gluino-pair
production process is suppressed at LHC energies, and the electroweak gaugino production is of particular importance. This search is dedicated to the directelectroweak gaugino production. Figure 7.2 shows an event display of the direct81

82

7.2 P  S E
± 0

production of χ̃1 χ̃1 in a signal MC event. Since a chargino decays into a neutralino
and a low-momentum charged pion in the ID, the signal events can not be triggered by a chargino. Therefore, a high-pT jet from initial-state radiation is utilized
to trigger the signal event. The resulting signal topology is then characterized by• A high-pT jet.
• Large missing transverse momentum due to LSPs escaping the detector.
• A high-pT disappearing track.
Primary diagrams contributing to this search are illustrated in Fig. 4.4.
~~~
∼0
gg production; g→ q q χ

ATLAS Preliminary

Observed limit (± 1 σtheory)1m0χ∼ [GeV]

1

1400

∫ L dt = 20.3 fb , s=8 TeV

Expected limit (± 1 σexp )Observed limit (4.7 fb -1, 7 TeV)

0-lepton combined

Expected limit (4.7 fb -1, 7 TeV)

SUSY

-1

1200
1000
800
600
400
200
200

400

600

800

1000

1200

1400
m~g [GeV]

Figure 7.1: 95% CL exclusion curve for the direct production of gluino pairs withdecoupled squarks. Gluinos are required to decay to two jets and a neutralino LSP.
The blue dashed lines show the expected limits at 95% CL, with the light (yellow)
bands indicating the 1σ excursions due to experimental and background-theory
uncertainties. Observed limits are indicated by medium (maroon) curves, where
the solid contour represents the nominal limit, and the dotted lines are obtainedby varying the signal cross-section by the theoretical scale and PDF uncertainties.

7.2 Finding decaying chargino tracks
The expected signature of the chargino signal strongly depends on its lifetime. As
shown in Fig. 7.3, the mean decay length of the lightest charginos is expected tobe O(cm). The pixel and SCT detectors cover the radial range from 50 to 514 mm,
and the TRT, of particular importance to this search, covers the radial range from
560 to 1100 mm in the barrel region.
Strategies to detect the chargino tracks can be then classified into the followings±
depending on the χ̃1 decay vertex:

7.2 P  S E

83

± 0

18
16

0.5

1

τχ∼± [ns]cτχ∼± [cm]0.6

1

Figure 7.2: Event display of the direct production of χ̃1 χ̃1 in a signal MC event.
0±
The true particle tracks in decays χ̃1 → χ̃1 π± are indicated by solid lines.

14
0.4

12
10

0.3
8
0.2

6
τχ∼± vs ∆mχ∼
1

0.1

4

1

∼± = 200GeV)∆mχ∼ (χ11∼±∆mχ∼ (χ1 = 400GeV)

2

1

0

145

150

155

160

0

165

170

∆mχ∼ [MeV]1

Figure 7.3: The τχ̃± (cτχ̃± ) as a function of ∆mχ̃1 . The light (dark) green band shows
1
1
the wino mass splitting (∆mχ̃1 ) derived from Fig. 2.4 for the wino mass of 200 (400)
GeV.

84

7.3 P  S E
• Rdecay < 50 mm: Charginos decay before reaching the ID, therefore, they±leave no hits in the detector; the detection of χ̃1 is impossible. In this case,the search for events with mono-jet plus large Emiss
may work. However, itTsuffers from irreducible background processes such as Z → ν ν̄ and W → τν̄, and has low sensitivity.
• 50 mm < Rdecay < 550 mm: Charginos decay in the silicon detectors andresults in short tracks that can be reconstructed by the Re-tracking.
• 550 mm < Rdecay < 1100 mm: Charginos decay in the TRT detectors. Their
tracks can be fully reconstructed with the ATLAS standard track reconstruction.

In the search based on the 7 TeV dataset [13], the extended track reconstruction
was not adopted and the sensitivity to charginos having τχ̃± of a fraction of a1
nanosecond was poor as shown in Fig. 2.9, which is attributed to the fact that the
ATLAS standard track reconstruction is non-optimal for decaying charginos. It
requires at least seven silicon hits associated to a track, resulting in an inefficiency
for charginos decaying before reaching the outer SCT layers. The Re-tracking
proceeds by re-running the pixel-seeded tracking algorithm and provides a fully
efficient tracking capability for charginos traversing the pixel detector. The track
selection efficiency for decaying charginos has been improved significantly as
shown in Fig. 7.4. The efficiency for charginos with τχ̃± ∼ 0.2 ns, predicted for1∆mχ̃1 ∼ 160 MeV, is around 100 times larger than in the previous searches. Details
on the track selection are given in Sec. 8.2.5.

7.3 Properties of chargino tracks
There are no SM processes that predict the disappearing-track signature. Therefore, the vast majority of background events can be removed by identifying adisappearing track in the event. However, tracks with a small number of hits inthe silicon detectors can be easily formed by a wrong combination of space-points
originating from low-pT charged particles that are not reconstructed, and could
have anomalously high values of pT . Such pT -mismeasured tracks can mimic thedisappearing-track signature. This section investigates ways to suppress the pT mismeasured tracks by comparing track properties and to enhances the searchsensitivity.
Figure 7.5–7.9 show the track properties for charginos with a mean lifetimeof 0.2 ns and pT -mismeasured tracks in a QCD multi-jet enriched sample. The
properties for the hadron tracks that are fully reconstructed in a QCD multi-jet
enriched sample are also shown in the figure. The following requirements arecommonly applied to the tracks.
The common requirements

85

1
TRT

1000

0.9
0.8

800

Efficiency

Radius (mm)7.3 P  S E

0.7
0.6

600

SCT

0.5
0.4

400

0.3
200

0.2

Pixel

0.1
0

-2

-1

0

1

2

0
η

1
TRT

1000

0.8

Efficiency

Radius [mm](a) ATLAS standard track reconstruction

800
0.6
600

SCT

0.4

400
200
0

0.2

Pixel

-2

-1

0

1

2

0
η

(b) Extended track reconstruction

Figure 7.4: Track selection efficiency for decaying charginos of (a) the ATLAS
standard track reconstruction and (b) the extended track reconstruction with theapplication of the track selection requirements given in Sec. 8.2.5.

86

7.3 P  S E
• pT > 15 GeV.
• Tracks must point the TRT active region 0.1 < |η| < 1.9.
• |d0 | < 1.5 mm.
• |z0 sin θ| < 1.5 mm.
• Nb−layer ≥ 1, if crossing an active module of the innermost pixel layer.
• Npixel ≥ 1.
• NSCT ≥ 2.

In addition to requirements listed above, the chargino signal, pT -mismeasured
tracks, and hadron tracks are required to fulfill the following criteria.
The definition of chargino tracks• The reconstructed track is required to originate from the chargino based on
the MC truth information.
The definition of pT -mismeasured tracks• The reconstructed track pT must be at least two times larger than the true pT :(pT rec -pT true )/pT true > 1.0.
The definition of hadron tracks• The track must accompany hadronic activities in the calorimeter: ∆R between
the track and nearest jets with pT > 20 GeV must be less than 0.1.
Properties of chargino tracks The chargino tracks have high pT and are wellisolated from hadronic activities as shown in Fig. 7.5. The pT -mismeasured tracks
are presumably originated from low-pT charged particles in pile-up jets, therefore,pT -mismeasured tracks could have finite hadronic activities around them. By
requiring a small hadronic activity around the candidate track, not only hadron
tracks but also pT -mismeasured tracks can be suppressed.
Concerning the hit information in the pixel detector, the pixel qualities for pT mismeasured tracks become poor because they are formed by a wrong combinationof space-points of different particles. Therefore, the number of absent hits (holes)in the pixel detector, hits with bad track-fit qualities (outliers), and hits with broad
position errors (spoilt hits) for pT -mismeasured tracks become large compared tothose for charginos and hadron tracks as shown in Fig. 7.6. A similar feature isobserved in the SCT detector as shown in Fig. 7.7.

7.3 P  S E

87

The number of hits in the SCT detector for charginos depends on the proper
lifetime of the chargino. As shown in Fig. 7.7 (a), the number of SCT hits forcharginos with a mean lifetime of 1.0 ns peaks at NSCT ∼ 8, while that with a mean
lifetime of 0.2 ns peaks at NSCT ∼ 2.
The mechanism that makes pT -mismeasured tracks implies that their impact
parameters are randomly distributed as shown in Fig. 7.5 (e), (f), and (g). Therefore, a large amount of pT -mismeasured tracks can be removed by requiring asmall impact parameter without any loss of chargino tracks. The track-quality
information of pT -mismeasured tracks are also shown in Fig. 7.7 (e) and (f).
The energy loss information (dE/dx), provided by the pixel detector, is expected
to help to identify charginos especially having high masses. Figure 7.8 shows
the dE/dx for chargino tracks with various masses, pT -mismeasured, and hadron
tracks. The charginos that remain after the monojet selection applied at the trigger
level are highly boosted, and pT -mismeasured tracks could have large dE/dx asthey originate from low-pT hadrons. Therefore, the discrimination of charginos
based on the dE/dx information does not work effectively.
When charginos decay before reaching the TRT detector (Rdecay < 563 mm in
the barrel region), NTRT is expected to have a value near zero; conversely, charged
particles traversing the TRT typically have NTRT ' 34. Therefore, vast majority ofhadron tracks can be removed by requiring few hits in the TRT detector as shownin Fig. 7.9

7.3 P  S E

10

Tracks (a.u.)Tracks (a.u.)

88

pT-mismeasured tracks
Hadron tracks

1

Charginos ( τ∼χ± = 0.2 ns )
1

-1

10

0.06
p -mismeasured tracks

0.05

Charginos ( τ∼± = 0.2 ns )T

χ1

Hadron tracks

0.04
0.03
0.02

10-2
0.01
-3

10

20

30 40

200 300
1000
Track p [GeV]

100

-1.5

-1

-0.5

0

0.5

1

1.5
Track ηT(b) Track η

10Tracks (a.u.)Tracks (a.u.)(a) Track pT

pT-mismeasured tracks

1

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )
1

10-1

10
pT-mismeasured tracks

1

10-2

10-3

10-3

0.2

0.4

0.6

0.8

1

1.2

1

10-1

10-2

10-40

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )

1.4

10-40

1.6 1.8
2
p cone40 / p
T

0.2

0.4

Tracks (a.u.)Tracks (a.u.)

pT-mismeasured tracks
Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )1

10-1

p -mismeasured tracks

10-3

0.8

1

1.2

1.4

χ1

Hadron tracks

10-4-1.5

1.6 1.82
ET(dR<0.4) / p

-1

-0.5

0

T

P

clus
track
∆R<0.4 ET /pT

10
p -mismeasured tracks

Charginos ( τ∼± = 0.2 ns )

T

1

χ1

Hadron tracks

p -mismeasured tracks

10-2

10-2

10-3

10-3

0

(g) d0 significance510d0 / σ(d0)Charginos ( τ∼± = 0.2 ns )

T

1
10-1

-5

1
1.5
d0 w.r.t PV [mm]

10

10-1

10-4-100.5(f) d0Tracks (a.u.)Tracks (a.u.)(e)

1.6 1.82
ETcone40 / p

10-1

10-3

0.6

1.4

Charginos ( τ∼± = 0.2 ns )

T

1

10-2

0.4

1.2

10

10-2

0.2

1

(d) Econe40/ptrack
T
T

10

10-40

0.8

T

(c) pcone40/ptrack
T
T

1

0.6

T

10-4-1.5

χ1Hadron tracks

-1

-0.5

0

0.5
1
1.5
z0sinθ w.r.t PV [mm](h) z0 sin θ

Figure 7.5: Reconstructed track pT , η, isolation information, and impact parametervariables for charginos, pT -mismeasured tracks, and hadron tracks in QCD multijet MC events.

89

103
pT-mismeasured tracks

2

10

Hadron tracks

10

Charginos ( τ∼χ± = 0.2 ns )Tracks (a.u.)Tracks (a.u.)

7.3 P  S E

103
pT-mismeasured tracks

102

Hadron tracks

10

Charginos ( τ∼χ± = 0.2 ns )

1

1

1

1

10-1

10-1

10-2

10-2

-3

10-3

10

10-40

0.5

1

1.5

2

2.5

3

10-40

3.5
4
N(b-layer)

102
pT-mismeasured tracks

10

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )

1

1

4

5

6

7
8
N(Pixel)pT-mismeasured tracks

10

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )

1
10

-2

10

10-2

10-3

10-3

1

-1

10

0.5

1

1.5

10-40

2
2.5
3
N(PixelDeadSensor)

102
pT-mismeasured tracks

10

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )

1

0.5

1

1.5

2

2.5
3
N(PixelHoles)(d) Number of pixel holes
Tracks (a.u.)(c) Number of dead pixel sensors
Tracks (a.u.)

3

102

-1

10-40

2

(b) Number of pixel hits
Tracks (a.u.)Tracks (a.u.)(a) Number of b-layer hits

1102
pT-mismeasured tracks

10

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )

1

1

1
-1

10

10-1
10-2
10-2

10-3
10-40

0.5

1

1.5

2

10-30

2.5
3
N(PixelOutliers)102
pT-mismeasured tracks

10

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )1

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )
1

1

10-1

10-1

10-2

10-2

1

1.5
2
2.5
3
N(GangedPixel flagged as fake)(g) Number of ganged pixel hits flagged as fake

2.5
3
N(GangedPixel)

pT-mismeasured tracks

10

1

0.5

2

102

1

10-30

1.5

(f) Number of ganged pixel hits
Tracks (a.u.)Tracks (a.u.)(e) Number of pixel outliers

0.5

10-30

0.5

1

1.5

2

2.5
3
N(SpoiltPixels)(h) Number of spoilt pixel hits

Figure 7.6: Pixel-hit variables for charginos, pT -mismeasured tracks, and hadron
tracks in QCD multi-jet MC events.

102
Charginos ( τ∼χ± = 0.2 ns )p -mismeasured tracks
T

10

1

Hadron tracks

Charginos ( τ∼χ± = 1.0 ns )1

1

Tracks (a.u.)

7.3 P  S E

Tracks (a.u.)

90

102

10

-2

10

10-2

10-3

10-3

1

-1

10

2

4

6

8

10

12

14

10-40

16
18
N(SCT)(a) Number of SCT hits
102
pT-mismeasured tracks

10

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )

1

0.5

1

1.5

2

2.5
3
N(SCTHoles)(b) Number of SCT holes
Tracks (a.u.)Tracks (a.u.)

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )

1

-1

10-40

pT-mismeasured tracks

10102
pT-mismeasured tracks

10

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )

1

1

1
10-1
10-1
10-2
10-2

10-3
10-40

0.5

1

1.5

2

2.5

3

10-30

3.5
4
4.5
5
N(SCTOutliers)10
pT-mismeasured tracks

1

1

1.5

Hadron tracks
Charginos ( τ∼χ± = 0.2 ns )2.5
3
N(SpoiltSCT)

10
pT-mismeasured tracks
Hadron tracks

1

Charginos ( τ∼χ± = 0.2 ns )

1

10-1

2

(d) Number of spoilt SCT hits
Tracks (a.u.)Tracks (a.u.)(c) Number of SCT outliers

0.5

1

-1

10
10-2

10-2

10-3
10-40

1

2

3

4

5

10-30

0.1

0.2

0.3

0.4

0.5

0.6

χ2 / ndf(e) χ2 /ndf

0.7 0.8 0.9
1Probability(χ2,ndf)(f) χ2 probability

Figure 7.7: SCT-hit variables and track quality information for charginos, pT mismeasured tracks, and hadron tracks in QCD multi-jet MC events.

91

10
9

Charginos with mass = 200 GeV

8
7

10-3

6
5
4

dE/dx [MeV g-1 cm2]

dE/dx [MeV g-1 cm2]

7.3 P  S E

10
9

6

10-5

5
4
3

2

10-6

2

1
0
0

10-4

7

10-4

3

Charginos with mass = 200 GeV

81
100 200 300 400 500 600 700 800 900 1000
Track p [GeV]

10-5

0
0

100 200 300 400 500 600 700 800 900 1000
Track p [GeV]

dE/dx [MeV g-1 cm2](a) The dE/dx vs track momentum for chargino (b) The dE/dx vs track momentum for chargino
tracks and pT -mismeasured tracks
tracks and hadron tracks
5
4.5m∼χ± = 100 GeV

4m∼χ± = 200 GeV

3.5m∼χ± = 300 GeV

1

1

1

3
2.5
2
1.5
1
0.5
0

100 200 300 400 500 600 700 800 900 1000
Track p [GeV](c) The dE/dx vs track momentum for chargino
tracks with various masses

Figure 7.8: The dE/dx information for charginos, pT -mismeasured tracks, andhadron tracks in QCD multi-jet MC events.

7.3 P  S E

Tracks (a.u.)

92

102
pT-mismeasured tracks
Hadron tracks

10

Charginos: τ∼χ± = 0.2 ns (Decay radius < infinite)

1

1

Charginos: τ∼χ± = 0.2 ns (Decay radius < 563 mm)
1

10-1
10-2
10-3
10-4
10-50

5

10

15

20

25

30

35

40

45 50
N TRT

Figure 7.9: The NTRT distribution for charginos, pT -mismeasured tracks, andhadron tracks in QCD multi-jet MC events. When charginos decay before reaching
the TRT detector (Rdecay < 563 mm), NTRT is expected to have a value near zero;conversely, charged particles traversing the TRT typically have NTRT ' 34.

Chapter 8

Event Selection, can
The SM processes, especially W+jet events that naturally have large Emiss
T
result in final-state kinematics similar to that of the signal. Therefore, the signal
events can not be enhanced by any kinematic selection requirements, and tightrequirements on kinematics reduce the signal efficiency a lot. In this analysis,a dedicated topological trigger is developed to attain a higher signal efficiency.
Kinematic selection criteria are applied to ensure a high trigger efficiency, andthe vast majority of background events are removed by identifying a high-pT
disappearing track in the event.

8.1 Online selection
8.1.1 Trigger
As stated in Sec. 7.1, the signal event topology is characterized by a high–pT jet andlarge Emiss, and they are well balanced in the transverse plane. Therefore, the analyT
sis makes use of a dedicated topological trigger named EF j80 a4tchad xe70 tclcw dphi2j45xe10
in order to suppress a huge QCD multi-jet events and attain a higher signal efficiency than that of ATLAS standard triggers. Details on the selection requirements
are described in Sec. 4.1.1.
jet−EmissFigure 8.1 shows the ∆φmin T distributions for signal and SM MC events
jet−Emissafter the trigger requirement except for the ∆φmin T

selection. The signal events

jet−Emissmake up the population at ∆φmin T ∼ π while QCD multi-jet events have smaller
jet−Emissvalues of ∆φmin T due to the fact that Emiss
originates most likely from the jet–pTT
missmismeasurements, and ET and the mismeasured jet are aligned in the transverse

plane. This trigger helps to reject the majority of QCD multi-jet events online.
jet−EmissAs shown in Fig. 8.2, by requiring ∆φmin T > 1.0, nearly half of QCD multi-jet
events can be removed without any loss of signal events with mχ̃± = 200 GeV
1and τχ̃± = 0.2 ns. Figure 8.3 shows estimated event rates at a peak luminosity
1

93

94

8.1 E S
jet−Emissof 7.7 × 1033 cm−2 s−1 as a function of a threshold on a ∆φmin T for QCD multijet events, W → lν+jets events, Z → νν̄+jets events, and signal events, whichjet−Emissindicates that the event rate can be reduced to ∼ 4 Hz with ∆φmin T > 1.0 cut.
However, a part of events is also triggered by other triggers used in ATLAS SUSY
searches [12,101] whose trigger rates are ∼ 10 Hz. Table 8.1 shows overlap fractions
and rate increases for QCD multi-jet events, W → lν+jets events, and Z → νν̄+jets
events. The actual contribution to the total event rate is reasonably small andestimated to be ∼ 2 Hz.

Events (a.u.)

Table 8.2 shows the trigger efficiency for signal events with mχ̃± = 200 GeV and1τχ̃± = 0.2 ns. The trigger efficiencies of other triggers used in ATLAS SUSY searches
1
are also shown. The topological trigger enables to achieve a higher efficiency thanthose of the standard triggers.

107
106
105
104
103
QCD multi-jets
Z → νν
W → lν
Z → lltt and single top
Signal×100 (m∼± = 200 GeV, τ∼χ± = 0.2 ns)

102

χ

0

0.5

1

1.5

2

1

1

2.5

3

jet-E miss∆φmin T
jet−EmissFigure 8.1: The ∆φmin T

distributions for signal and SM MC events.

Overlap fraction
Rate increase

QCD
32 %
1.4 HzW → lν
88 %0.14 HzZ → νν̄89 %
0.08 Hz

Table 8.1: Overla fraction of the topological trigger with other triggers used in
ATLAS SUSY searches [12, 101]. The actual contribution to the total event rate isalso shown.

Efficiency

8.1 E S

95

110
100
90
80
70
60
50
QCD

40

W→ lν

30

Z→νν
Signal (m∼ ± = 200 GeV, τ∼χ± = 0.2 ns)
χ

20

0

1

1

0.5

1

1.5

2

jet-E miss

∆φmin

T

thresholdjet−EmissFigure 8.2: Trigger efficiencies as a function of a threshold on a ∆φmin T requiremenet for QCD multi-jet events, W → lν+jets events, Z → νν̄+jets events andsignal events with mχ̃± = 200 GeV and τχ̃± = 0.2 ns.
1

Event rate (Hz) @ L = 7×1033cm-2s-1

1

4

QCD
W →l ν

3.5

Z→νν

3

10 × Signal (m∼ ± = 200 GeV, τ∼χ± = 0.2 ns)
3

χ

1

1

2.5
2
1.5
1
0.5

0

0.5

1

1.5

2

jet-E miss

∆φmin

T

threshold

Figure 8.3: Estimated event rates at a peak luminosity of 7.7 × 1033 cm−2 s−1 asjet−Emissa function of a threshold on a ∆φmin T requiremenet for QCD multi-jet events,
W → lν+jets events, and signal events with mχ̃± = 200 GeV and τχ̃± = 0.2 ns.
1

1

96

8.1 E S
Trigger

EF j80 a4tchad
xe100 tclcw
veryloose> 80 GeV> 100 GeVEF xe80 tclcw loose

Leading jet pT
Emiss
TEF j80 a4tchad
xe70 tclcw
dphi2j45xe10> 80 GeV> 70 GeV

Rate
Signal efficiency> 1.0∼ 4 Hz6.9 %∼ 2 Hz3.0 %∼ 10 Hz
4.6 %jet−Emiss∆φmin T

> 80 GeV

Table 8.2: Comparison of trigger efficiencies for signal events (mχ̃± = 200 GeV, τχ̃±1
1
= 0.2 ns) with three different triggers.
Efficiency
In order to evaluate the trigger efficiency for the signal events, a data control
sample collected by the muon triggers is used. The events are required to pass thefollowing criteria:• Trigger : EF mu24i tight or EF mu36 tight 1 .
• No muons that have significantly large values of the curvature error, |d0 | and|z0 |, which could arise from failures of reconstruction or cosmic-ray muons.
• No identified electrons with pT > 10 GeV.
• Exact one reconstructed muon with pT > 25 GeV and | η |< 2.4.
• pcone40/pT < 0.1.
T
• mT > 40 GeV.
• Emiss> 40 GeV.
T
where mT is the transverse mass between a lepton and Emissdefined byTqlepton ~ misslepton miss· ET ).
ET − ~
pT
mT = 2(pT(8.1)

These requirements remove the vast majority of QCD multi-jet events and enhance
W → µν+jets events.
The signal event topologies can be emulated by subtracting the muon contribution in the calculation of Emissfor the W → µν+jets data control sample.
Tjet−EmissFigure 8.4 (a) and (b) show the offline ∆φmin T distributions in the W → µν+jets
control sample before and after the trigger requirement. The data and MC events
jet−Emissreasonably agree, and the cut of 1.5 on ∆φmin T
1places it on plateau.

Triggers used in this analysis are summarized in Sec. 4.1.

97

106

Events (a.u.)

Events (a.u.)

8.2 E S

105

105
104
103

104
103

102
Data ( s = 8 TeV,

102

Total background10

Z → ll∫-1

L dt = 20.3 fb )Data ( s = 8 TeV,

10

W → lν

0.5

1

1.5

-1

W → lν
Z → ll1

tt and single top

10∫ L dt = 20.3 fb )Total background2

tt and single top

2.5

3

10-10

miss

jet-E T

0.5

1

1.5

2

2.5

3

miss

jet-E T

∆φ

∆φ

min

min

(a) Before the trigger requirement(b) After the trigger requirement

jet−EmissFigure 8.4: Offline ∆φmin T distribution for the W → µν control sample before(a) and after (b) applying the trigger requirement.

Trigger Efficiency

Trigger Efficiency

The trigger efficiency is then given by the fraction of events passing the trigger
after the application of W → µν selection criteria. Figure 8.5 (a) and (b) show thetrigger efficiencies as a function of the leading jet pT and Emiss. The requirementsT
missof ET > 90 GeV and pT > 90 GeV on the leading jet are applied in Fig. 8.5 (a)and (b), respectively. Two-dimensional efficiency maps and its data/MC ratio arealso shown in Fig. 8.6 and 8.7, respectively. The MC simulation describes thedata well, and the trigger acceptances for the data and MC events surviving thekinematic selection described in Sec. 8.2.4 agree within 4.5%; this value is assigned
as a systematic uncertainty on the trigger efficiency for the signal MC events.

1
0.8
miss

ET

> 90 GeV

0.6

1
0.8
1st jet p > 90 GeV
T

0.6

Data

0.4
0.2
0
40

Data

MC (W → µν)
s = 8 TeV,

60

80

100

120

∫ L dt = 20.3 fb

140

MC (W → µν)

0.4-1s = 8 TeV,

0.2

160 180 200
1st jet p [GeV]

0
40

60

80

100

120

T

(a) Leading jet pT∫ L dt = 20.3 fb

140

160

-1

180 200
Emiss
[GeV]
T

(b) EmissT

Figure 8.5: Trigger efficiency as a function of the leading jet pT (a) and Emiss(b).
T8.2 E S

E Tmiss [GeV]

98

0.371 0.167 0.693 0.983 0.993 0.996 0.996 0.996 0.994 0.996 0.990± 0.082 ± 0.036 ± 0.027 ± 0.005 ± 0.003 ± 0.002 ± 0.002 ± 0.002 ± 0.002 ± 0.001 ± 0.002

200

0.228 0.196 0.717 0.978 0.996 0.996 0.993 0.996 0.993 0.994 0.985± 0.047 ± 0.025 ± 0.018 ± 0.004 ± 0.002 ± 0.001 ± 0.002 ± 0.001 ± 0.002 ± 0.002 ± 0.003

180
160

0.8

0.106 0.083 0.660 0.975 0.993 0.993 0.994 0.992 0.983 0.968 0.942± 0.013 ± 0.007 ± 0.009 ± 0.003 ± 0.001 ± 0.001 ± 0.001 ± 0.001 ± 0.002 ± 0.005 ± 0.010

0.7

0.053 0.080 0.638 0.965 0.987 0.992 0.993 0.985 0.967 0.932 0.920± 0.006 ± 0.005 ± 0.006 ± 0.002 ± 0.001 ± 0.001 ± 0.001 ± 0.002 ± 0.004 ± 0.009 ± 0.015

140

0.020 0.065 0.610 0.940 0.980 0.987 0.983 0.964 0.936 0.901 0.874± 0.002 ± 0.003 ± 0.004 ± 0.002 ± 0.001 ± 0.001 ± 0.001 ± 0.003 ± 0.007 ± 0.014 ± 0.023

120

0.007 0.059 0.561 0.898 0.959 0.968 0.954 0.936 0.908 0.833 0.905± 0.001 ± 0.002 ± 0.003 ± 0.002 ± 0.001 ± 0.002 ± 0.003 ± 0.006 ± 0.010 ± 0.021 ± 0.021

100

0.002 0.044 0.455 0.800 0.886 0.897 0.881 0.836 0.834 0.805 0.890± 0.000 ± 0.001 ± 0.002 ± 0.002 ± 0.002 ± 0.004 ± 0.006 ± 0.011 ± 0.017 ± 0.025 ± 0.028

80
60

0.5
0.4
0.3

0.000 0.010 0.119 0.241 0.284 0.285 0.259 0.263 0.296 0.234 0.295± 0.000 ± 0.000 ± 0.001 ± 0.003 ± 0.005 ± 0.009 ± 0.014 ± 0.021 ± 0.029 ± 0.032 ± 0.049

0.2

0.000 0.001 0.007 0.010 0.008 0.011 0.014± 0.000 ± 0.000 ± 0.001 ± 0.001 ± 0.002 ± 0.003 ± 0.006

40

0.6

0.001 0.027 0.294 0.576 0.663 0.672 0.660 0.629 0.622 0.641 0.587± 0.000 ± 0.001 ± 0.002 ± 0.003 ± 0.004 ± 0.007 ± 0.012 ± 0.018 ± 0.026 ± 0.032 ± 0.045

0.000 0.003 0.032 0.064 0.061 0.058 0.056 0.047 0.050 0.092 0.053± 0.000 ± 0.000 ± 0.001 ± 0.002 ± 0.004 ± 0.006 ± 0.009 ± 0.012 ± 0.015 ± 0.029 ± 0.026

40

0.9

0.115 0.122 0.699 0.985 0.994 0.996 0.992 0.995 0.995 0.987 0.978± 0.023 ± 0.013 ± 0.012 ± 0.003 ± 0.001 ± 0.001 ± 0.001 ± 0.001 ± 0.001 ± 0.002 ± 0.005

60

80

0.018
± 0.0170.008± 0.008

100 120 140 160 180 200
1st jet p [GeV]

0.1
0

T

E Tmiss [GeV](a) Data

0.131 0.345 0.854 0.990 1.000 1.000 1.000 1.000 0.997 0.991 0.981± 0.123 ± 0.106 ± 0.051 ± 0.010 ± 0.000 ± 0.000 ± 0.000 ± 0.000 ± 0.003 ± 0.004 ± 0.011

200

0.424 0.262 0.715 0.992 1.000 0.995 0.996 0.989 0.987 0.984 0.981± 0.108 ± 0.068 ± 0.045 ± 0.006 ± 0.000 ± 0.003 ± 0.003 ± 0.004 ± 0.007 ± 0.008 ± 0.007

180
160

0.147 0.187 0.796 0.977 0.989 0.999 0.988 0.980 0.993 0.988 0.914± 0.046 ± 0.043 ± 0.024 ± 0.007 ± 0.008 ± 0.001 ± 0.006 ± 0.006 ± 0.003 ± 0.005 ± 0.019

0.8
0.7

0.023 0.105 0.699 0.945 0.975 0.987 0.974 0.956 0.878 0.900 0.824± 0.007 ± 0.008 ± 0.012 ± 0.006 ± 0.004 ± 0.002 ± 0.004 ± 0.007 ± 0.022 ± 0.028 ± 0.055

120

0.010 0.100 0.627 0.912 0.964 0.965 0.952 0.895 0.871 0.831 0.836± 0.002 ± 0.006 ± 0.010 ± 0.006 ± 0.004 ± 0.004 ± 0.006 ± 0.015 ± 0.027 ± 0.045 ± 0.067

100

0.002 0.076 0.557 0.826 0.892 0.918 0.883 0.810 0.777 0.782 0.880± 0.001 ± 0.004 ± 0.008 ± 0.007 ± 0.007 ± 0.007 ± 0.013 ± 0.025 ± 0.041 ± 0.054 ± 0.065

80
60

0.5
0.4
0.3

0.000 0.022 0.168 0.313 0.313 0.323 0.275 0.300 0.443 0.203 0.370± 0.000 ± 0.002 ± 0.006 ± 0.011 ± 0.017 ± 0.024 ± 0.034 ± 0.048 ± 0.066 ± 0.068 ± 0.099

0.2

0.002 0.020 0.024 0.009 0.006 0.023± 0.001 ± 0.004 ± 0.007 ± 0.005 ± 0.006 ± 0.016

40

0.6

0.001 0.048 0.387 0.634 0.685 0.687 0.621 0.646 0.549 0.643 0.591± 0.000 ± 0.003 ± 0.007 ± 0.009 ± 0.012 ± 0.018 ± 0.029 ± 0.042 ± 0.068 ± 0.074 ± 0.104

0.000 0.005 0.055 0.083 0.078 0.061 0.042 0.028 0.030 0.040 0.065± 0.000 ± 0.001 ± 0.005 ± 0.009 ± 0.013 ± 0.014 ± 0.017 ± 0.019 ± 0.029 ± 0.039 ± 0.063

40

0.9

0.063 0.184 0.756 0.981 0.992 0.996 0.994 0.988 0.974 0.962 0.879± 0.022 ± 0.026 ± 0.019 ± 0.005 ± 0.003 ± 0.002 ± 0.002 ± 0.003 ± 0.008 ± 0.010 ± 0.028
0.050 0.120 0.743 0.965 0.983 0.991 0.985 0.984 0.951 0.900 0.915± 0.011 ± 0.013 ± 0.015 ± 0.007 ± 0.005 ± 0.002 ± 0.004 ± 0.004 ± 0.012 ± 0.021 ± 0.027

140

1

60

80

0.040 0.031± 0.039 ± 0.031

100 120 140 160 180 200
1st jet p [GeV]

0.1
0

T

(b) MC

Figure 8.6: Trigger efficiency for the data (a) and MC events (b) as function of theleading jet pT and Emiss.
T8.2 E S

99

E Tmiss [GeV]

130
0.637
± 0.0350.921± 0.0200.981± 0.0101.011± 0.0091.000± 0.005

0.998± 0.0030.688± 0.0340.971± 0.0220.994± 0.0110.995± 0.0071.000± 0.006

1.002± 0.0050.682± 0.0300.870± 0.0160.975± 0.0110.978± 0.008

0.995± 0.008

0.992± 0.0090.694± 0.0290.835± 0.0150.955± 0.0130.994± 0.0140.980± 0.0120.980± 0.0140.663± 0.0280.802± 0.0180.904± 0.0180.927± 0.0190.983± 0.0270.984± 0.0340.576± 0.0290.778± 0.0270.858± 0.0300.936± 0.0391.003± 0.055

0.936± 0.069

120
110

0.95
0.9
0.85
0.8

100
90

0.75
0.7
0.65

80
70
70

1

80

90

100

0.6

110
120
130
1st Jet p [GeV]T

Figure 8.7: Ratio of the trigger efficiency between the data and MC events as afunction of the leading jet pT and Emiss.
T

8.2 Offline selection
First, rejection cuts to suppress events originating from non-collision background
events are applied to reconstructed jets. The signal candidate events are thenselected using kinematic variables together with the properties of disappearing
tracks. This section describes the kinematic and disappearing track selection
criteria. The data reduction is also summarized.

8.2.1 Primary vertex selection
The event vertex [92] is required to have at least five associated tracks with pT >0.4
When more than one such vertex is found, the vertex with the largest
P GeV.
|pT |2 of the associated tracks is chosen as primary.

8.2.2 Overlap removal
Since the physics objects described in Sec. 5 are reconstructed independently, theiroverlap must be resolved to avoid double counting. The overlap removal isproceeded in the following way.
1. First, a jet that overlaps with an electron is removed. If a jet is reconstructedwith ∆R < 0.2 of an electron, they are considered to be seeded by the same
object and in this case, the object is regarded as an electron.

100

8.2 E S

2. After the overlap removal, an electron with ∆R < 0.4 around a jet is removed
since jets are reconstructed with the distance parameter of ∆R = 0.4 and it isaccounted in the original jet.
3. Similarly, a muon with ∆R < 0.4 around a jet is removed. Unlike the electron
case, even if a muon is close to a jet, the muon deposits only a small amountof energy in the calorimeter. The removed muon is not counted as an isolated
muon candidate but its momentum is included in the missing momentum
calculation.

8.2.3 Event cleaning
Fake jets can be formed from non-collision background events such as beamhalo muons or cosmic-rays if the muons undergo significant bremsstrahlung in
the detector. The burst-noise in the HEC and the coherent noise in the LAr EM
calorimeter could also be reconstructed as fake jets. A set of requirements are designed to have a high rejection against such fake jets while keeping an inefficiency
of jets originating from pp collisions ∼ 0.1 % [102]. These selection requirements are
based on the timing information, the pulse shape, the fraction of energy depositedin specific calorimeter layers, and the information of tracks associated with thejets. Events are rejected if any jets that have pT > 20 GeV and fail to pass any of
the rejection requirements.

8.2.4 Kinematic selection criteria
In order to suppress background events from W/Z + jets and top-pair production
processes, events are discarded if they contain any electron or muon candidates(lepton veto). Events containing muons are further suppressed by requiring no
tracks with pT > 10 GeV reconstructed in the MS (stand-alone muon veto). The
candidate events are required to have Emiss> 90 GeV and at least one jet with pT >T
jet−Emiss90 GeV. In order to further suppress the QCD multi-jet events, ∆φmin T > 1.5 isalso required.2 The trigger selection is > 98% efficient for signal events satisfying
these selection requirements.
Figure 8.8 (a),(b), (c), and (d) show the distributions of leading jet pT , Emiss,
Tjet−Emiss∆φmin T , and the number of jets with pT > 45 GeV after applying all the kinematic
selection requirements and requiring at least one isolated track with pT > 15 GeV.
At this stage, the background events dominate in the search, however, the vast
majority of these backgrounds are removed by identifying a disappearing track.
2jet−EmissIf the event has only one jet with pT > 45 GeV, ∆φmin T

is calculated using the leading jet only.

101

106
105

s = 8 TeV,

∫ L dt = 20.3 fb-1

Events / 45GeV

Events / 45GeV

8.2 E S

Data
Total backgroundW → lν
Z → νν
Z → lltt and single top±±m(∼χ1 ) = 200 GeV, τ(∼χ1 ) = 0.2 ns

104
103

106
105

102

10

10

1

1

10-1

10-1
200

300

400

500

600

700

-1

Data
Total backgroundW → lν
Z → νν
Z → lltt and single top±±m(∼χ1 ) = 200 GeV, τ(∼χ1 ) = 0.2 ns

103

10

100

∫ L dt = 20.3 fb

104

2

10-2

s = 8 TeV,

10-2 100 200 300 400

800

500 600 700 800 900
miss

1st Jet pT [GeV]ET(b) Emiss
T

107
Data
Total background5

Z → νν

10

s = 8 TeV,

W → lν

10∫ L dt = 20.3 fb

Events (a.u.)

Events (a.u.)(a) Leading jet pT

6-1

Z → lltt and single top∼±) = 200 GeV, τ( χ∼±) = 0.2 ns
m(χ

104

1

107
106

102

10

10

1

1

10-1

10-1
1.8

2

-1
Data
Total backgroundW → lν
Z → νν

104

1

102

1.6

∫ L dt = 20.3 fb

10

103

1.4

s = 8 TeV,

5

103

10-2

2.2

2.4

2.6

2.8

3jet-E

∆φ

miss

10-20

Z → llt t and single top±±m(∼χ 1 ) = 200 GeV, τ (∼
χ 1 ) = 0.2 ns

2jet−Emiss

4

6

8

10

# of jets with pT > 45 GeVTmin(c) ∆φmin T[GeV](d) Number of jets with pT > 45 GeV
jet−EmissFigure 8.8: Distributions of leading jet pT , Emiss, ∆φmin T , and the number of jets
Twith pT > 45 GeV after all the kinematic selection cuts and requiring at least one
isolated track.

102

8.2 E S

8.2.5 Disappearing track selection
As shown in Fig. 7.5–7.9, the tracks originating from charginos are expected tohave high pT , to be isolated, and to have few associated hits in the outer region of
the ID. The TRT detector, in particular, provides substantial discrimination against
penetrating stable charged particles if only a small number of hits on the track isrequired. Therefore, candidate tracks for decaying charginos are required to fulfill
the following criteria:(I) the track must have Npixel ≥ 1, Nb-layer ≥ 1 if crossing an active module ofthe innermost pixel layer, NSCT ≥ 2, |d0 | < 0.1 mm, and |z0 sin θ| < 0.5 mm.
(II) the track reconstruction must be of good quality, meeting the following
requirements: it must have a track fit χ2 -probability of > 10%, no hits formed
in a single pixel row of which the readout is shared with another pixel (thenumber of ganged pixel hits flagged as fake is required to be zero), andno hits missing in active silicon modules along the trajectory between thefirst and last hit of the track (the number of holes in the silicon detectors is
required to be zero).
(III) the track must be isolated: it must fulfill pcone40/pT < 0.04. There must alsoT
be no jets having pT above 45 GeV within a cone of ∆R = 0.4 around thecandidate track.
(IV) the candidate track must have pT above 15 GeV, and must be the highest-pT
isolated track in the event.
(V) the candidate track must satisfy 0.1 < |η| < 1.9.
(VI) the number of TRT hits associated with the track NTRT , determined by counting hits lying on the extrapolated track, must be less than five.
These selections are determined by comparing the properties of chargino tracks
with those of background tracks as shown in Fig. 7.5–7.9. Criteria (I) and (II) areapplied in order to ensure well-reconstructed primary tracks. Criteria (III) and(IV) are employed to select chargino tracks that are isolated and have in most cases
the highest pT . These criteria also substantially reduce background tracks fromthe pile-up. Criterion (V) is used to ensure coverage by the TRT active region
and enhance the rejection of background tracks. Criterion (VI) helps to remove
the majority of background tracks in SM processes, as shown in Fig. 8.9. For SM
charged particles traversing the TRT detector, the number of TRT hits is typically
NTRT ' 32, whereas for charginos that decay before reaching the TRT the expected
is NTRT ' 0. The distributions for different η regions are also shown in Fig. 8.10.
Hereafter, “high-pT isolated track selection” and “disappearing-track selection”
indicate criteria (I)–(V) and (I)–(VI), respectively.

8.2 E S

103

TracksA summary of track selection requirements and the data reduction are given in
Table 8.3. After the application of all kinematic and disappearing-track selection
criteria, 3256 candidate events remain.
108
107
106

∫Ldt = 20.3 fb )SM MC predictionm∼χ± = 200 GeV, τ∼χ± = 0.2 ns (Decay radius < infinite)
1

105

-1

Data ( s = 8 TeV,

1m∼χ± = 200 GeV, τ∼ ± = 0.2 ns (Decay radius < 563 mm)
1

χ

10

20

1

104
103
102
10
1
10-1
10-20

30

40

50

60
N TRT

Figure 8.9: The NTRT distribution for data and signal MC events (mχ̃± = 200 GeV,1τχ̃± = 0.2 ns) with the high-pT isolated track selection. The expectation from SM MC
1events is also shown. The solid colored histogram shows the expected distribution
for charginos with a decay radius < 563 mm while the hatched histogram shows
it for all charginos for these mass and lifetime values. Tracks with NTRT < 5 in SM
events, mimicking the decaying-chargino signature, are described in Chapter 9.
The breakdown of the background processes and their origins, derived by
the MC simulation, are given in Table 8.4 and 8.5, respectively, showing thatthe primary contribution comes from charged hadrons predominantly in W(→τν)+jets processes.
The fraction of background events remaining after the kinematic and disappearingtrack selection criteria is significantly small; a background estimation based on theMC simulation would suffer from large uncertainties due to the poor statistics.
There is also a difficulty simulating the properties of background tracks. Therefore, a data-driven approach is employed to estimate the background track-pT
spectrum after the application of all the selection requirements, in particular thedisappearing track selection. Detailed descriptions of the background estimation
procedure are given in Chapter 9.

8.2 E S

Tracks

104

105
104

Data

SM MC prediction

Barrel ( 0.10 < |η| < 0.80 )

3

10

102
10
1
10-1
10-2
10-30

s = 8 TeV,

10

∫ L dt = 20.3 fb

-1

20

30

40

50

60
N TRT

Tracks(a) 0.1 < |η| ≤ 0.8
105
104

Data

SM MC prediction

Endcap ( 1.20 < |η| < 1.90 )

3

10

102
10
1
10-1
10-2
10-30

s = 8 TeV,

10

∫ L dt = 20.3 fb

20

30

-1

40

50

60
N TRT

Tracks(b) 1.2 < |η| ≤ 1.9
105
104

Data

SM MC prediction

Transition ( 0.80 < |eta| < 1.20 )

103
102
10
1
10-1
10-2
10-30

s = 8 TeV,

∫Ldt = 20.3 fb

10

20

-1

30

40

50

60
N TRT

(c) 0.8 < |η| ≤ 1.2

Figure 8.10: The NTRT distribution of high-pT isolated tracks pointing to the TRTbarrel (a), endcap (b), and transition (c) regions for data. The expectation from SM
MC events is also shown.

8.2 E S

105

Selection requirement

Observed events

Quality requirements and trigger
Jet cleaning
Lepton vetoLeading jet pT > 90 GeV> 90 GeV
Emiss
T

20479553
18627508
12485944
10308840
6113773

Expected signal MC events(efficiency [%])
1873 (8.8)
1867 (8.8)
1827 (8.6)
1571 (7.4)
1484 (7.0)∆φmin T > 1.5
High-pT isolated track selection
Disappearing-track selection

560408734379
3256

1444 (6.8)
21.9 (0.10)
18.4 (0.087)

jet−EmissTable 8.3: Summary of selection requirements and data reduction for data and expected signal events (mχ̃± = 200 GeV, τχ̃± = 0.2 ns). The signal selection efficiencies
1
1
are also shown in parentheses. Signal efficiencies are low at the first stage due tothe trigger based on a jet from initial-state radiation.

MC process
W(→ eν)+jets
W(→ µν)+jets
W(→ τν)+jets
Z(→ ee)+jets
Z(→ µµ)+jets
Z(→ ττ)+jets
Z(→ νν̄)+jets
tt̄
single top

Fraction [%]
10.6
4.1
71.5
0.01
0.17
2.7
4.1
5.5
1.3

Table 8.4: Contribution of individual SM background processes to the total background after the application of the kinematic selection requirements and the highpT isolated track selection.

Track origin
Charged hadron tracks
Electron tracks
Muon tracks
pT -mismeasured tracks

Fraction [%]
83.9
12.2
3.4
0.5

Table 8.5: Origins of the background tracks and their contribution to the total
background after the application of the kinematic selection requirements and thehigh-pT isolated track selection.

106

8.2 E S

Chapter 9

Background Estimation
Charged hadrons, leptons in the SM processes, and pT -mismeasured tracks remain
after the application of the high–pT isolated track selection requirements and afraction of them could be reconstructed as disappearing tracks. Background trackmechanisms are categorized as follows:• Charged hadrons interacting with the materials of the SCT detector.
• Charged leptons failing to satisfy their identification criteria.
• Low-pT charged particles whose pT is badly measured due to the scattering
by material of the ID.
The first category is labeled as “high–pT interacting-hadron tracks”, predominantly
originating from τ hadronic decays. The charged lepton background tracks originate most likely due to large bremsstrahlung or scattering before reaching the TRT
detector; the large contribution comes from low–pT electrons. The pT -mismeasured
tracks could remain after all event selection and have anomalously high values of
pT . Figure 9.1 shows an illustration of these disappearing high–pT tracks.
Interacting-hadron and electron tracks are responsible for the background in
the range pT < 50 GeV, whereas pT -mismeasured tracks are dominant for pT >100 GeV. A small contribution from muon tracks is expected throughout the fullpT range.
A background estimation based on the MC simulation has difficulty accurately
describing the properties of these background tracks. Therefore, the backgroundcontribution to the disappearing-track candidates is estimated using techniquesthat do not rely on the MC simulation. Each of background tracks shows a distinctive pT spectrum; the track pT is used as the final discriminant. A simultaneous fit
is performed for the extraction of the signal yield using the observed pT spectrum
and templates of background-track pT spectra produced from dedicated control
data samples.
This section introduces data-driven techniques to estimate the pT spectrum foreach background component.
107

108

9.1 B E

Figure 9.1: Origins of high-pT disappearing tracks

9.1 Interacting-hadron tracks
9.1.1 Control sample for interacting-hadron tracks
Figure 9.2 shows an event display of an interacting-hadron track that is reconstructed as a disappearing track. A template of track-pT spectrum of interactinghadron tracks is derived based on the fact that the shape of the pT distribution ofinteracting-hadron tracks is the same as that of non-interacting-hadron tracks. In
the pT range above 15 GeV, where inelastic interactions dominate, the interaction
rate has nearly no dependence on pT [21], which is also confirmed by the detector
simulation as shown in Fig. 9.3.
A pure data control sample of high-pT non-interacting-hadron tracks is obtained as follows:
1. Apply the same kinematic selection requirements as for the signal searchsample.
2. Require the leading track to have NTRT > 25 to ensure that charged particles
penetrate the TRT detector without any interactions.
>
3. Require the leading track to have an associated calorimeter activity: Econe40
T
P
track
7.5 GeV and ∆R<0.4 Eclus
/p
>
0.4.
T
T
P
distributions for/ptrack
Figure 9.4 (a) and (b) show the Econe40
and ∆R<0.4 Eclus
T
T
T
hadronically decaying taus, charged hadrons in QCD multi-jet events, electrons,muons, and decaying charginos, respectively. By applying the selection requirements defined above, a pure data sample of non-interacting-hadron tracks is obtained; the contamination of electrons and muons is estimated to be negligible

9.1 B E

109

Figure 9.2: Event display of a MC event containing an interacting-hadron track.
The upper (middle) figure shows a true (reconstructed) charged hadron interactingwith the TRT detector. The corresponding reconstructed track is indicated by ared solid line.

(< 1%). A validation of this approach is performed using the W → τν MC sample,
since the interacting-hadron track background in the signal search sample predominantly comes from hadronically decaying taus in W+jets events as shown in
Table 8.4. Figure 9.5 shows the pT distribution of isolated hadron tracks in W → τνMC events with the requirements that are applied in the signal search and thecontrol sample, showing no difference of the pT shape between each other.

110

9.1 B E
105
No interaction
Inelastic interaction
Elastic interaction
Decay in flight

104
103
102
10
1
10-1
10
1
10

-1

10

-2

10

-3

10

-4

10

20

30

40

50

60

70

80

90
truth

PT

100
[GeV]

Figure 9.3: True pT distribution of charged pions for each type of interaction. The
ratios with respect to the non-interacting hadrons are also shown in the bottom
figure.

9.1.2 Extraction of pT shape
The interacting-hadron tracks show a steeply falling pT spectrum with a highpT tail, which is ”power low with exponential tail cutoff” like. However, thecontribution of this background to the disappearing-track candidates having pT >100 GeV is negligibly small due to the overlap removal with jets with pT > 45 GeV.
In order to develop a data-driven pT spectrum of the high-pT interacting-hadron
tracks, an ansatz functional formf (x) =

(x + a0 )a1,
xa2 +a3 ln(x)(9.1)

is fitted to the pT spectrum of the control data sample of the high-pT non-interactinghadron tracks, where x ≡ ptrack
and ai (i = 0, 1, 2) are fit parameters. Figure 9.6
Tshows the track pT distribution and the pT shape derived by a maximum likelihood
fit. The data is well-described by this functional form; χ2 per degree of freedom of
25.2/50 is calculated from the difference between the data and the best-fit form.
The resulting parameters and the 3 × 3 covariance matrix are given as 7.45 75.9ai = 
 1.16

9.40

±
±
±
±


0.20 

0.76 
,
0.63 

0.08

(9.2)

111

10

Tracks (a.u.)Tracks (a.u.)

9.1 B E

Hadronic τ
Hadron tracks
Electron tracks
Muon tracks
Chargino

1

10

10-1

10-1

10-2

10-2

10-3

10-3

10-4

0

0.5

1

1.5

2

2.5

∑

∆ R<0.4(a)P

Hadronic τ
Hadron tracks
Electron tracks
Muon tracks
Chargino

1

10-4

3

0

Tracks (a.u.)

10

T

clus
track
∆R<0.4 ET /pT

Figure 9.4: The

5

/p
Eclus
T

15

20
25
ETcone40 [GeV](b) Econe40
TPclus track
and Econe40
distributions.
∆R<0.4 ET /pT
T10
Interacting hadron tracks

1
10-1

Non-Interacting hadron tracks

10-2
10-3
10-4

Int / Non-Int

10-5
2.5
2
1.5
1
0.5
0

20

30

40

50

60

70
80
Track pT [GeV]

Figure 9.5: The pT distribution of hadron tracks for W → τν MC samples with therequirement for the interacting-hadron tracks and non-interacting-hadron tracks.

9.1 B E

Tracks / GeV

112

105
104
103

s = 8 TeV,

102a(x+a ) 1/x∫ L dt = 20.3 fb-1

a2+a3ln(x)010a0 = 7.45 ± 0.20
a1 = 75.9 ± 0.76
a2 = 1.16 ± 0.63
a3 = 9.40 ± 0.081
10-1

Significance

10-2
100

1000

100

200 300
1000
Track p [GeV]

2
1
0
-1
-2

20

30 40

T

Figure 9.6: The pT distribution of hadron track background control sample. The
data and the fitted shape are shown by solid circles and a line with a error band,
respectively. The significance of the data-model difference on a bin-by-bin basis isalso shown at the bottom of the figure.

9.2 B E

0


−2
3.84 × 10 0
1.06 × 10−2
cov(ai , a j ) =  1

−2
 2 −6.97 × 10
3
1.22 × 10−3

113
1

2

1.06 × 10−2 −6.97 × 10−2
5.79 × 10−1
2.19 × 10−1
2.19 × 10−1
3.97 × 10−1
−2
3.17 × 10−1.61 × 10−2


3 

1.22 × 10−3 
3.17 × 10−2  . (9.3)
−1.61 × 10−2 
5.98 × 10−3

The values of the correlation coefficients between the parameters, ρi j (≡ cov(ai , a j )/σi σ j ,where σi is the error on ai ), are also given as


0
1
2
3 



1.000 0.071 −0.565
0.080  0
0.071 1.000
0.456
0.539  .
ρij =  1
1.000 −0.330 
 2 −0.565 0.456

3
0.080 0.539 −0.330
1.000

(9.4)

9.2 Leptons failing to satisfy identification criteria
A small fraction of charged leptons (` ≡ e or µ) lose much of their momenta in
the ID due to scattering by material or large bremsstrahlung. Such leptons areunlikely to be correctly identified and may be classified as disappearing tracks.
The contribution of unidentified electron tracks in the high-pT region is negligibly small because they are reconstructed as jets and are rejected by resolving
object overlaps. The effect of bremsstrahlung for muons is considerably small
compared to that for electrons, but tiny detection inefficiencies lead to a fractional
contribution to the background in the high-pT region of the candidate tracks. Thesemuon tracks can not be reconstructed as any other objects, therefore they can not
be rejected by resolving object overlaps.
In order to estimate the track-pT spectrum of lepton tracks, a control data
sample is defined by adopting the kinematic selection identical to those for thesignal search sample1 , while requiring one lepton that fulfills both its identification
criteria and the isolated track selection criteria. The pT spectrum of true leptons,
without any identification requirements, is obtained by applying a correction for
the identification efficiency. The pT distribution of lepton background tracks isthen estimated by multiplying this distribution by the probability (Pdis) of failing`to satisfy the lepton identification criteria (hence being retained in the signal searchsample) and passing the disappearing-track selection criteria. To sum up, thetrack-pT spectrum of lepton background tracks is estimated with the following
equation;1

In order to mimic Emissin the signal search sample, muons are treated like ν in the calculation of
T

Emiss
.
T

114

9.2 B E

N`CR pT
 × PdispT = 
p
,
η,
φ
,
T
`
 pT , η, φ




Pdis
≡
1
−

p
,
η,
φ
× P`NTRT <5 pT ,
T
`


N`SR

(9.5)
(9.6)

SR(CR)

electron η

where N`
, P`N <5 , and  are the number of events in the signal search (control)
TRT
sample, the probability of having NTRT < 5, and the identification efficiency, respectively. Figure 9.7 shows the identification efficiencies of electrons and muons,and the average efficiencies for electrons and muons are ∼ 70 % and ∼ 94 %, respectively. According to the MC simulation, the purity of electrons and muons ineach control samples is close to 100% with these requirements.
0.57
0.70

0.60
0.72

0.61
0.75

0.62
0.76

0.63
0.77

0.64
0.78

0.65
0.79

2

0.60

0.64

0.66

0.69

0.71

0.73

0.74

1.5

0.63
0.52
0.64

0.65
0.57
0.67

0.68
0.61
0.70

0.71
0.64
0.74

0.74
0.66
0.76

0.75
0.68
0.77

0.76
0.69
0.78

1

0.69

0.71

0.75

0.78

0.80

0.81

0.82

0.73

0.75

0.79

0.82

0.84

0.84

0.85

0.5

0.75

0.76

0.80

0.83

0.85

0.84

0.85

0

0.66
0.67

0.68
0.69

0.71
0.72

0.74
0.75

0.76
0.77

0.75
0.76

0.76
0.76

0.75

0.76

0.80

0.83

0.85

0.84

0.85

0.72

0.75

0.78

0.81

0.83

0.84

0.84

-0.5
-1
-1.5
-2
15

0.68

0.71

0.74

0.78

0.80

0.80

0.81

0.64
0.51
0.62
0.60

0.67
0.56
0.65
0.63

0.70
0.61
0.68
0.66

0.73
0.64
0.71
0.69

0.76
0.66
0.73
0.71

0.77
0.68
0.75
0.73

0.78
0.70
0.76
0.74

0.70

0.72

0.75

0.76

0.77

0.78

0.79

0.55

0.57

0.59

0.60

0.61

0.62

0.63

20

25

30

35

40
45
50
electron p [GeV]

0.85
0.8
0.75

0.96

BEE

0.94

0.94

0.95

0.96

0.96

0.96

0.96

EndCap Small

0.94

0.94

0.95

0.95

0.96

0.95

0.95

EndCap Large

0.94

0.94

0.95

0.95

0.96

0.96

0.95

Transition

0.92

0.93

0.93

0.94

0.94

0.94

0.94

Feet

0.93

0.93

0.93

0.94

0.94

0.95

0.94

Overlap

0.92

0.93

0.93

0.94

0.94

0.94

0.94

Barrel Small

0.92

0.92

0.93

0.93

0.94

0.94

0.94

0.925

Barrel Large

0.93

0.93

0.94

0.94

0.95

0.95

0.94

0.92

0.955
0.95
0.945

0.7
0.65

0.94
0.935

0.6
0.55

15

20

25

30

35

T

(a) Electron

0.93

40
45
50
muon p [GeV]T(b) Muon

Figure 9.7: Two-dimensional identification efficiency map for electrons (a) andmuons (b).
The following sections give details on the measurements of Pdis.
`

9.2.1 Probability of identifying a lepton as a disappearing track, is measured
The probability of identifying a lepton as a disappearing track, Pdis`by the “tag and probe” method using Z → `` events. The object definitions for tag
and probe leptons and the event selection criteria to enhance Z → `` events are
summarized below. Tag leptons are required to be well isolated from jets and tofulfill the lepton identification criteria.
Definition of tag electrons• pT > 25 GeV and | η |< 2.47.
• Not pointing to the crack region (1.37 <| η |< 1.52).
• Satisfying the Tight identification criteria.

9.2 B E

115• pcone40/pT < 0.1, | d0 | < 1 mm, and | z0 | < 2 mm.
T
Definition of tag muons• Satisfying the combined muon criteria.
• pT > 25 GeV and | η |< 2.4.
• pcone40/pT < 0.1.
T
Definition of probe leptons• Reconstructed tracks that fulfill the requirements on the high-pT isolated
tracks described in Sec. 8.2.5.
Event selection for Z → `` events• Trigger (Electron): EF e24vhi medium1 or EF e60 medium1.
• Trigger (Muon): EF mu24i tight or EF mu36 tight.
• Jet quality requirements.
• At least one reconstructed primary vertex with a minimum of five tracks
with pT > 0.4 GeV.
• No identified muons (electrons) for the Z → ee (Z → µµ) channel.
• At least one tag lepton and one isolated track.
• |m`` − mZ | < 5 GeV, where m`` and mZ are the reconstructed invariant masscalculated using the tag lepton and the probe track momenta (~pprobe ) and themass of Z boson, respectively.
probe

Figure 9.8 and 9.9 show the mee and mµµ distributions for each pTrange incomparison with the prediction based on Z → `` MC events, showing a reasonableagreement between each other in the region |m`` − mZ | < 5 GeV.
µSince Pdis gives a very tiny value for muons, the interacting-hadron tracks(predominantly in W → µν + jets events) also contribute to the ensemble of probe
tracks. Therefore, in order to suppress the interacting-hadron track contamination and ensure a pure sample of probe muons, an additional requirement of/pT < 0.18 is applied.
Econe40
T
The probability Pdis
is finally given by the fraction of events in which the`probe lepton passes the disappearing-track selection criteria. Figure 9.10 shows
the resulting probability of identifying an electron as a disappearing track as afunction of pT , showing a good agreement with the MC prediction. In the muonchannel, there are no sufficient statistics of MC events to estimate the probability

116

9.3 B E

for probe muons due to its tiny probability, therefore, it is estimated only using
data as shown in Fig. 9.11. The probability ranges between 10−2 –10−4 for electronsand 10−4 –10−5 for muons. Since the unidentified muon background in the highµpT region is led by the detection inefficiencies and Pdis is largely independent ofµpT , one common value of Pdis is used for the pT region above 50 GeV, which isvalidated by checking single-muon MC samples in which its truth pT ranges up to
1000 GeV.

9.2.2

pT spectrum of lepton background tracks

Figure 9.12 shows the resulting pT spectrum of lepton background tracks derived
by the data-driven method previously described. The systematic uncertainties,
which are given in Sec. 9.2.3, are incorporated in the pT spectrum. The numbersof electron and muon background tracks in the signal search sample are estimatedto be 883 ± 35 and 22 ± 8, respectively. The complicated pT spectrum of muon
background track is caused by the kinematic selection in the signal search sample.
In this analysis, leading jet and Emissare required to be back-to-back in the φ-plane,Tand the unidentified muons make the large component of ETmiss . Therefore, the pT
spectrum of muon background track is susceptible to the leading jet pT spectrum,and resulting pT spectrum has a sharp edge near the threshold of the requirementon the leading jet pT .

9.2.3 Uncertainty of the lepton background yield
The systematic uncertainty mainly arises due to the rejection of the contaminationof interacting-hadron tracks.
In the electron channel, the uncertainty is evaluated by comparing with theprobability Pedis derived by changing the invariant mass window by 2.5 GeV thatgive two times larger or smaller contamination of QCD multi-jet events and W+jets
processes. Figure 9.13 shows Pedis with three different invariant-mass selection
requirements as a function of the probe electron track pT , showing no significant
difference and an agreement within the statistical uncertainty of 4%. Therefore, an
overall uncertainty of ± 4% is assigned on the electron background yield.
In the muon channel, the uncertainty is evaluated by varying the cut value onµ/pT by ± 0.4. Figure 9.14 shows Pdis with different selection requirements
Econe40
Tof Econe40/pT as a function of pT . The purity of muons in the probe tracks withThigher pT , which is of particular importance in this search, is quite high and thesystematic effect in the high-pT region of the estimated muon track spectrum issmall.

9.3 B E

117

3

Events / GeV

Events / GeV

×10
160
140
120
100

70000
60000
50000
40000

80
30000

60
DATA, s = 8 TeV,

∫ L dt = 20.3 fb-1∫ L dt = 20.3 fb-1

MC, Z → ee

10000

MC, Z → ee

20

DATA, s = 8 TeV,

20000

40

DATA / MCDATA / MC

0
1.4
1.2
1
0.8
0.6

82

84

86

88

90

probe

94

96

98 100Mee [GeV]

82

45000
40000
35000
30000
25000

88

90

92

94

96

98 100Mee [GeV]< 40 GeV

22000
20000
18000
16000
14000
12000
10000

15000

DATA, s = 8 TeV,

10000

∫ L dt = 20.3 fb

8000
-1

DATA, s = 8 TeV,

6000
4000

MC, Z → ee

5000

∫ L dt = 20.3 fb-1

MC, Z → ee

2000
0

DATA / MC

0

DATA / MC86(b) 35 GeV < pT

20000

1.4
1.2
1
0.8
0.6

82

84

86

88

90

92

probe

94

96

1.4
1.2
1
0.8
0.6

98 100Mee [GeV]

82

10000

8000

6000

84

86

88

90

92

probe

< 35 GeV(d) 25 GeV < pT
Events / GeV(c) 30 GeV < pT
Events / GeV

84

probe

> 40 GeV

Events / GeV

Events / GeV(a) pT

92

1.4
1.2
1
0.8
0.6

94

96

98 100Mee [GeV]< 30 GeV

4500
4000
3500
3000
2500
2000

4000
DATA, s = 8 TeV,2000

∫ L dt = 20.3 fb

1500

-1

∫ L dt = 20.3 fb-1

MC, Z → ee

5000DATA / MC

0

DATA / MC

DATA, s = 8 TeV,

1000

MC, Z → ee

1.4
1.2
1
0.8
0.6

82

84

86

88

90

92

probe

(e) 20 GeV < pT

9496

98 100Mee [GeV]< 25 GeV

1.4
1.2
1
0.8
0.6

82

84

86

88

90

92

probe

(f) 15 GeV < pT

9496

98 100Mee [GeV]< 20 GeV

Figure 9.8: The mee distributions for various pT -ranges of the probe electron. The
MC events are normalized to the observed number of events.

118

9.3 B E

3

3

DATA, s = 8 TeV,

700
600

∫ L dt = 20.3 fb

×10

Events / GeV

Events / GeV

×10
800

-1

MC, Z → µ µ

500

250
DATA, s = 8 TeV,

200

∫ L dt = 20.3 fb-1

MC, Z → µ µ

150

400
300

100

200
50
100
0

DATA / MCDATA / MC

0
1.4
1.2
1
0.8
0.6

82

84

86

88

90

probe

(a) pT

92

94

96

1.4
1.2
1
0.8
0.6

98 100Mµµ [GeV]

82

84

86

88

90

92

probe

> 40 GeV(b) 35 GeV < pT

9496

98 100Mµµ [GeV]< 40 GeV

3

Events / GeV

Events / GeV

×10
160
140

DATA, s = 8 TeV,

∫ L dt = 20.3 fb-1

120
MC, Z → µ µ

100

80000
70000

40000

60

30000

40

20000

20

10000
0

DATA / MCDATA / MC

-1

50000

80

1.4
1.2
1
0.8
0.6

82

84

86

88

90

92

probe

30000
DATA, s = 8 TeV,

∫ L dt = 20.3 fb

94

96

1.4
1.2
1
0.8
0.6

98 100Mµµ [GeV]

82

-1

25000
MC, Z → µ µ

20000

84

86

88

90

92

probe

< 35 GeV(d) 25 GeV < pT
Events / GeV(c) 30 GeV < pT
Events / GeV∫ L dt = 20.3 fb

MC, Z → µ µ

0

94

96

98 100Mµµ [GeV]< 30 GeV

14000
DATA, s = 8 TeV,

12000
10000

15000

∫ L dt = 20.3 fb-1

MC, Z → µ µ

8000
6000

10000

4000

5000

2000
0

DATA / MC

0

DATA / MC

DATA, s = 8 TeV,

60000

1.4
1.2
1
0.8
0.6

82

84

86

88

90

92

probe

(e) 20 GeV < pT

9496

98 100Mµµ [GeV]< 25 GeV

1.4
1.2
1
0.8
0.6

82

84

86

88

90

92

probe

(f) 15 GeV < pT

9496

98 100Mµµ [GeV]< 20 GeV

Figure 9.9: The mµµ distributions for various pT -ranges of the probe muon. The
MC events are normalized to the observed number of events.

Pedis

9.3 B E

119

10-1

10-2

-3

10

DATA
MC (Z → ee)

10-4

s = 8 TeV,
-5

∫Ldt = 20.3 fb-1

DATA / MC

10

2
1.5
1
0.5
0

15

20

25

30

35

40
45
50
Probe track pT [GeV]Pµdis

Figure 9.10: Probability of failing identification and satisfying disappearing-track
selection criteria for electrons. The results derived by the tag-and-probe method
based on the data and Z → ee MC events are indicated by black and red circles,
respectively.

10-1
10-2
s = 8 TeV,

10-3

∫ L dt = 20.3 fb

-1

10-4
10-5
10-6
10-7

20

30

40

50

60

70
80
90
100
Probe track p [GeV]T

Figure 9.11: Probability of failing identification and satisfying disappearing-track
selection criteria for muons. The shaded red band shows the systematic uncertainty
related to the rejection of the interacting-hadron track contamination.

9.3 B E

Tracks / GeV

120

104
103

s = 8TeV,∫Ldt = 20.3 fb-1

Electron track background102

Muon track background

10
1
10-1
10-2
10-3

20 30 40

200

100

1000
Track p [GeV]
T

Pedis

Figure 9.12: Estimated pT distribution of residual lepton background tracks. The
systematic uncertainties are incorporated in the pT spectrum and indicated byhatched error bands.

10-1
s = 8 TeV,

10-2

∫ L dt = 20.3 fb

-1

10-3
10-4

|M - Mz| < 2.5 GeVll|M - Mz| < 5.0 GeVll|M - Mz| < 7.5 GeV
ll

Ratio

10-5
2
1.5
1
0.5
0

15

Probe track p [GeV]
T

20

25

30

35

40
45
50
Probe track pT [GeV]

Figure 9.13: Probability Pedis derived by three different invariant-mass selection
requirements.

Pµdis

9.3 B E

121

10-2
ETcone40/p < 0.14
T
ETcone40/p < 0.18
T
ETcone40/p < 0.22

-3

10

T

-4

10

s = 8 TeV,

∫ L dt = 20.3 fb

-1

10-5

Ratio

10-6
2
1.5
1
0.5
0

Probe track p [GeV]
T

20

30

40

50

60

70

80
90
100
Probe track pT [GeV]µ/pT by ±Figure 9.14: Probability Pdis derived by varying the cut value on Econe40
T
0.4.

9.3 Tracks with mismeasured pT
The background contribution to disappearing-track candidates with pT > 100 GeV
originates primarily from pT -mismeasured tracks. A high density of silicon hits,
hadronic interactions and scattering can lead to combinations of wrong spacepoints in the procedure of track-seed finding or outward-extension of trajectories, resulting in anomalously high values of pT especially for short-length tracks.
The application of Re-tracking helps to increase the acceptance for the decaying
chargino tracks at low radius but also leads to a non-negligible contribution ofpT -mismeasured tracks in the signal search sample. Figure 9.15 shows a MC event
display of a pT -mismeasured track formed by a hadronic interaction in the SCT
detector. The pT -mismeasured tracks could have anomalously high values of pT
and could mimic signal tracks, therefore, the understanding of their properties
and a fair estimation of their background contribution based on the data are ofparticular importance.
Figure 9.16 shows the d0 distributions for pT -mismeasured tracks obtained by
QCD multi-jet MC events and decaying chargino tracks. The pT -mismeasured
tracks in QCD multi-jet events most likely originate from a wrong combinationof space-points, therefore their d0 is broadly distributed. On the other hand, thehigh-pT chargino tracks have a good pointing resolution and cluster around zeroin the d0 distribution. Therefore, by applying the tight requirement on the impact
parameter for the candidate tracks as described in Sec. 8.2.5, the contribution ofpT -mismeasured tracks can be significantly suppressed. Furthermore, pT and d0
of pT -mismeasured tracks are (most likely) randomly given since they arise fromrandomly combined space-points. On this assumption, the sample of tracks having
large d0 is used as a pure control sample of pT -mismeasured tracks.

122

9.3 B E

Figure 9.15: Event display of a MC event containing a pT -mismeasured track. The
figure on the left shows true charged tracks with pT > 1 GeV. The corresponding
reconstructed track is indicated by a red solid line in the middle figure.

9.3.1 Control data sample of pT -mismeasured tracks
The pure control data sample of pT -mismeasured tracks is obtained by inverting
the requirement on the impact parameters as follows:
1. Apply the same kinematic selection requirements as for the signal searchsample.
2. Apply an inverted cut on the impact parameter: 1.0 mm < |d0 | < 10.0 mm.
3. Apply the disappearing track selection criteria except for the d0 requirement.
4. Require that the track
have an associated calorimeter activity:
P must notclus track< 0.4. This removes the contamina/p
<
7.5
GeV
and
E
Econe40∆R<0.4 TTTtion of interacting-hadron tracks.
One important point is to ensure that the pT shape of pT -mismeasured tracks
does not alter by the requirement on d0 . Figure 9.17 shows the pT distributionof pT -mismeasured tracks for small (close to the region |d0 | < 0.1 mm used in
the candidate track selection) and large d0 values in the data with the kinematic

9.3 B E

123

Tracks (a.u.)10
p -mismeasured tracks
T

Chargino tracks ( τ∼± = 0.2 ns )

1

χ

1

10-1
10-2
10-3
10-4-1.5

-1

-0.5

0

0.5

1
1.5
d0 w.r.t PV [mm]Tracks / GeV

Figure 9.16: The d0 distribution for pT -mismeasured (black) and decaying chargino(red) tracks. The sample of pT -mismeasured tracks is obtained in the QCD multi-jet
MC events by requiring that the reconstructed pT must have a significantly large
value compared to that of the corresponding true charged particle.

1
0.5 mm < |d | < 1.0mm
010-1

1.0 mm < |d | < 10.0mm
0

-2

10

10-3
10-4

Ratio

10-5

2.5
2
1.5
1
0.5
0

20

30 40

100

200 300
1000
Track p [GeV]T

Figure 9.17: The pT distribution of pT -mismeasured tracks in the data with therequirements 0.5 < |d0 | < 1.0 mm and 1.0 < |d0 | < 10 mm. The same kinematic
selection requirements as for the signal search sample are applied.

124

9.3 B E

and disappearing-track selection requirements, showing no impact-parameter-cut
dependence on the pT shape.
The pT shape of the pT -mismeasured background tracks is then modeled byfitting an ansatz functional formf (x) = x−b0 ,(9.7)

to the pT spectrum observed in the pT -mismeasured track control sample, wherex ≡ ptrack
and b0 are fit parameters. Figure 9.18 shows the track-pT distribution andTthe pT shape derived by the maximum likelihood fit. The resulting parameter isb0 = 1.78 ± 0.05.

(9.8)

Tracks / GeV

The data is well-described by this functional form; χ2 per degree of freedom of
55.2/60 is calculated from the difference between the data and the best-fit form.

104
103
102
s = 8 TeV,

10

∫ L dt = 20.3 fb

-1

1
10-1
10-2

-b

x 0, b0 = 1.78 ± 0.05

10-3

Significance

10-4
100

1000

100

200 300
1000
Track p [GeV]

2
1
0
-1
-2

20

30 40

T

Figure 9.18: The pT distribution of pT -mismeasured tracks in the control sample.
The data and the fitted shape are shown by solid circles and a line, respectively.
The significance of the data-model difference on a bin-by-bin basis is also shownat the bottom of the figure.

9.3.2 Further check on the impact-parameter dependence
The pT -mismeasured tracks remaining in the signal search sample have valuesof |d0 | < 0.1 mm and potentially associated to hits arising from charged particles

9.3 B E

125

produced from hard-collision vertices. Therefore, validating the principle of “noimpact-parameter-cut dependence” using tracks |d0 | < 0.1 mm is important tojustify the background estimation technique. The validation is then performed bychecking pT distributions of pT -mismeasured tracks in a QCD multi-jet enrichedsample obtained by applying the following requirements:• Dataset: QCD multi-jet enriched sample collected by single-jet triggers.
• Jet quality requirements.
• At least one reconstructed primary vertex with a minimum of five tracks
with pT > 0.4 GeV.
• No identified electrons and muons with pT > 10 GeV.
• At least one jet with pT > 90 GeV.
• Emiss< 90 GeV, which is orthogonal to the event selection in the signal searchTsample.
• The high-pT isolated track selection with the requirements of ptrack> 50 GeV
T
and |d0 | < 0.1 mm.
The contamination from interacting-hadron and lepton tracks is expected to bevery small in this sample.
Figure 9.19 shows the resulting track-pT distribution in comparison with one
obtained by requiring 1 mm < |d0 | < 10 mm. The distributions are also fittedwith the functional form given in Eq. 9.7; the fitted parameters are summarizedin Table 9.1. Two pT shapes are consistent within the statical uncertainties and no
impact-parameter-cut dependence is observed.
Parameterb0|d0 | < 0.1 mm
1.62 ± 0.07

1 mm < |d0 | < 10 mm
1.69 ± 0.01

Table 9.1: Fitted b0 parameters derived in the QCD multi-jet enriched sample.

9.3.3 Systematic uncertainty on the track-pT shape
Figure 9.20 shows the d0 distribution of the pT -mismeasured tracks in W → `νMC events that dominate in the signal search sample. A large fraction of thesepT -mismeasured tracks have d0 ∼ 0, indicating that pT of these tracks is wrongly
determined most likely when extending their tracks outwards and combining
incorrect space-points, while the control sample is expected to be dominated bytracks whose seeds are wrongly formed. Therefore, in order to take accountof possible biases induced by the d0 requirement, an additional uncertainty is
assigned by taking the difference between the b0 parameter given in Eq. 9.8 and9.3 B E

Tracks / GeV

126

1|d | < 0.1mm
010-1

1.0mm < |d | < 10.0mm
0

-2

10

10-3
10-4

Ratio

10-5
2.5
2
1.5
1
0.5
0

60

100

200

300 400

1000
Track p [GeV]T

Figure 9.19: The pT distributions of pT -mismeasured tracks with |d0 | < 0.1 mm and
1 mm < |d0 | < 10 mm in the QCD multi-jet enriched sample.
the one derived by W → τν MC events. Figure 9.21 shows the pT distributions of
pT -mismeasured tracks in the control sample and W → τν MC background events.
The b0 parameter for W → τν MC events is found to be 1.82 ± 0.07, showing thatthe track-pT shapes of these two samples are consistent with each other within
the statistical uncertainties. The difference of b0 parameter between the control
sample and W → τν MC events is assigned as a systematic uncertainty.

Tracks (a.u.)

9.3 B E

127

1
0.9

W −> µ ν (p >75GeV)
T

0.8
0.7

W −> τ ν (p >75GeV)
T

0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1

-0.05

0

0.05
0.1
d0 w.r.t. PV [mm]Tracks / GeV

Figure 9.20: The d0 distribution of pT -mismeasured tracks that pass the disappearing track selection in W → `ν MC events. The predictions for W → µν and W → τνbackground events are shown by black and red solid circles, respectively.

1
p - mismeasured track control sample
T

10-1

SM MC prediction, |d | < 0.1 mm
0

-2

10

10-3
10-4
s = 8 TeV,

Ratio

10-5

2.5
2
1.5
1
0.5
0

80 100∫ L dt = 20.3 fb

-1

200

300 400

1000
Track p [GeV]T

Figure 9.21: The pT distribution of pT -mismeasured tracks in the control sample
and the SM MC background prediction (W → τν MC events). The error bars andband indicate the statistical uncertainties of each sample.

128

9.3 B E

Chapter 10

Systematic Uncertainties
This section describes the uncertainties of signal normalization and the backgroundtrack shapes to be incorporated in the fit and resulting signal yield.

10.1 Uncertainty on the signal events
The following sources of systematic uncertainties on the signal normalization havebeen considered:• Uncertainties on the theoretical cross-section.
• Uncertainties on the initial and final state radiation (ISR/FSR).
• Uncertainties on the jet energy scale and resolution (JES/JER).
• Uncertainties on the trigger efficiency.
• Uncertainties on the pile-up modeling.
• Uncertainties on the track reconstruction efficiency and pT resolution.
• Uncertainty on the integrated luminosity.
Theoretical uncertainties on the signal cross-section are evaluated by computing the changes in the cross-section when the renormalization and factorization
scales, the choice of PDFs, and the strong coupling constant (αs ) are varied. Varying the scales with factors of 0.5 and 2, the uncertainties on the scales and αs are
estimated. The uncertainty arising from PDFs is evaluated by using the error setsof the PDF set as described in Sec. 4.2.1. Finally, by taking the maximum difference
between 68% CL upper and lower limits on the cross-sections calculated using
CTEQ6.6 and MSTW2008 NLO PDFs.
High–pT jets originating from ISR and FSR alter the signal acceptance. The
uncertainties on ISR and FSR are estimated by varying generator tunes in thesimulation as well as by generator-level studies carried out on samples produced
129

130

10.2 S U

with an additional jet in the matrix-element method using Madgraph5 [103] program and PYTHIA6 program. By adopting the PDF tunes that provide less andmore radiations and taking the maximum deviation from the nominal tune, theuncertainty due to jet radiation is evaluated. The uncertainty arising from thematching of matrix elements with parton showers is evaluated by doubling andhalving the default value of the matching parameter [104]. The resulting changes
are combined in quadrature and yield an uncertainty of 10–17% depending onmχ̃± .
1
The uncertainty on the JES and JER results in a variation of the signal selection efficiency, but does not directly affect the pT distribution of chargino tracks
remaining after the selection cuts. In this analysis, the uncertainty of the overall
normalization arising from JES and JER is assessed according to Ref. [96] . The
due to JES is also taken into account in a coherent way. The
uncertainty on Emiss
Teffect of JER are found to be much smaller than 1% and neglected.
An uncertainty of 4.5% on the trigger efficiency is assigned by taking thedifference between data and MC simulation in a W+jet sample in which W decays
into µ plus νµ , as described in Sec. 8.1.1.
The uncertainty originating from the pile-up modeling in the simulation isevaluated by weighting simulated samples so that the average number of pile-upinteractions is increased or decreased by 10%, which yields a 0.5% uncertainty
on the signal efficiency. The impact on the selection efficiency, arising from theincrease of the noise occupancy in the TRT detector, is found to be negligible.
The modeling of the ID material alters the track reconstruction efficiency andaffects the signal selection efficiency. An uncertainty of 2% on the tracking efficiency due to the material description in the MC simulation, especially for tracks
in the region of |η| < 1.9, is quoted as given in Ref. [100]. No evidence of thedifference of the track-pT resolution between data and MC events is also found bychecking the interacting-hadron tracks especially having NSCT = 2; the impact ofthe pT resolution on the broad distribution of high-pT chargino tracks is negligibly
small.
The uncertainty on the integrated luminosity is estimated to be ±2.8%, as given
in Ref. [66, 67].
Contributions of each systematic uncertainty in the signal expectations are
summarized in Table 10.1.

10.2 Uncertainty on the background events
Systematic uncertainties on the background pT shapes and normalizations arisingfrom statistical uncertainties of the control data samples and uncertainties on the
lepton identification efficiencies are considered in deriving the results1 . In order
to incorporate these uncertainties, the shape parameters are adopted as nuisance1

Details on each systematic uncertainty is described in Sec. 9

10.2 S U

Source(Theoretical uncertainty)
Cross-section(Uncertainties on
the acceptance)
ISR/FSR
JES/JER
Trigger efficiency
Pile-up modeling
Track reconstruction efficiency
Luminosity
Sub-total

131

80.5

98.9

Chargino mass ( GeV)
124.5 149.8 201.0 225.6

250.0

274.4

±8.2

±7.6

±7.1

±6.8

±6.4

±6.4

±6.6

±6.5

±12.6

±13.2

±15.0

±14.5

±14.5

±15.0

±16.0

±16.2

+3.6
−5.5

+4.9
−3.9

+5.8
−3.7

+3.2
−3.9

+3.8
−3.9

+3.0
−2.3

+2.9
−4.7

+3.8
−1.3

±16.3

±17.6

±17.6

±4.5 %±0.5 %±2.0 %

±14.9

±15.2

±17.1

±2.8 %±16.1 ±16.1

Table 10.1: Summary of systematic uncertainties [%] in the expectation of signal
events.

Source(Theoretical uncertainty)
Cross-section(Uncertainties on
the acceptance)
ISR/FSR
JES/JER
Trigger efficiency
Pile-up modeling
Track reconstruction efficiency
Luminosity
Sub-total

300.0

Chargino mass ( GeV)
351.1 399.1 449.5

499.6

±6.8

±7.0

±7.6

±8.0

±8.2

±16.4

±16.8

±17.9

±18.1

±17.8

+2.3
−6.0

+2.2
−3.3

+5.7
−1.5

+2.1
−2.1

+3.6
−2.0

±4.5 %±0.5 %±2.0 %

±18.4

±18.0

±2.8 %±19.6 ±19.1±19.0

Table 10.2: Summary of systematic uncertainties [%] in the expectation of signal
events.

132

10.2 S U

parameters in the fit and the correlations between them are taken into account.
For the pT shape of pT -mismeasured tracks, in order to take account of possible
biases induced by the d0 requirement, an additional uncertainty is assigned bytaking the difference of the fit result for pT -mismeasured track control samples
and SM background events as discussed in 9.3.3. Details of the statistical analysis
procedure are given in the next section.

Chapter 11

Extraction of Signal Yield
The chargino tracks have high pT , and all background tracks show distinctive
pT spectra. The signal hypothesis with given values of mχ̃± and τχ̃± is there1
1
fore tested based on an extended maximum likelihood fit to the pT spectrum of
the disappearing-track candidates. The likelihood function for the track pT consists of one probability density function for the signal and four for the different
background components. If multiple disappearing-track candidates appear in an
event, the candidate with the highest pT is used to define the likelihood function.
In the fit, the yields of the signal, interacting-hadron, and pT -mismeasured tracks
are left free. The yields of electron and muon background tracks are constrained
to their estimated values within the systematic uncertainties. The effects of systematic uncertainties on the yields and the parameters describing the pT shapesof the background tracks are also incorporated into the likelihood function. The
following sections describe the details of the fit procedure.

11.1 Unbinned extended maximum likelihood fit
In order to evaluate how well the observed data agree with a given signal model,
a statistical test is performed based on an extended maximum likelihood. The
likelihood function for the track pT (L) in a sample of observed events (nobs ) isdefined as~h , θ~m )L(ns , nh , ne , nµ , nm ) ≡ Lshape (pT ; ns , nh , ne , nµ , nm , θ~h , θ
~m ),× Lsys (αs , αe , αµ , θ
ns ≡
ne ≡
nµ ≡

exp
µ(1 + αs )ns = µLσs s (1 + αs ),exp(1 + αe )ne ,exp(1 + αµ )nµ ,

where
133

(11.1)(11.2)(11.3)(11.4)

134
(exp)

ns

11.2 E  S Y: the (expected) number of signal events for a given model,(exp)

ne,µ : the (expected) number of electron and muon track events,
nh,m : the number of interacting-hadron and pT -mismeasured track events,
αs,e,µ : the nuisance parameter representing the overall normalization of the signal,
electron, and muon track yields,~h : the nuisance parameters representing the pT spectrum of the interactingθhadron tracks (~a = (a0 , a1 , a2 , a3 )),~m : the nuisance parameter representing the pT spectrum of the pT -mismeasured
θtracks (~b = (b0 )),µ : the signal strength,
σs : the signal cross-section,
s : the signal selection efficiency,
L: the integrated luminosity (20.3fb−1 ).
The formulations of Lshape and Lsys are given as

Lshape =nobs~h ) + ne Fe (pT ; αe ) + nµ Fµ (pT ; αµ ) + nm Fm (pT ; θ~m )Y
ns Fs (pT ; αs ) + nh Fh (pT ; θ

ns + nh + ne + nµ + nm,

(11.5)
Lsys =~h ; Ch ) × M(θ~m ; Cm ),N(αs ; ∆2s ) × N(αe ; ∆2e ) × N(αµ ; ∆2µ ) × M(θwhere Fs , Fh , Fe , Fµ and Fm are the probability density functions of signal,
interacting-hadron, electron, muon, and pT -mismeasured tracks. The functionsof N and M represent the normal distribution and the multivariate normal distribution in which the variance ∆2s(e,µ) represents the systematic uncertainty ofthe signal (electron and muon background) normalization that is summarized inSec. 10, respectively. The resulting probability densities of the signal and background components on the track pT are shown in Fig. 11.1. The selection efficiency
and the expected number of events as a function of τχ̃± for various signal points
1
are also shown in Fig. 11.2. The chargino pT spectra for various values of lifetimeand mass are shown in Fig. 11.3.

11.2 Validation of the signal extraction method
In order to validate the fit procedure and performance, an ensemble of pseudoexperiments of observing 3256 events with any compositions of signal and background events is prepared by using the model described in Eq. 11.1 with various(11.6)

Tracks / GeV

11.2 E  S Y

135

10Interacting hadronp -mismeasured track

1

T

ElectronMuonmχ∼± = 200 GeV, τχ∼± = 0.2 ns

10-1

1

1

10-2
10-3
10-4
10-5

20

30 40

200 300
1000
Track p [GeV]

100

T

10

103

1

10-2

τχ∼± [ns]101τχ∼± [ns]Figure 11.1: Probability densities of the signal model with mχ̃± = 200 GeV and τχ̃±1
1
= 0.2 ns and all background components on the track pT .

102
10-3
1

10
1
1

10-4

10-1
-5

10

10-1
100

150

200

250

300

350

400

450
mχ∼± [GeV]1(a) Efficiency (s )

10-2

10-1
100

150

200

250

300

350

400

450
mχ∼± [GeV]

10-3

1

exp

(b) Expected number of events (ns )Figure 11.2: Selection efficiency (a) and the expected number of signal events (b)as a function of the chargino lifetime and chargino mass.

10-1τχ∼± = 0.2 nsτχ∼1± = 0.6 ns
τχ∼1± = 1.0 nsτχ∼±1 = 10 ns

10-2

1

-3

Tracks / GeV

11.3 E  S Y

Tracks / GeV

136

10-1
m∼χ± = 100 GeV
11m∼χ± = 300 GeV
1

-3

10

10

10-4

10-4

10-5

10-5

10-6 50m∼χ± = 200 GeV

10-2

100

200

300

1000
Track p [GeV]

10-6 60

100

200

300

T

1000
Track p [GeV]T(a) Chargino pT spectra for mχ̃±1 = 200 GeV and (b) Chargino pT spectra for τχ̃±1 = 0.2 ns and varivarious lifetime values
ous masses

Figure 11.3: Chargino pT spectra for various values of lifetime (a) and mass (b).
The distributions are normalized to unity.
values of parameters. In this analysis, the yields of the electron and muon track
backgrounds are estimated with finite systematic uncertainties. Therefore, thenormalization of the electron and muon track backgrounds are fixed to their estimated values in this closure test, while the yields of the signal, interacting-hadron,and pT -mismeasured tracks are left free. Figure 11.4 and 11.5 show fitted values ofµ and the fraction of pT -mismeasured tracks (ffitted) as a function of ftruemm defined by
ftrue
≡
m

nm
.
(exp)
(exp)
nobs − (ne+ nµ )(11.7)The signal strengths obtained by the pseudo-experiments with a signal sample(mχ̃± = 150 GeV, τχ̃± = 0.2 ns) peak around unity for any values of ftrue
m in Fig. 11.5
11(b). The fitted value ffitted
is determined within an uncertainty σ(fm ) ∼ 0.03 asmshown in Fig 11.5 (c). In the case of no signal injection, the signal strengths peak
fittedaround zero for any values of ftrue
m as shown in Fig. 11.4 (b). The fitted value fm
is determined within an uncertainty σ(fm ) ∼ 0.03 as shown in Fig. 11.4 (c), whichis consistent with the error expected from the statistical uncertainty. These results
indicate that the fit procedure works as expected, and it can correctly extract theyields of the signal, interacting-hadron, and pT -mismeasured tracks.

11.3 Null-hypothesis testing in the high-pT region
The high-pT signal region provides significant signal-to-background ratios for any
signal models, therefore, the agreement between the observation and the prediction on the null hypothesis in the high-pT region is assessed with p-values incounting experiments; higher-pT regions give sensitivities on decaying charged
particles with shorter lifetimes because large boost factors are required to fulfill the track selection requirements for such signals. Table 11.1 summarizes thefm

fitted

11.3 E  S Y

137

103

1
0.8

102
0.6
0.4

10

0.2
0
0

0.2

0.4

0.6

0.8

1
true
fm

1

µ fitted

fitted(a) ftrue
m vs fm

103

2
1.8
1.6
1.4

102

1.2
1
0.8
10

0.6
0.4
0.2
0
0

0.2

0.4

0.6

0.8

1
true
fm

1

Events (a.u.)(b) ftrue
m vs µ10
f m(true) = 0.00
f m(true) = 0.25
f m(true) = 0.50
f m(true) = 0.75
f m(true) = 1.00

1
10-1
10-2
10-3
10-4
10-5-0.3

-0.2

-0.1

0

0.1

0.2
0.3
fitted
true
fm
- fm

fitted(c) ftruem - fm(a) and µ (b) as a function of ftrue
Figure 11.4: The ffitted
m derived by each pseudom
fitted , are also
experiment with the only backgrounds. The pull distributions, ftrue
m - fmshown.

11.3 E  S Yfm

output

138

1
0.8
102
0.6
0.4

10

0.2
0
0

0.2

0.4

0.6

0.8

1
input
fm

1

f ittedµ fitted(a) ftrue
m vs fm
3.5

102

3
2.5
2

10

1.5
1
1

0.5
0
0

0.2

0.4

0.6

0.8

1
true
fm

Events (a.u.)(b) ftrue
m vs µ10
f m(true) = 0.00
f m(true) = 0.25
f m(true) = 0.50
f m(true) = 0.75
f m(true) = 1.00

1
10-1
10-2
10-3
10-4
10-5-0.3

-0.2

-0.1

0

0.1

0.2
0.3
fitted
true
fm
- fm

f itted(c) ftruem - fm(a) and µ (b) as a function of ftrue
Figure 11.5: The ffitted
m derived by each pseudom, are also
- ffitted
experiment with a signal sample. The pull distributions, ftruemmshown.

11.5 E  S Y

139

observed and expected numbers of tracks with pT >pT thr for four values of pthr.
T
The probability (p0 value) that a background-only experiment is more signal-likethan the observation and the model-independent upper limit on the visible crosssection (σ95%) at 95% CL are also given in the table. Details on the limit setting arevissummarized in Appendix A. The expected numbers of background tracks for eachregion are derived by extrapolating the result of the background-only fit in the pT
region below 75 GeV. No significant excess is observed.

Observed events
Expected events
p0 value
Observed σ95%
[fb]
vis
95%
Expected σvis [fb]> 75 GeV
ptrackT
59
48.5 ± 12.3
0.17
1.76
1.42+0.50−0.39> 100 GeV
36
37.1 ± 9.4
0.41
1.02
1.05+0.37−0.28> 150 GeV
19
24.6 ± 6.3
0.46
0.62
0.67+0.27−0.19> 200 GeV
13
18.0 ± 4.6
0.44
0.44
0.56+0.23−0.16

Table 11.1: Numbers of observed and expected background events as well as theprobability that a background-only experiment is more signal-like than observed
and the model-independent upper limit on the visible cross-section σ95%
at 95%
visCL.

11.4 Full pT -range fit
Figure 11.6 shows the pT distribution for disappearing-track candidates comparedto the background model derived by the background-only fit in the full pT range:
the best-fit values for the yields of interacting-hadron, electron, muon, and pT mismeasured tracks are 2187 ± 71, 852 ± 35, 23 ± 8, and 212 ± 33, respectively. The
predictions for three selected signal samples are also indicated in the figure.
Although the resulting background-only spectrum well describes the data,an excess with a corresponding significance of ∼ 2σ is seen at pT around 90 GeV.
Properties of the events in this region are fully addressed and discussed in Sec. 11.5,
which shows no peculiarities or significant differences in event kinematics or track
properties compared to candidates in nearby track-pT regions.
Table 11.2 shows the values of fit parameters before and after the backgroundonly fit. The parameter that describes the shape of the pT -mismeasured tracks,
which is most important to this analysis, is slightly shifted by the fit due to theexistence of this excess. However, its value is close to the center value of pT mismeasured tracks for systematic samples described in Sec. 9.3.3.
The upper limit on the production cross-section for a given mχ̃± and τχ̃± or1
1∆mχ̃1 at 95% CL is then set along various values of signal strength. The expected
limit is set by the median of the distribution of the 95% CL limits derived by thepseudo-experiments with the expected background and no signal. The systematic parameters are also varied according to their systematic uncertainties when
performing the pseudo-experiments.

11.5 E  S Y

Tracks / GeV

140

105
104
103

s = 8TeV,∫Ldt = 20.3 fb

-1

102

Data
Total background
Interacting hadronp -mismeasured track
T
ElectronMuonmχ∼± = 200 GeV, τχ∼± = 0.2 nsmχ∼±1 = 300 GeV, τχ∼±1 = 0.2 nsmχ∼±1 = 300 GeV, τχ∼±1 = 1.0 ns
1

1

10
1
10-1

Data / Fit

10-2
10-3
2.5
2
1.5
1
0.5
0

track p [GeV]
T

20

30 40

100

200 300
1000
Track p [GeV]T

Figure 11.6: The pT distribution of disappearing-track candidates. The solid circles
show data and lines show each background track-pT spectrum obtained by the
background-only fit. The resulting uncertainties on the pT spectrum for eachbackground are indicated by the error bands. The signal expectations are also
shown. The ratio of the data to the background track-pT spectrum is shown at thebottom of the figure.

Parameter
αe
αµ
~
[θm ]
b0
~h ]
[θ
a0
a1
a2
a3

Before fit
1.00 ± 0.04
1.00 ± 0.35

After fit
0.96 ± 0.04
1.05 ± 0.37

1.78 ± 0.09

1.83 ± 0.08

7.45 ± 0.20
75.9 ± 0.76
1.16 ± 0.63
9.40 ± 0.08

7.51 ± 0.20
75.9 ± 0.78
1.12 ± 0.67
9.39 ± 0.08

Table 11.2: Values of fit parameters before and after the background-only fit.

11.5 E  S Y

141

11.5 Properties of the excess in the track-pT spectrum
This section addresses the excess observed in Fig. 11.6. The followings could resultin such an excess especially in the pT region 75–120 GeV:
• Instrumental background.
• Underestimate of the muon background.
• Signature as decaying charged massive particles.
On the assumption that the excess is originating from charginos, pT shapes ofsignal points with longer lifetimes and lower masses are preferred as shown in
Fig. 11.3.

11.5.1 Instrumental background
Instrumental effects that could mimic disappearing tracks have been already taken
into account in the background estimation. However, there might be missing
detector issues that lead to this excess. Such background tracks might cluster in
specific detector regions or appear in certain data-taking periods. Fig. 11.7 and11.8 show the η–φ distribution of the candidate tracks and its projections. No spots
are found especially in the pT region where the excess is observed, and the events
are randomly distributed during the period. No indication of the instrumental
background effect is found.

track φ

DATA, 75 GeV < p < 120 GeV
T
DATA, 50 GeV < p < 75 GeV
T
DATA, p > 120 GeV
T

3
2
1
0
-1
-2
-3
-2

-1.5

-1

-0.5

0

0.5

1

1.5
2
track ηFigure 11.7: The η–φ distribution of the candidate tracks.

0.5

0.45

s = 8 TeV,

0.4

∫ L dt = 20.3 fb

DATA, 50 GeV < p < 75 GeV-1T

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeV
T

0.35

0.5

0.45

0.25

0.2

0.2

0.15

0.15

0.1

0.1

0.05

0.05
-0.5

0

T

DATA, 75 GeV < p < 120 GeVTTm∼χ± = 200 GeV, τ∼χ± = 10 ns
1

0.25

-1

DATA, 50 GeV < p < 75 GeV

-1

1

0.3

-1.5

∫ L dt = 20.3 fb

DATA, p > 120 GeV

0.3

0
-2

s = 8 TeV,

0.4

0.35

m∼χ± = 200 GeV, τ∼χ± = 10 ns
1

Tracks (a.u.)

11.5 E  S Y

Tracks (a.u.)

142

0.5

1

0

1.5
2
track η(a) The η distribution of the candidate tracks.

-3

-2

-1

0

1

1

2

3
track φ(b) The φ distribution of the candidate tracks.

Figure 11.8: The η and φ distribution of the candidate tracks.

11.5.2 Possibility of underestimating the muon background

Tracks (a.u.)

Unidentified muons cannot be rejected by resolving object overlaps and they could
have high pT . If the excess is caused by the underestimate of W → µν background,the transverse mass reconstructed using the track momentum and EmissshouldThave a peak around 80 GeV. Fig. 11.9 shows the mT distribution of the candidate
tracks in the excess region. The mT distributions of sideband track-pT region andsignal tracks are also shown. One event that has mT of ∼ 80 GeV is observed in thisregion, which is consistent with the estimated muon background yield. The excess
is therefore not likely arising from the underestimate of W → µν background.
0.7
DATA, 50 GeV < p < 75 GeV
T

0.6

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeV

0.5Tm∼χ± = 200 GeV, τ∼χ± = 10 ns
1

1

0.4
0.3

s = 8 TeV,

∫ L dt = 20.3 fb

-1

0.2
0.1
0
0

50

100

150

200

250
300
MT [GeV]Figure 11.9: The mT distribution of the candidate tracks with 75 GeV < pT <120 GeV.

11.5 E  S Y

143

11.5.3 Signature of decaying charged massive particles

101τχ∼± [ns]Figure 11.10 shows the best-fit signal strengths in the τχ̃± –mχ̃± plane. No signal
1
1model explaining the yield and shape of the excess simultaneously is found; theshape of the excess is compatible with a signal point with a lower mass (∼ 80 GeV)
and a longer mean lifetime (∼ 10 ns) as shown in Fig. 11.11, although, its expected
yield is too large with respect to the observation.

9

0.06

8

0.05

7
0.04

6
5

0.03

4

0.02

3
0.01

2
1

100 120 140 160 180 200 220 240mχ∼± [GeV]0

1

Figure 11.10: Best-fit signal strengths in the τχ̃± –mχ̃± plane.

Tracks / GeV

1

1

105
104

s = 8TeV,103∫

Ldt = 20.3 fb

-1

Data
Total background
Interacting hadronp -mismeasured track
T

ElectronMuon
BG + m∼± = 80 GeV, τ∼± = 10 nsχχ11
BG + best-fit µ × m∼± = 80 GeV, τ∼± = 10 ns

102

χ

χ

1

1

10
1
10-1
10-2
10-3

20

30 40

100

200 300

1000
track p [GeV]T

Figure 11.11: The pT spectrum of the candidate tracks and the best-fit signal+background model with mχ̃± = 80 GeV and τχ̃± = 10 ns. The predicted
1
1
pT spectrum by setting the signal strength to unity is also shown.
Data-data comparisons could also provide meaningful information: pT -mismeasured
tracks dominate the background in the high-pT region and the comparisons of kinematic distributions and track properties with those in sideband track-pT regions,144

11.5 E  S Y1
0.9

s = 8 TeV,

0.8∫

L dt = 20.3 fb

DATA, 50 GeV < p < 75 GeV-1T

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeVTm∼χ± = 200 GeV, τ∼χ± = 0.2 ns

0.7

1

1

1

1

m∼χ± = 200 GeV, τ∼χ± = 10 ns

0.6

Tracks (a.u.)Tracks (a.u.)where pT -mismeasured tracks still dominate, could give any hints on the originof the excess. Figure 11.12–11.14 show various kinematic distributions and track
properties for the candidate tracks with pT regions 50–75, 75–120 and >120 GeV.
No significant differences are observed between each other. If the mean lifetime
is relatively short (<∼ 1 ns), the signal events are likely to cluster at NSCT = 2. In
the case of longer mean lifetimes (>∼ 10 ns), a sizable fraction of signal particles
decay in the TRT detector and the number of TRT outliers could have a peak withan intermediate values as a result of a kinked-track signature.
In conclusion, the excess is consistent with neither any of AMSB signal points
nor signatures of decaying charged massive particles, and most likely due to astatistical fluctuation of the background.
1
0.9

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1
2

4

6

8

10

12

0
0

14
N(SCT)

1.2
DATA, 50 GeV < p < 75 GeV
T

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeVTm∼χ± = 200 GeV, τ∼χ± = 10 ns

0.8

1

1

0.6
s = 8 TeV,

0.4

∫ L dt = 20.3 fb

T

DATA, p > 120 GeVTm∼χ± = 200 GeV, τ∼χ± = 0.2 ns
1

1

1

1

m∼χ± = 200 GeV, τ∼χ± = 10 ns

5

10

15

20

25

30

35

40 45 50
N(TRT Outlier)

1.2
DATA, 50 GeV < p < 75 GeV
T

1

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeVTm∼χ± = 200 GeV, τ∼χ± = 10 ns

0.8

1

1

0.6
-1

0.2
0
0

T

DATA, 75 GeV < p < 120 GeV(b) Number of TRT outliers
Tracks (a.u.)Tracks (a.u.)(a) Number of SCT hits

1

DATA, 50 GeV < p < 75 GeV

-1

0.6
0.5

0

∫ L dt = 20.3 fb

0.7

0.5

0

s = 8 TeV,

0.8

s = 8 TeV,

0.4

∫ L dt = 20.3 fb

-1

0.2

5

10

15

(c) Econe40
T

20

25
30
Econe40
[GeV]
T

0
0

5

10

15

20

25

∑E

clus

30
[GeV]

∆ R<0.4 T

(d)

P

clus
∆R<0.4 ET

Figure 11.12: The NSCT , number of TRT outliers and calorimeter isolation variables
for the candidate tracks.

0.7
0.6

s = 8 TeV,

∫ L dt = 20.3 fb

DATA, 50 GeV < p < 75 GeV-1T

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeV

0.5Tm∼χ± = 200 GeV, τ∼χ± = 10 ns
1

145

Tracks (a.u.)Tracks (a.u.)

11.5 E  S Y

0.7

0.3

0.2

0.2

0.1

0.1
0

0.02 0.04 0.06 0.08 0.1
d0 w.r.t PV [mm]Tm∼χ± = 200 GeV, τ∼χ± = 10 ns

-8

-6

T

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeVTm∼χ± = 200 GeV, τ∼χ± = 10 ns

0.6

1

1

0.5

Tracks (a.u.)Tracks (a.u.)

0.7∫

DATA, 50 GeV < p < 75 GeV

-1

-4

-2

0

1

2

4

6
8
d0 / σ(d0)(b) d0 significance

0.9
L dt = 20.3 fb

T

DATA, p > 120 GeV(a) d0s = 8 TeV,T

DATA, 75 GeV < p < 120 GeV

1

0.3

0.8

DATA, 50 GeV < p < 75 GeV

-1

1

0.4

0

∫ L dt = 20.3 fb

0.5

0.4

0
-0.1 -0.08 -0.06 -0.04 -0.02

s = 8 TeV,

0.6

0.6
0.5

s = 8 TeV,

∫ L dt = 20.3 fb

DATA, 50 GeV < p < 75 GeV-1T

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeVTm∼χ± = 200 GeV, τ∼χ± = 10 ns

0.4

1

1

0.3

0.4
0.3

0.2

0.2
0.1
0.1
0
-0.5 -0.4 -0.3 -0.2 -0.10(c) z0 sin θ

0.1

0.2 0.3 0.4 0.5
z0sinθ w.r.t PV [mm]

0
0

0.1

0.2

0.3

0.4

0.5

0.6

0.7 0.8 0.9
1Probability(chi2,ndf)(d) χ2 probability

Figure 11.13: Impact parameter variables and the χ2 probability for the candidate
tracks.

11.5 E  S Y

0.7

Tracks (a.u.)Tracks (a.u.)146

DATA, 50 GeV < p < 75 GeV
T

0.6

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeV

0.5Tm∼χ± = 200 GeV, τ∼χ± = 10 ns
1

0.7
DATA, 50 GeV < p < 75 GeV
T

0.6

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeV

0.5Tm∼χ± = 200 GeV, τ∼χ± = 10 ns
1

1

0.4

1

0.4

0.3

s = 8 TeV,

∫ L dt = 20.3 fb

-1

0.3

0.2

0.2

0.1

0.1

0
50

100

150

200

250

300

350

0
50

400 450 500
1st jet p [GeV]s = 8 TeV,

100

150

200

250

300

∫ L dt = 20.3 fb

350

400

450 500
Emiss
[GeV]
T

T

(b) EmissT

0.7

Tracks (a.u.)Tracks (a.u.)(a) Leading jet pT

DATA, 50 GeV < p < 75 GeV
T

0.6

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeV

0.5Tm∼χ± = 200 GeV, τ∼χ± = 10 ns
1

1
0.9

DATA, 50 GeV < p < 75 GeV
T

DATA, 75 GeV < p < 120 GeV

0.8T

DATA, p > 120 GeVT

0.7m∼χ± = 200 GeV, τ∼χ± = 10 ns
1

1

1

0.6

0.4
0.3

-1

s = 8 TeV,

∫ L dt = 20.3 fb

0.5-1

s = 8 TeV,

0.4

∫ L dt = 20.3 fb

-1

0.3

0.2

0.2
0.1
0

0.1
1.6

1.8

2

2.2

2.4

2.6

2.8

0
0

3

jet-E miss

0.5

1

1.5

2

2.5
3
∆φ(Emiss, track)∆φmin TT

jet−EmissT(d) ∆φ(track,Emiss
)
T

1.2

Tracks (a.u.)Tracks (a.u.)(c) ∆φmin

DATA, 50 GeV < p < 75 GeV
T

1

DATA, 75 GeV < p < 120 GeV
T

DATA, p > 120 GeVTm∼χ± = 200 GeV, τ∼χ± = 10 ns

0.8

1

0.6
s = 8 TeV,

0.4

DATA, 50 GeV < p < 75 GeV

0.7

T

DATA, 75 GeV < p < 120 GeV
T

0.6

DATA, p > 120 GeVTm∼χ± = 200 GeV, τ∼χ± = 10 ns

0.5

1∫ L dt = 20.3 fb

0.8

1

0.4
-1

s = 8 TeV,

0.3

1

∫ L dt = 20.3 fb

-1

0.2
0.2
0
0

0.1
1

2

3

4

5

6

7

(e) Number of jets with pT > 45 GeV

8
Njets

0
0

5

10

15

20

25

30

35
40
# of vertices(f) Number of reconstructed vertices

Figure 11.14: Kinematic distributions and reconstructed vertices for the candidate
tracks.

Chapter 12Interpretations and Discussion
12.1 Constraints on the AMSB model
In the absence of a signal, constraints are set on mχ̃± and τχ̃± . The upper limit
11
on the production cross-section for a given mχ̃± and τχ̃± at 95% CL is set at the11
point where the CL of the “signal+background” hypothesis, based on the profile
likelihood ratio [105] and the CLs prescription [106], falls below 5% when scanning
the CL along various values of signal strength. The constraint on the allowedτχ̃± –mχ̃± parameter space is shown in Fig. 12.1. The expected limit is set by the11median of the distribution of 95% CL limits derived by pseudo-experiments withthe expected background and no signal, where the systematic parameters arevaried according to their systematic uncertainties. The regions excluded by theprevious ATLAS search [13] and the LEP2 searches are also indicated: the exampleof the exclusion reached by the ALEPH experiment [28] of 88 GeV at 95% CL thatis derived for the chargino mass in the case of heavy sfermions, irrespective of thechargino-neutralino mass difference is shown as LEP2 result.
The analysis is not performed for signals having τχ̃1 > 10 ns (corresponding ∆mχ̃1 being below the charged pion mass) because a significant fraction of
charginos would traverse the ID before decaying, thereby reducing the event selection efficiency. In these scenarios, the charginos are considered as stable particles
and the main search tool would be to look for tracks with anomalous ionizationenergy loss. In comparison with the previous result, the sensitivity to charginos
having τχ̃± < 1 ns is significantly improved and the exclusion reach is extended by
1∼ 200 GeV.
Figure 12.2 shows the constraint on the allowed ∆mχ̃1 –mχ̃± parameter space of1
GeV forthe AMSB model; the expected 95% CL exclusion reaches mχ̃± = 245+25−30
1∆mχ̃1 ∼ 160 MeV. The limits on τχ̃± are converted onto ∆mχ̃1 following Ref. [107].
1
The theoretical prediction of ∆mχ̃1 for wino-like lightest chargino and neutralinostates at two-loop level [25] is also indicated in the figure. A new limit that excludes
charginos of mχ̃± < 270 GeV (corresponding ∆mχ̃1 and τχ̃± being ∼ 160 MeV and11∼ 0.2 ns, respectively) at 95% CL is set in the AMSB model.
147

148

12.2 I  D

tanβ = 5, µ > 01τχ∼± [ns]Figure 12.3 shows the constraint on the allowed m3/2 – m0 parameter space of
the AMSB model. The limit on the gravitino mass m3/2 is calculated by Eq. 2.12. The
shaded gray region is excluded theoretically because sleptons become tachyons
due to small m0 . The shaded light blue region is also excluded due to no electroweak symmetry breaking. The gravitino with m3/2 < 96 TeV is excluded at 95%
CL in the AMSB model.

10

1

ATLAS
s = 8 TeV,

∫ L dt = 20.3 fb-1

Observed 95% CL limit (±1 σtheory)

10-1

Expected 95% CL limit (±1 σexp)
ATLAS ( s = 7 TeV, 4.7 fb-1, EW prod.)
ALEPH (Phys. Lett. B533 223 (2002))∼±‘Stable’ χ1

100 150 200 250 300 350 400 450 500 550 600mχ∼± [GeV]1

Figure 12.1: Constraint on the allowed τχ̃± –mχ̃± space for tan β = 5 and µH > 0.
1
1
The black dashed line shows the expected limits at 95% CL, with the surrounding
shaded band indicating the 1σ exclusions due to experimental uncertainties. Observed limits are indicated by the solid bold contour representing the nominal limit
and the narrow surrounding shaded band is obtained by varying the cross-section
by the theoretical scale and PDF uncertainties. The previous result from Ref. [13]and an example of the limits achieved at LEP2 by the ALEPH experiment [28]are also shown on the left by the dotted line and the shaded region, respectively.
The search for charginos with long lifetimes, as indicated by the upper shaded
region, is not covered by this analysis. The limits achieved at LEP2 by the ALEPH
experiment of 101 GeV for long-lived charginos is taken from [28].

1∆mχ∼ [MeV]12.2 I  D

220

149
tanβ = 5, µ > 0
Observed 95% CL limit (±1 σtheory)
Expected 95% CL limit (±1 σexp)

210
200

ATLAS ( s = 7 TeV, 4.7 fb-1, EW prod.)
ALEPH (Phys. Lett. B533 223 (2002))
Theory (Phys. Lett. B721 252 (2013))∼±‘Stable’ χ
1

ATLAS
190
s = 8 TeV,

∫ L dt = 20.3 fb

-1

180
170
160
150
140
100 150 200 250 300 350 400 450 500 550 600mχ∼± [GeV]1

Figure 12.2: Constraint on the allowed ∆mχ̃1 –mχ̃± space of the AMSB model for1
tan β = 5 and µH > 0. The dashed line shows the expected limits at 95% CL, withthe surrounding shaded band indicating the 1σ exclusions due to experimental
uncertainties. Observed limits are indicated by the solid bold contour representing
the nominal limit and the narrow surrounding shaded band is obtained by varying
the cross-section by the theoretical scale and PDF uncertainties. The previous
result from Ref. [13] and an example of the limits achieved at LEP2 by the ALEPH
experiment [28] are also shown on the left by the dotted line and the shaded region,
respectively. Charginos in the lower shaded region could have significantly longer
lifetime values for which this analysis has no sensitivity as the chargino does notdecay within the tracking volume. For this region of long-lived charginos, thelimits achieved at LEP2 by the ALEPH experiment is 101 GeV [28].

12.2 Constraints on the Pure Gravity Mediation model
The result is interpreted in the framework of the PGM model. The difference
between the AMSB and PGM models is that the gauginos in the PGM modelobtain their masses from additional contributions from threshold effects of theheavy Higgsinos. The gluino mass is about eight times higher than the wino massfor L = 0 as in the AMSB model, while the mass ratio of gluino to wino not differ
in the PGM model. Therefore, the results set by this analysis can be interpreted in
the framework of the PGM model.

12.3 I  D

m3/2 [TeV]150

tanβ = 5, µ > 0

200

Observed 95% CL limit

180

Expected 95% CL limit ( ±1 σexp)

160

Tachyonic Particle
No Electroweak Symmetry Breaking

140
120
100
80
60
40
20
0

500

1000 1500 2000 2500 3000 3500 4000

m0 [GeV]Figure 12.3: Constraint on the allowed m3/2 –m0 space of the AMSB model fortan β = 5 and µH > 0. The dashed line shows the expected limits at 95% CL, withthe surrounding shaded band indicating the 1σ exclusions due to experimental
uncertainties. Observed limits are indicated by the solid red line. The shadedgray region is theoretically excluded because sleptons become tachyons due tosmall m0 . The shaded light blue region is also theoletically excluded due to noelectroweak symmetry breaking.

Figure 12.4 shows the relation between mχ̃0 and m g̃ in the PGM model for a1
given L. The red lines show the predictions for L/m3/2 = 0, 1 and 2. The grayshaded region is not favored because the gluino becomes LSP (m g̃ < mχ̃0 ), and the1light violet shaded regions are excluded at 95 % CL by this analysis. The constraint
obtained by the search via the gluino pair production process [108] is also shown.
This analysis excludes the gluino having a mass up to 2240, 1200 and 840 GeV for
L/m3/2 = 0, 1 and 2, respectively. A limit on the gluino mass of 2.2 TeV in the
AMSB model (L/m3/2 = 0), achieved by this analysis, is more stringent than thatobtained by the search for the gluino pair production process giving a limit of ∼1.4 TeV [108].

12.4 I  D

151

2 =

2

900

3/

L/

m

1

mχ∼0 [GeV]

1000

1

χ

m∼ 0

700

>m

g~

800

=1

600

2

3/
L/m

500
400
300

L/m 3/2

=0

(AM

SB)

ATLAS ( s = 8 TeV, 20.3 fb-1)

200

~~
~
Limits from gg production; g→qq
(ATLAS-CONF-2013-047)

100
500

1000

1500

2000

2500

3000

m~g [GeV]

Figure 12.4: Relation between mχ̃0 and m g̃ in the PGM model for a given L. The red
1lines show the predictions with L/m3/2 = 0, 1 and 2, respectively. The gray shaded
region is not favored because gluino becomes LSP (m g̃ < mχ̃0 ), and the light violet
1
shaded region is excluded by this analysis. The constraint obtained by the search
for the gluino pair production process [108] is also shown.

12.3 Constraints on wino dark matter
The result is also interpreted in the framework of wino dark matter scenarios,
and the limit on the annihilation cross-section is set. As stated in Sec. 2.3.5,
the wino annihilation cross-section in the thermal scenario is less constrained by
the indirect searches especially in the low mass region. Figure 12.5 shows theconstraints on the annihilation cross-section as a function of the wino mass [32] on
top of Fig. 2.11. A new limit on the wino dark matter with mχ̃0 < 270 GeV is set. In
1
the thermal scenario, the collider experiments have better sensitivity than that ofindirect searches in the low mass region. The constraint on the wino annihilation
cross-section in the non-thermal scenario is also shown in Fig. 12.6.

12.4 Discussion
This analysis, based on 20.3 fb−1 of 8 TeV pp collision data, has increased thesensitivity compared to the previous ATLAS searches [9, 13] due to analysis im-

10-22

10-21
-23

10
-22

10

σγ γ + γ Zv [cm3/s]

12.4 I  D

σWWv [cm3/s]

152

10-24
-23

10

-25

10
10-24

-26

ATLAS ( s = 8 TeV,

-25

10

∫

10
-1

L dt = 20.3 fb )H.E.S.S.

Annihilation cross-section: σWWv

Fermi

Annihilation cross-section: σγ γ + γ Zv

10-27

-26

10

0.5

1

1.5

2

2.5

3

mχ∼0 [TeV]1Figure 12.5: Constraints on the annihilation cross-section as a function of the winomass [32]. The theoretical cross-section of annihilation into W+ W− , γ γ and γ
Z final states are also indicated by the dashed red line and the solid blue line,
respectively. The shaded green region is excluded by this result. The excluded
regions by the Fermi and H.E.S.S. observations are indicated by the shaded red
region and the shaded blue region, respectively. These exclusion contours are
obtained on the assumption that the wino abundance is set by thermal freezeout. The shaded yellow region between the dotted lines corresponds to Ωh2 =0.12 ± 0.06. In the black shaded region, a thermal wino exceeds the observed relicdensity.

provements and increases in the beam energy and luminosity. The most significant improvement is achieved by enhancing the track reconstruction efficiency
for charginos having short decay length and the trigger efficiency by adopting
a dedicated topological trigger. In particular, the efficiency for charginos withτχ̃± ∼ 0.2 ns, predicted for ∆mχ̃1 ∼ 160 MeV, is around 100 times larger than in1
the previous searches. This analysis significantly surpasses the reach of the LEP
experiments, and a chargino mass below 270 GeV is excluded at 95% CL, which
also directly constrains the mass of wino dark matter..
The center-of-mass energy will be increased to 14 TeV in 2015 with a peak34
2 −1
instantaneous√ luminosity of ∼ 10 cm s . In order to estimate the expected sensitivity with s = 14 TeV data, two benchmark values of integrated luminosity are

153

10-22
-21

10

-23

10

10-22
10-24

σγ γ + γ Zv [cm3/s]

σWWv [cm3/s]

12.4 I  D

-23

10

-25

10
-24

10

10-26
-25

10

ATLAS ( s = 8 TeV,
-26

10

10-27

0.5

10-27

∫ L dt = 20.3 fb )-1

H.E.S.S.

Annihilation cross-section: σWWv

Fermi

Annihilation cross-section: σγ γ + γ Zv

1

1.5

2

2.5

3

mχ∼0 [TeV]1Figure 12.6: Constraints on the annihilation cross-section as a function of the winomass [32]. The theoretical cross-section of annihilation into W+ W− , γ γ and γ
Z final states are also indicated by the dashed red line and the solid blue line,
respectively. The shaded green region is excluded by this result. The excluded
regions by the Fermi and H.E.S.S. observations are indicated by the shaded red
region and the shaded blue region, respectively. These exclusion contours are
obtained on the assumption that the wino comprises all dark matter due to a
non-thermal history.

154

12.4 I  D

1

τχ± [ns]

considered: the integrated luminosity of 20 (100) fb−1 will be achieved by the endof 2016 (2018). Figure 12.7 shows the expected 5 σ discovery reach in the τχ̃± –mχ̃±
1
1√
space using 20 fb−1 and 100 fb−1 data at s = 14 TeV. These are calculated on
the assumption that the same signal selection efficiency and signal-to-backgroundratio achieved in the 8 √TeV analysis. A chargino mass up to 500 GeV can be
explored with the early s = 14 TeV data.

1

Discovery, 20 fb-1
Exclusion, 20 fb-1
Discovery, 100 fb-1
Exclusion, 100 fb-1

s = 14 TeV

10-1
100 150 200 250 300 350 400 450 500mχ± [GeV]
1√
Figure 12.7: Expected 5 σ discovery reach at s = 14 TeV with 20fb−1 (light blue)and 100fb−1 (green) data in the τχ̃± –mχ̃± space.
1

1

In 14 TeV pp collisions, a fourth pixel layer [109] will be installed between a
new beryllium beam-pipe and the current b-layer, which enhances the tracking
performance in a high-luminosity. This additional layer of pixel detector may help
to reconstruct the chargino tracks that decay before reaching the SCT detector. The
signal track reconstruction efficiency in the 14 TeV run is therefore expected to bemuch larger than that in the 8 TeV run, which provides a capability of exploringa chargino being much heavier and having a smaller production cross-section.

Chapter 13

Conclusions
A search is presented for direct chargino production
based on a disappearing-track√
signature using 20.3 fb−1 of pp collisions at s = 8 TeV collected with the ATLAS
experiment at the LHC.
Anomaly-Mediated Supersymmetry Breaking model, where the SUSY breaking is caused by loop effects, provides a constrained mass spectrum of SUSY
particles. One prominent feature of these models is that the LSP is the nearly pureneutral wino that is mass-degenerate with the charged wino. The lightest chargino
is slightly heavier than the lightest neutralino due to radiative corrections involv0±
ing electroweak gauge bosons. The typical mass splitting between χ̃1 and χ̃1 is±∼ 160 MeV, which implies that χ̃1 has a significant lifetime and predominantly
0±
decays into χ̃1 plus a low-momentum π± . The mean lifetime of χ̃1 is expected
to be typically a fraction of a nanosecond. Therefore, some charginos could havedecay length exceeding a few tens of centimeters at the LHC. When decaying in thesensitive volume, they are expected to be observed as “disappearing tracks” thathave no more than a few associated hits in the outer region of the tracking system,and the softly emitted π± is not reconstructed. This dissertation explores AMSB
scenarios by searching for charginos with their subsequent decays that result insuch disappearing tracks.
A new method to detect charginos that decay before reaching the SCT detector
called “Re-tracking” has been developed in this dissertation. In the search basedon the 7 TeV dataset, the Re-tracking was not adopted and the sensitivity tocharginos having a fraction of a nanosecond was limited. The Re-tracking requires
a minimum of three pixel hits and provides a fully efficient tracking capability
for charginos traversing the pixel detector. The efficiency for charginos withτχ̃± ∼ 0.2 ns, is around 100 times larger than that in the previous searches.
1, and a high-pT
The signal topology is characterized by a high pT jet, large Emiss
T
disappearing track. The SM processes can result in final-state kinematics similar
to that of the signal, therefore, the signal events can not be enhanced by any
kinematic selection requirements. In this dissertation, a dedicated topologicaltrigger is developed to attain a higher signal efficiency.
155

156

13.0 C

There are three types of background tracks: interacting-hadron tracks, charged
leptons failing to satisfy their identification criteria, and pT -mismeasured tracks.
A background estimation based on the MC simulation suffers from large uncertainties due to the poor statistics and has difficulty simulating the properties ofbackground tracks. Therefore, a data-driven approach has been developed toestimate the background track-pT spectrum.
No significant excess above the background expectation is observed for candidate tracks with large pT , and constraints on the AMSB model are obtained. In
the AMSB model, a chargino mass below 270 GeV is excluded at 95% CL. Given
the ratio M3 /M2 of ∼ 8, a gluino mass below 2240 GeV is excluded at 95% CL,
which is more stringent than that obtained via inclusive searches for squark andgluino production giving a limit of ∼ 1.4 TeV on a gluino mass. The results arealso interpreted in the framework of the Pure Gravity Mediation model. In the
PGM model, the gluino having a mass up to 2240, 1200, and 840 GeV for L/m3/2 =0, 1, and 2 is excluded at 95% CL, respectively. Finally, the results are interpreted
in the context of the wino dark matter scenario. A wino dark matter with a mass
below 270 GeV is excluded at 95% CL.

Acknowledgments
I hereby would like to acknowledge the help and support I have received frommany people throughout my Ph.D studies.
First of all, I would like to sincerely thank my supervisor, Prof. Tomio Kobayashi
for giving me the opportunity to perform my Ph.D studies within the ATLAS groupof the University of Tokyo. I am very grateful for the encouragement, guidance,and dedicated support that I have received from him.
I would like to express my great appreciation to Prof. Shoji Asai for his continuous assistance and guidance. He taught me the basics of high energy physics
and guided me to the world of elementary particle physics, especially the worldof supersymmetry.
My deepest gratitude goes to Prof. Shimpei Yamamoto for his passionate guidance, sincere advice, and great encouragement. Whenever I was confronted witha difficulty in my study, he helped me a lot and guided the work to the right
direction with his experience. Without his help, my dissertation would never havecompleted.
I would like to thank Dr. Frederic Brochu for teaching me the tracking software.
The significant improvements that were achieved in my study would never haverealized without his help.
I would like to thank Dr. Toshio Namba for teaching me experimental techniques when I was a master’s student.
I am very grateful to all the members of the University of Tokyo and ATLAS
Japan group at CERN, Prof. Tatsuo Kawamoto, Prof. Junichi Tanaka, Prof. Naoko
Kanaya, Prof. Ikuo Ueda, Prof. Koji Terashi, Prof. Yuji Enari , Prof. Koji Nakamura,
Prof. Yousuke Kataoka, Dr. Tatsuya Masubuchi, Dr. Takashi Yamanaka, Dr. Keita
Hanawa, Mr. Takuya Nobe, Mr. Ryo Nagai, Mr. Masahiro Morinaga, and Mr. Yuto
Minami for their guidance, advice, and help throughout my time at CERN. I
would like to thank Prof. Tetsuro Mashimo, Prof. Yoshizumi Inoue, Prof. Tomoaki
Nakamura, and Mr. Nagataka Matsui for providing me an excellent computing
environment for data analysis at Tokyo.
I appreciate the conveners and the collaborators of the SUSY working group;
Prof. Pascal Pralavorio, Prof. Andreas Hoecker, Dr. Monica D’Onofrio, Dr. Nick
Barlow, Dr. David Miller, Prof. Christoph Rembser, Prof. Andy Haas, and Prof. Claudia Gemme gave me useful advice and excellent suggestions to my study.
157

I am very happy to have many friends in the same period; Mr. Yuichi Sasaki,
Mr. Akira Miyazaki, Mr. Yohei Yamaguchi, Mr. Yutaro Iiyama, Mr. Keisuke Yoshihara, Mr. Yoichi Ninomiya, Mr. Yuki Fujii, and Mr. Kim Siang Khaw. It is my greathonor that I was able to work with them, and I am looking forward to working
together in the near future.
A big thank you goes of course to all the other members from the Universityof Tokyo for providing a very friendly, collaborative, and productive working
atmosphere: Prof. Akira Ishida, Dr. Takayuki Yamazaki, Mr. Ginga Akimoto,
Mr. Inada Toshiaki, Ms. Aine Kobayashi, Ms. Maya Okawa, Mr. Yuki Kawanishi,
Mr. Chikuma Kato, Mr. Syunsuke Adachi, Mr. Tomohiro Yamaji, Mr. Shingo Terao,
Mr. Shingo Hisajima, and Mr. Yuji Minegishi.
I would like to thank all the members of my choir, scatola di voce. Whenever I
came back to Japan from CERN, they warmly welcomed me and encouraged meby their excellent performances.
Last but not least, I would like to thank my family for supporting my decision
to study physics and their constant encouragement.

158

Bibliography[1] G. F. Giudice, M. A. Luty, H. Murayama, and R. Rattazzi, Gaugino Mass
without Singlets, JHEP 12 (1998) 027, arXiv:hep-ph/9810442.
[2] L. Randall and R. Sundrum, Out of this world supersymmetry breaking, Nucl.
Phys. B557 (1999) 79, arXiv:hep-th/9810155.
[3] M. Ibe, S. Matsumoto, and T. T. Yanagida, Pure Gravity Mediation with m3/2
= 10-100 TeV, Phys. Rev. D85 (2012) 095011, arXiv:1202.2253 [hep-ph].
[4] L. J. Hall, Y. Nomura, and S. Shirai, Spread Supersymmetry with Wino LSP:
Gluino and Dark Matter Signals, JHEP 1301 (2013) 036, arXiv:1210.2395[hep-ph].
[5] A. Arvanitaki, N. Craig, S. Dimopoulos, and G. Villadoro, Mini-Split, JHEP
1302 (2013) 126, arXiv:1210.0555 [hep-ph].
[6] N. Arkani-Hamed, A. Gupta, D. E. Kaplan, N. Weiner, and T. Zorawski,
Simply Unnatural Supersymmetry, arXiv:1212.6971 [hep-ph].
[7] J. Hisano, S. Matsumoto, M. Nagai, O. Saito, and M. Senami,
Non-perturbative Effect on Thermal Relic Abundance of Dark Matter, Phys. Lett.
B646 (2007) 34–38, arXiv:0610249 [hep-ph].
[8] T. Moroi and L. Randall, Wino cold dark matter from anomaly mediated SUSY
breaking, Nucl. Phys. B570 (2000) 455–472, arXiv:hep-ph/9906527.
[9] ATLAS Collaboration, Search for anomaly-mediated supersymmetry breaking
with√ the ATLAS detector based on a disappearing-track signature in pp collisions
at s = 7 TeV, Eur.Phys.J. C72 (2012) 1993, arXiv:1202.4847 [hep-ex].
[10] ATLAS Collaboration, Search for long-lived charginos in anomaly-mediatedsupersymmetry √breaking scenarios with the ATLAS detector using 4.7 fb−1 data of
pp collisions at s = 7 TeV, Tech. Rep. ATLAS-CONF-2012-034, CERN,
Geneva, Mar, 2012.
[11] Y. Azuma, Search for Long-lived Chargino with Anomaly-Mediated√Supersymmetry Breaking Scenarios in pp Collisions at s = 7 TeV. PhD thesis,
The University of Tokyo, 2012.
159

[12] ATLAS Collaboration, Search for squarks and gluinos with the ATLAS detector√
in final states with jets and missing transverse momentum and 20.3 fb−1 of s = 8
TeV proton-proton collision data, Tech. Rep. ATLAS-CONF-2013-047, CERN,
Geneva, May, 2013.
[13] ATLAS Collaboration, Search for direct chargino production inanomaly-mediated supersymmetry
breaking models based on a disappearing-track√
signature in pp collisions at s = 7 TeV with the ATLAS detector, JHEP 1301
(2013) 131, arXiv:1210.2852 [hep-ex].
[14] F. Englert and R. Brout, Broken symmetry and the mass of gauge vector meson,
Phys. Rev. Lett. 13 (1964) 321.
[15] P. W. Higgs, Broken symmetry, massless particles and gauge field, Phys. Lett. 12(1964) 132.
[16] ATLAS Collaboration, Observation of a new particle in the search for theStandard Model Higgs boson with the ATLAS detector at the LHC, Phys. Lett.
B716 (2012) 1–29, arXiv:1207.7214 [hep-ex].
[17] C. Collaboration, Observation of a new boson at a mass of 125 GeV with the
CMS experiment at the LHC, Phys. Lett. B716 (2012) 30–61, arXiv:1207.7235[hep-ex].
[18] http://en.wikipedia.org/wiki/Standard Model.
[19] J. L. Feng, Naturalness and the Status of Supersymmetry,
http://arxiv.org/abs/1302.6587, 2013. arXiv:1302.6587 [hep-ph].
[20] S. P. Martin, A Supersymmetry Primer,
http://arxiv.org/abs/hep-ph/9709356, 1997. arXiv:hep-ph/9709356.
[21] Particle Data Group Collaboration, K. Nakamura et al., Review of particle
physics, J. Phys. G37 (2010) 075021 (398–399).
[22] G.D. Coughlan et al., Cosmological problems for the polonyi potential, Phys.
Lett. B131 (1983) 59.
[23] M. Ibe, Y. Shinbara, and T.T. Yanagida, The Polonyi Problem and Upper boundon Inflation Scale in Supergravity, Phys. Lett. B639 (2006) 534, arXiv:0605252[hep-ph].
[24] K. Huitu, J. Laamanen, and P. N. Pandita, Sparticle spectrum and constraints
in anomaly mediated supersymmetry breaking models, Phys. Rev. D 65 (2010)
115003–1.
[25] M. Ibe, S. Matsumoto, and R. Sato, Mass Splitting between Charged andNeutral Winos at Two-Loop Level, Phys.Lett. B721 (2013) 252–260,
arXiv:1212.5989 [hep-ph].
160

[26] O. Yasuhiro, Y. Masahiro, and Y. Tsutomu, Upper Bound of the Lightest Higgs
Boson Mass in the Minimal Supersymmetric Standard Model, Prog. Theor.
Phys. 85 (1) (1991) 1–5.
[27] F. Gabbiani, E. Gabrielli, A. Masiero, and L. Silvestrini, A complete analysis of
FCNC and CP constraints in general SUSY extensions of the standard model,
Nucl. Phys. B477 (1996) 321–352, arXiv:hep-ph/9604387.
[28] ALEPH Collaboration, Search for charginos nearly mass degenerate with thelightest neutralino in e+ e- collisions at center-of-mass energies up to 209-GeV,
Phys.Lett. B533 (2002) 223–236, arXiv:hep-ex/0203020 [hep-ex].
[29] OPAL Collaboration, Search for nearly mass degenerate charginos andneutralinos at LEP, Eur.Phys.J. C29 (2003) 479–489, arXiv:hep-ex/0210043[hep-ex].
[30] DELPHI Collaboration, Search for SUSY in the AMSB scenario with the
DELPHI detector, Eur.Phys.J. C34 (2004) 145–156, arXiv:hep-ex/0403047[hep-ex].
[31] http://lepsusy.web.cern.ch/lepsusy/www/inoslowdmsummer02/
charginolowdm pub.html.
[32] T. Cohen, M. Lisanti, A. Pierce, and T.R. Slatyer, Wino Dark Matter Under
Siege, http://arxiv.org/abs/1307.4082, 2013.
arXiv:1307.4082 [hep-ph].
[33] J. Fan and M. Reece, In Wino Veritas? Indirect Searches Shed Light on
Neutralino Dark Matter, http://arxiv.org/abs/1307.4400, 2013.
arXiv:1307.4400 [hep-ph].
[34] Fermi-LAT Collaboration, Constraining Dark Matter Models from a Combined
Analysis of Milky Way Satellites with the Fermi Large Area Telescope, Phys. Rev.
Lett. 107 (2011) 241302, arXiv:1108.3546 [astro-ph].
[35] H.E.S.S. Collaboration, Search for photon line-like signatures from Dark Matterannihilations with H.E.S.S, Phys. Rev. Lett. 110 (2013) 041301,
arXiv:1301.1173 [astro-ph].
[36] PAMELA Collaboration, PAMELA results on the cosmic-ray antiproton flux
from 60 MeV to 180 GeV in kinetic energy, Phys. Rev. Lett. 105 (2010) 121101,
arXiv:1007.0821 [astro-ph].
[37] J. Hisano, K. Ishiwata, and N. Nagata, A complete calculation for direct
detection of Wino dark matter, Phys.Lett. B706 (2011) 208,
arXiv:hep-ph/1004.4090.
161

[38] J. Hisano, K. Ishiwata, N. Nagata, and T. Takesako, Direct Detection ofElectroweak-Interacting Dark Matter, JHEP 1107 (2011) 005,
arXiv:hep-ph/1104.0228.
[39] XENON100 Collaboration, Dark Matter Results from 225 Live Days of
XENON100 Data, Phys. Rev. Lett. 109 (2012) 181301, arXiv:1207.5988[hep-ex].
[40] ATLAS Collaboration, ATLAS detector and physics performance : Technical
Design Report, 1, tech. rep., CERN, 1999. ATLAS-TDR-014,
CERN-LHCC-99-014.
[41] ATLAS Collaboration, ATLAS detector and physics performance : Technical
Design Report, 2, tech. rep., CERN, 1999. ATLAS-TDR-015,
CERN-LHCC-99-015.
[42] ATLAS Collaboration, ATLAS Experiment at the CERN Large Hadron Collider,
JINST 3 (2008) S08003.
[43] CMS Collaboration, CMS technical proposal, tech. rep., CERN, 1994.
CERN-LHCC-894-38.
[44] LHCb Collaboration, LHCb technical proposal, tech. rep., CERN, 1998.
CERN-LHCC-98-004.
[45] ALICE Collaboration, ALICE: Technical proposal for a Large Ion collider
Experiment at the CERN LHC, tech. rep., CERN, 1997. CERN-LHCC-95-71.
[46] A. Yamamoto et al., Progress in ATLAS Central Solenoid Magnet, IEEE Trans.
Appl. Supercond. 10 (2000) 353.
[47] P. Védrine et al., Manufacturing and integration progress of the ATLAS barrel
toroid magnet at CERN, IEEE Trans. Appl. Supercond. 16 (2006) 553.
[48] ATLAS Collaboration, ATLAS inner detector: Technical Design Report, 1, tech.
rep., CERN, Geneva, 1997. CERN-LHCC-97-016.
[49] ATLAS Collaboration, ATLAS inner detector: Technical Design Report, 2, tech.
rep., CERN, Geneva, 1997. CERN-LHCC-97-017.
[50] ATLAS Pixel Collaboration, ATLAS pixel detector electronics and sensors,
JINST 3 (2008) P07007.
[51] ATLAS SCT Collaboration, The barrel modules of the ATLAS semiconductor
tracker, Nucl. Instrum. Meth. A568 (2006) 642–671.
[52] ATLAS SCT Collaboration, The ATLAS semiconductor tracker end-cap module,
Nucl. Instrum. Meth. A575 (2007) 353–389.
162

[53] ATLAS SCT Collaboration, The silicon microstrip sensors of the ATLAS
semiconductor tracker, Nucl. Instrum. Meth. A578 (2007) 98–118.
[54] ATLAS TRT Collaboration, The ATLAS Transition Radiation Tracker (TRT)
proportional drift tube: design and performance, JINST 3 (2008) P02013.
[55] ATLAS TRT Collaboration, The ATLAS TRT Barrel Detector, JINST 3 (2008)
P02014.
[56] ATLAS TRT Collaboration, The ATLAS TRT end-cap detectors, JINST 3 (2008)
P10003.
[57] ATLAS Collaboration, ATLAS calorimeter performance : Technical Design
Report, tech. rep., CERN, 1996. ATLAS-TDR-1, CERN-LHCC-96-040.
[58] ATLAS Collaboration, ATLAS liquid-argon calorimeter : Technical Design
Report, tech. rep., CERN, 1996. ATLAS-TDR-2, CERN-LHCC-96-041,
CERN/LHCC 96-41.
[59] ATLAS Collaboration, ATLAS tile calorimeter : Technical Design Report, tech.
rep., CERN, 1996. ATLAS-TDR-3, CERN-LHCC-96-042.
[60] D.M. Gingrich et al., Construction, Assembly and Testing of the ATLAS
Hadronic End-cap Calorimeter , JINST 2 (2007) P05005.
[61] A. Artamonov et al., The ATLAS Forward Calorimeter, JINST 3 (2008) P02010.
[62] ATLAS Collaboration, ATLAS muon spectrometer : Technical Design Report,
tech. rep., CERN, 1997. ATLAS-TDR-10 ; CERN-LHCC-97-022.
[63] ATLAS Collaboration, ATLAS Forward Detectors for Measurement of Elastic
Scattering and Luminosity, tech. rep., CERN, 2008. ATLAS-TDR-18 ;CERN-LHCC-2008-004.
[64] ATLAS Collaboration, ATLAS level-1 trigger : Technical Design Report, tech.
rep., CERN, 1998. ATLAS-TDR-12 ; CERN-LHCC-98-014.
[65] ATLAS Collaboration, ATLAS high-level trigger, data-acquisition and controls :Technical Design Report, tech. rep., CERN, 2003. ATLAS-TDR-16 ;CERN-LHCC-2003-022.
[66] ATLAS Collaboration, Luminosity Determination in pp Collisions at sqrts=7
TeV Using the ATLAS Detector at the LHC, Eur.Phys.J. C71 (2011) 1630,
arXiv:1101.2185 [hep-ex].
√[67] ATLAS Collaboration, Improved luminosity determination in pp collisions at s
= 7 TeV using the ATLAS detector at the LHC, arXiv:1302.4393 [hep-ex].
163

[68] J. Pumplin et al., New generation of parton distributions with uncertainties fromglobal QCD analysis, JHEP 07 (2002) 012, arXiv:hep-ph/0201195.
[69] A. Martin, W. Stirling, R. Thorne, and G. Watt, Parton distributions for the
LHC, Eur. Phys. J. C63 (2009) 189, arXiv:0901.0002 [hep-ph].
[70] M. Guzzi et al., CT10 parton distributions and other developments in the global
QCD analysis, arXiv:1101.0561 [hep-ph].
[71] T. Sjostrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA
8.1, Comput. Phys. Commun. 178 (2008) 852–867, arXiv:0710.3820.
[72] ATLAS Collaboration, Further ATLAS tunes of PYTHIA 6 and Pythia 8, 2011.
ATL-PHYS-PUB-2011-014.
[73] GEANT4 Collaboration, S. Agostinelli et al., GEANT4: A simulation toolkit,
Nucl. Instrum. Meth. A506 (2003) 250–303.
[74] ATLAS Collaboration, The ATLAS Simulation Infrastructure, Eur. Phys. J.
C70 (2010) 823–874, arXiv:1005.4568 [physics.ins-det].
[75] F. E. Paige, S. D. Protopopescu, H. Baer, and X. Tata, ISAJET 7.69: A Monte
Carlo event generator for pp, anti-p p, and e+e- reactions,
arXiv:hep-ph/0312045 [hep-ph].
[76] M. Bahr et al., Herwig++ Physics and Manual, Eur.Phys.J. C58 (2008)
639–707, arXiv:0803.0883 [hep-ph].
[77] W. Beenakker, R. Hopker, M. Spira, and P. Zerwas, Squark and gluino
production at hadron colliders, Nucl.Phys. B492 (1997) 51–103,
arXiv:hep-ph/9610490 [hep-ph].
[78] P. M. Nadolski et al., Implications of CTEQ global analysis for collider
observation, Phys. Rev. D78 (2008) 013004, arXiv:0802.0007 [hep-ph].
[79] M. Botje et al., The PDF4LHC Working Group Interim Recommendations,
arXiv:1101.0538 [hep-ph].
[80] M. L. Mangano et al., ALPGEN, a generator for hard multiparton processes inhadronic collisions, JHEP 0307 (2003) 001, arXiv:hep-ph/0206293 [hep-ph].
[81] G. Corcella et al., HERWIG 6.5: an event generator for Hadron Emission
Reactions With Interfering Gluons (including supersymmetric processes), JHEP
01 (2001) 010.
[82] J. M. Butterworth, J. R. Forshaw and M. H. Seymour, Multiparton
interactions in photoproduction at HERA, Z. Phys C 72 (1996) 637–646.
arXiv:hep-ph/9601371.
164

[83] J. Alwall et al., Comparative study of various algorithms for the merging of parton
showers and matrix elements in hadronic collisions, Eur. Phys. J. C 53 (2008) 473.
arXiv:0706.2569 [hep-ph].
[84] P. Nason, A New Method for Combining NLO QCD with Shower Monte Carlo
Algorithms, JHEP 11 (2004) 040. arXiv:hep-ph/0409146.
[85] S. Frixione and B. R. Webber, The MC@NLO 3.3 event generator,
arXiv:hep-ph/0612272.
[86] B. P. Kersevan and E. Richter-Was, The Monte Carlo event generator AcerMC
version 2.0 with interfaces to PYTHiA 6.2 and HERWIG 6.5, JHEP 06 (2010)043. arXiv:hep-ph/0405247.
[87] T. Sjostrand, S. Mrenna, and P. Z. Skands, PYTHIA 6.4 Physics and Manual,
JHEP 0605 (2006) 026, arXiv:hep-ph/0603175.
[88] S. Catani, L. Cieri, G. Ferrera, D. de Florian, and M. Grazzini, Vector Boson
Production at Hadron Colliders: A Fully Exclusive QCD Calculation at
Next-to-Next-to-Leading Order, Phys. Rev. Lett. 103 (2009) 082001.
[89] T. Cornelisen et al., The new ATLAS track reconstruction (NEWT), J. Phys.
Conf. Ser 119 (2008) 032014.
[90] R. Fruhwirth, Application of Kalman filtering to track and vertex fitting, Nucl.
Instrum. Meth. A262 (1987) 444–450.
[91] R. Duda and P. Hart, Use of the Hough Transformation to Detect Lines andCurves in Pictures, Comm. ACM 15 (1972) 11–15, 1212.5989.
[92] ATLAS Collaboration, Performanceof primary vertex reconstruction in√
proton-proton collisions at s = 7 TeV in the ATLAS experiment, Tech. Rep.
ATLAS-CONF-2010-069, CERN, Geneva, Jul, 2010.
[93] ATLAS Collaboration, Performance of the ATLAS Inner Detector Track andVertex Reconstruction in the High Pile-Up LHC Environment, Tech. Rep.
ATLAS-CONF-2012-042, CERN, Geneva, Mar, 2012.
[94] M. Cacciari, G. P. Salam, and G. Soyez, The anti-kt jet clustering algorithm,
JHEP 04 (2008) 063, arXiv:0802.1189 [hep-ph].
[95] M. Aleksa et al., ATLAS Combined Testbeam: Computation and Validation of theElectronic Calibration Constatns for the Electromagnetic Calorimeter, Tech. Rep.
ATL-LARG-PUB-2006-003, CERN, Geneva, Jul, 2006.
[96] ATLAS Collaboration, Jet energy scale√and its systematic uncertainty for jets
produced in proton-proton collisions at s = 7 TeV and measured with the
ATLAS detector, Tech. Rep. Tech. Rep. ATL-CONF-2010-056, CERN, Geneva,
July, 2010.
165

[97] W. L. et al., Calorimeter Clustering Algorithms: Description and Performance,
Tech. Rep. ATL-LARG-PUB-2008-002, CERN, Geneva, 2008.
[98] ATLAS Collaboration, Improved electron reconstruction in ATLAS using theGaussian Sum Filter-based model for bremsstrahlung, Tech. Rep. Tech. Rep.
ATL-CONF-2012-047, CERN, Geneva, May, 2012.
[99] ATLAS Collaboration, Muon reconstruction efficiency in reprocessed 2010 LHC
proton-proton collision data recorded with the ATLAS detector, Tech. Rep. Tech.
Rep. ATL-CONF-2011-063, CERN, Geneva, 2011.
[100] ATLAS Collaboration, Measurements of underlying-event properties using
neutral and charged particles in pp collisions at 900 GeV and 7 TeV with the
ATLAS detector at the LHC, Eur.Phys.J. C71 (2011) 1636, arXiv:1103.1816[hep-ex].
[101] ATLAS Collaboration, Search for direct third-generation squark pair
√ production
in final states with missing transverse momentum and two b-jets in s = 8 TeV
pp collisions with the ATLAS detector, JHEP 1310 (2013) 189,
arXiv:1308.2631 [hep-ex].
[102] ATLAS Collaboration, Jet√energy measurement with the ATLAS detector inproton-proton collisions at s = 7 TeV, Eur.Phys.J. C73 (2013) 2304,
arXiv:1112.6426 [hep-ex].
[103] J. Alwall, M. Herquet, F. Maltoni, O. Mattelaer, and T. Stelzer, MadGraph 5 :Going Beyond, JHEP 1106 (2011) 128, arXiv:1106.0522 [hep-ph].
[104] J. Alwall, S. Hoche, F. Krauss, N. Lavesson, L. Lonnblad, et al., Comparative
study of various algorithms for the merging of parton showers and matrix elements
in hadronic collisions, Eur. Phys. J. C53 (2008) 473, arXiv:0706.2569[hep-ph].
[105] G. Cowan, K. Cranmer, E. Gross, and O. Vitells, Asymptotic formulae forlikelihood-based tests of new physics, Eur. Phys. J. C71 (2011) 1554,
arXiv:1007.1727 [physics.data-an].
[106] A. L. Read, Presentation of search results: The CL(s) technique, J. Phys. G28(2002) 2693–2704.
[107] C. Chen, M. Drees, and J. Gunion, Addendum/erratum for ‘searching forinvisible and almost invisible particles at e+ e− colliders’ [hep-ph/9512230] and ‘anonstandard string/SUSY scenario and its phenomenological implications’[hep-ph/9607421], arXiv:hep-ph/9902309 [hep-ph].
[108] ATLAS Collaboration, Search for squarks and gluinos with the ATLAS detector
in final states with jets and missing transverse momentum and 20.3 fb−1 of166√s = 8 TeV proton-proton collision data, tech. rep., CERN, 2013.
ATLAS-CONF-2013-047.
[109] ATLAS Collaboration, ATLAS Insertable B-Layer Technical Design Report,
tech. rep., CERN, Geneva, 2010. CERN-LHCC-2010-013.

167

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Appendix A

Profile Likelihood and CLs
A.1 Profile likelihood ratio
The profile likelihood ratio is used for the hypothesis test to determine the background and signal yields. In order to test a hypothesized value of µ, the profile
likelihood ratio is defined by,λ(µ) =

ˆL(µ, θ̂)L(µ̂, θ̂)

(A.1)

where L is the likelihood function that represents the probability for the measurement, and θ represents the parameters characterizing the shape of the probability
density function. The quantity θ̂ˆ denotes the value of θ that maximizes L for specified µ, and the quantities µ̂ and θ̂ refer to the values that give the unconditional
maximum of the likelihood.
For the purpose of establishing a one-sided upper limit, the test statistic isdefined by(−2 ln λ(µ) forµ̂ ≤ µ,
qµ =
(A.2)
0
forµ̂ > µ.
The ratio λ(µ) is expected to be close to unity if the observed data are in good
agreement with the hypothesized value of µ.
To quantify the degree of agreement between the observed data and hypothesized µ, the p-value defined byZ ∞p=f (qµ |µ)dqµ ,(A.3)
qµ,obsis calculated, where qµ,obs is the value of the statistic qµ observed from the data andf (qµ |µ) is the probability density function (pdf) of qµ .
In high energy physics, the p-value is often translated into an equivalent quantity called the significance, Z. The Z is defined such that a Gaussian distributed
1

2

A.2 P L  CLs

variable found Z standard deviations above its mean has an upper-tail probability
equal to the p-value. That is, the significance Z is related to the p-value by
Z ∞
p=
Z

2
1
√ e−x /2 dx = 1 − Φ(Z),
2π

(A.4)

(b)

(a)
f (qµ|µ)ϕ(x)where Φ is the cumulative standard Gaussian distribution.
The relation between the p-value and the observed qµ and also the significanceZ are shown in Fig. A.1. For the purpose of a discovery, a threshold p-value of
2.87×10−7 on the background-only hypothesis is used, which corresponds to Z =5. For the purpose of excluding a signal hypothesis, a threshold p-value of 0.05 on
the signal plus background hypothesis is used, which corresponds to Z = 1.64.
A.1.

q obs
p- valuep- valueqµZx

Figure A.1: (a) Illustration of the relation between the p-value obtained froman observed value of the test statistic qµ . (b) The standard normal distribution√
φ(x) = (1/ 2π) exp(−x2 /2) showing the relation between the significance Z andthe p-value.

A.2 Confidence level
In order to determine the statistical significance of the observation, CLs+b that isdefined as the probability to obtain a result that is less compatible with the signal
than the observed result assuming the signal hypothesis is used: i.e. CLs+b is
calculated byZ ∞CLs+b =f (qµ |µ)dqµ ,(A.5)

qµ,obs

The signal model is regarded as excluded at a confidence level (CL) of 1-α if CLs+b ≤α. However, if too few candidates are observed to account for the estimatedbackground, any signal models and even the background itself might be excluded.

A.2 P L  CLs

3

In order to solve this problem, the CLs method [106] is often used. In the CLs
method, the signal plus background hypothesis is rejected if
R∞f (qµ |µ)dqµqµ,obs
CLs+b
CLs ≡
= R∞
≤ α,
(A.6)
1 − CLbf (qµ |0)dqµ
qµ,obswhere 1 - CLb is the probability to obtain a result that is less compatible withthe background-only hypothesis than the observed result. A value of 95 % CL iscommonly used to set an exclusion limit on signal models.