P UBLISHED BY I NSTITUTE OF P HYSICS P UBLISHING AND SISSAR ECEIVED : March 18, 2008
R EVISED : May 29, 2008
ACCEPTED : July 7, 2008
P UBLISHED : August 8, 2008

E. Abat,e,† A. Abdesselam,am T.N. Addy,s T.P.A. Åkesson,ab P.P. Allport,yL. Andricek,ah F. Anghinolfi,i R. Apsimon,as E. Arik,e,† M. Arik,e N. Austin,yO.K. Baker,bb E. Banas,l A. Bangert,ah G. Barbier,o S. Baron,i A.J. Barr,am
S. Basiladze,ag L.E. Batchelor,as R.L. Bates,q J.R. Batley,h M. Battistin,i G.A. Beck,aa
A. Beddall,e,1 A.J. Beddall,e,1 P.J. Bell,ac W.H. Bell,q A. Belymam,aa D.P. Benjamin,mJ. Bernabeu,ay H. Bertelsen, j S. Bethke,ah A. Bingul,e,1 A. Bitadze,i J.P. Bizzell,asJ. Blocki,l A. Bocci,m M. Bochenek,k J. Bohm,ap V.G. Bondarenko,a f P. Bonneau,iC.N. Booth,au O. Brandt,am F.M. Brochu,h Z. Broklova,aq J. Broz,aq P.A. Bruckman deRenstrom,am,3 S. Burdin,y C.M. Buttar,q M. Capeáns Garrido,i L. Cardiel Sas,iC. Carpentieri,n A.A. Carter,aa J.R. Carter,h A. Catinaccio,i S.A. Cetin,e,2 M. Chamizo
llatas,o D.G. Charlton,d A. Cheplakov,q S. Chouridou,at M.L. Chu,av V. Cindro,zA. Ciocio,c J.V. Civera,ay A. Clark,o A.P. Colijn,ai T. Cornelissen,i M.J. Costa,ay
D. Costanzo,au J. Cox,am P. Cwetanski,t W. Dabrowski,k J. Dalmau,aa M. Dam, j
K.M. Danielsen,al H. Danielsson,i S. D’Auria,q I. Dawson,au P. de Jong,ai
M.D. Dehchar,am B. Demirkoz,i P. Dervan,y B. Di Girolamo,i S. Diez Cornell,ay
F. Dittus,i S.D. Dixon,au E. Dobson,am O.B. Dogan,e,† Z. Dolezal,aq B.A. Dolgoshein,a f
M. Donega,i M. D’Onofrio,o T. Donszelmann,au O. Dorholt,al J.D. Dowell,d Z. Drasal,aq
N. Dressnandt,an C. Driouchi, j R. Duxfield,au M. Dwuznik,k W.L. Ebenstein,mS. Eckert,n P. Eerola,ab U. Egede,ab K. Egorov,t L.M. Eklund,i M. Elsing,i R. Ely,cV. Eremin,ag C. Escobar,ay H. Evans,t P. Farthouat,i D. Fasching,ba O.L. Fedin,ao
L. Feld,n D. Ferguson,ba P. Ferrari,i D. Ferrere,o L. Fiorini,h J. Fopma,am A.J. Fowler,mH. Fox,n R.S. French,au D. Froidevaux,i J.A. Frost,h J. Fuster,ay S. Gadomski,o,2
P. Gagnon,t B.J. Gallop,as F.C. Gannaway,aa C. Garcia,ay J.E. Garcia Navarro,ay
I.L. Gavrilenko,ae C. Gay,az N. Ghodbane,ah M.D. Gibson,as S.M. Gibson,am
K.G. Gnanvo,aa J. Godlewski,i T. Göttfert,ah S. Gonzalez,ba S. Gonzalez-Sevilla,ay
M.J. Goodrick,h A. Gorišek,z E. Gornicki,l M. Goulette,i Y. Grishkevich,ag
J. Grognuz,i J. Grosse-Knetter, f C. Haber,c R. Härtel,ah Z. Hajduk,l M. Hance,anF.H. Hansen, j J.B. Hansen, j J.D. Hansen, j P.H. Hansen, j K. Hara,aw A. Harvey Jr.,s–1–

2008 JINST 3 P08003

Combined performance tests before installation of
the ATLAS Semiconductor and Transition Radiation
Tracking Detectors

–2–2008 JINST 3 P08003

M. Hauschild,i C. Hauviller,i B.M. Hawes,am R.J. Hawkings,i H.S. Hayward,yS.J. Haywood,as F.E.W. Heinemann,am N.P. Hessey,ai J.C. Hill,h M.C. Hodgkinson,au
P. Hodgson,au T.I. Hollins,d A. Holmes,am R. Holt,as S. Hou,av D.F. Howell,amW. Hulsbergen,i T. Huse,al Y. Ikegami,w Y. Ilyushenka,v C. Issever,am J.N. Jackson,yV. Jain,t K. Jakobs,n R.C. Jared,ba G. Jarlskog,ab P. Jarron,i L.G. Johansen,bP. Johansson,au M. Jones,am T.J. Jones,y D. Joos,n J. Joseph,ba P. Jovanovic,dV.A. Kantserov,a f J. Kaplon,i M. Karagoz Unel,am F. Kayumov,ae P.T. Keener,an
G.D. Kekelidze,v N. Kerschen,au C. Ketterer,n S.H. Kim,aw D. Kisielewska,kB. Kisielewski,l T. Kittelmann, j E.B. Klinkby, j P. Kluit,ai S. Kluth,ah B.R. Ko,mP. Kodys,aq T. Koffas,i E. Koffeman,ai T. Kohriki,w T. Kondo,w N.V. Kondratieva,a f
S.P. Konovalov,ae S. Koperny,k H. Korsmo,ab S. Kovalenko,ao T.Z. Kowalski,kK. Krüger,i V. Kramarenko,ag G. Kramberger,z M. Kruse,m P. Kubik,aq L.G. Kudin,ao
N. Kundu,am C. Lacasta,ay V.R. Lacuesta,ay W. Lau,am A-C. Le Bihan,i S.-C. Lee,av
R.P. Lefevre,o B.C. LeGeyt,an K.J.C. Leney,y C.G. Lester,h Z. Liang,av P. Lichard,iW. Liebig,ai M. Limper,ai A. Lindahl, j S.W. Lindsay,y A. Lipniacka,b G. Llosa Llacer,ay
S. Lloyd,aa A. Loginov,bb C.W. Loh,az M. Lozano Fantoba,ay S. Lucas,i A. Lucotte,rI. Ludwig,n J. Ludwig,n F. Luehring,t L. Luisa,ax J. Lynn,am M. Maaßen,n D. Macina,oR. Mackeprang, j A. Macpherson,a C.A. Magrath,ai P. Majewski,au P. Malecki,lV.P. Maleev,ao I. Mandić,z M. Mandl,i M. Mangin-Brinet,o S. Marti i Garcia,ay
A.J. Martin,bb F.F. Martin,an T. Maruyama,aw R. Mashinistov,a f A. Mayne,au
K.W. McFarlane,s S.J. McMahon,as T.J. McMahon,d J. Meinhardt,n B.R. Mellado
Garcia,ba C. Menot,i I. Messmer,n B. Mikulec,o M. Mikuž,z S. Mima,a j M. Minano,ay
B. Mindur,k V.A. Mitsou,ay P. Modesto,ay S. Moed,o B. Mohn,b R.M. Moles Valls,ay
J. Morin,aa M-C. Morone,o S.V. Morozov,a f J. Morris,aa H.G. Moser,ah
A. Moszczynski,l S.V. Mouraviev,ae A. Munar,an W.J. Murray,as K. Nagai,aa Y. Nagai,aw
D. Naito,a j K. Nakamura,aw I. Nakano,a j S.Y. Nesterov,ao F.M. Newcomer,anR. Nicholson,au R.B. Nickerson,am T. Niinikoski,i N. Nikitin,ag R. Nisius,ah H. Ogren,tS.H. Oh,m M. Olcese, p J. Olszowska,l M. Orphanides,au V. O’Shea,q W. Ostrowicz,lB. Ottewell,am O. Oye,b E. Paganis,au M.J. Palmer,h M.A. Parker,h U. Parzefall,nM.S. Passmore,i S. Pataraia,ah G. Pellegrini,ay H. Pernegger,i∗E. Perrin,oV.D. Peshekhonov,v T.C. Petersen,i R. Petti,g A.W. Phillips,h P.W. Phillips,as
A. Placci,i K. Poltorak,k A. Poppleton,i M.J. Price,i K. Prokofiev,au O. Røhne,alC. Rembser,i P. Reznicek,aq R.H. Richter,ah A. Robichaud-Veronneau,oD. Robinson,h S. Roe,i O. Rohne,al A. Romaniouk,a f L.P. Rossi, p D. Rousseau,ak
G. Ruggiero,i K. Runge,n Y.F. Ryabov,ao A. Salzburger,u J. Sanchez,ay H. Sandaker,bJ. Santander,ay V.A. Schegelsky,ao D. Scheirich,aq J. Schieck,ah M.P. Schmidt,bb,†
C. Schmitt,i E. Sedykh,ao D.M. Seliverstov,ao A. Sfyrla,o T. Shin,s A. Shmeleva,aeS. Sivoklokov,ag S.Yu. Smirnov,a f L. Smirnova,ag O. Smirnova,ab M. Söderberg,ab
A.O. Solberg,b V.V. Sosnovtsev,a f L. Sospedra Suay,ay H. Spieler,c G. Sprachmann,iE. Stanecka,l S. Stapnes,al J. Stastny,ap M. Stodulski,l A. Stradling,ba B. Stugu,bS. Subramania,t S.I. Suchkov,a f V.V. Sulin,ae R.R. Szczygiel,l R. Takashima,xR. Tanaka,a j G. Tartarelli,ad P.K. Teng,av S. Terada,w V.O. Tikhomirov,ae P. Tipton,bb

M. Titov,n K. Toms,ag A. Tonoyan,b D.R. Tovey,au A. Tricoli,as M. Turala,l M. Tyndel,asF. Ukegawa,aw M. Ullan Comes,ay Y. Unno,w V. Vacek,ar S. Valkar,aq J.A. Valls
Ferrer,ay E. van der Kraaij,ai R. VanBerg,an V.I. Vassilakopoulos,s L. Vassilieva,ae
T. Vickey,ba G.H.A. Viehhauser,am E.G. Villani,as J.H. Vossebeld,y T. Vu Anh,oR. Wall,bb R.S. Wallny,i C. Wang,m C.P. Ward,h R. Wastie,am M. Webel,n M. Weber,as
A.R. Weidberg,am P.M. Weilhammer,i C. Weiser,n P.S. Wells,i P. Werneke,ai
M.J. White,h D. Whittington,t A. Wildauer,i I. Wilhelm,aq H.H. Williams,anJ.A. Wilson,d M.W. Wolter,l S.L. Wu,ba A. Zhelezko,a f H.Z. Zhuau and A. Zseneio
Edmonton, AB T6G 2G7, Canadab University of Bergen, Department for Physics and Technology,

Allegaten 55, NO - 5007 Bergen, Norwayc Lawrence Berkeley National Laboratory and University of California, Physics Division,

MS50B-6227, 1 Cyclotron Road, Berkeley, CA 94720, United States of Americad School of Physics and Astronomy, University of Birmingham,

Edgbaston, Birmingham B15 2TT, United Kingdom
e Faculty of Sciences, Department of Physics, Bogazici University,

TR - 80815 Bebek-Istanbul, Turkey
f Physikalisches Institut der Universitaet Bonn, Nussallee 12, D - 53115 Bonn, Germany
g Brookhaven National Laboratory, Physics Department,

Bldg. 510A, Upton, NY 11973, United States of America
h Cavendish Laboratory, University of Cambridge,

J J Thomson Avenue, Cambridge CB3 0HE, United Kingdom
i CERN, CH - 1211 Geneva 23, Switzerland, Switzerland
j Niels Bohr Institute, University of Copenhagen,Blegdamsvej 17, DK - 2100 Kobenhavn 0, Denmark
k Faculty of Physics and Applied Computer Science of the AGH-University of Science andTechnology, (FPACS, AGH-UST), al. Mickiewicza 30, PL-30059 Cracow, Polandl The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of Sciences,ul. Radzikowskiego 152, PL - 31342 Krakow, Polandm Duke University, Department of Physics, Durham, NC 27708, United States of American Physikalisches Institut, Universitaet Freiburg,

Hermann-Herder Str. 3, D - 79104 Freiburg i.Br., Germanyo Universite de Geneve, Section de Physique,

24 rue Ernest Ansermet, CH - 1211 Geneve 4, Switzerland
p INFN Genova and Università di Genova, Dipartimento di Fisica,

via Dodecaneso 33, IT - 16146 Genova, Italy
University of Glasgow, Department of Physics and Astronomy,
UK - Glasgow G12 8QQ, United Kingdom
r Laboratoire de Physique Subatomique et de Cosmologie, CNRS-IN2P3, Universite Joseph
Fourier, INPG, 53 avenue des Martyrs, FR - 38026 Grenoble Cedex, Frances Hampton University, Department of Physics, Hampton, VA 23668, United States of AmericatIndiana University, Department of Physics, Swain Hall West,
Room 117, 727 East Third St., Bloomington, IN 47405-7105, United States of Americau Institut fuer Astro- und Teilchenphysik, Technikerstrasse 25, A - 6020 Innsbruck, Austriaq–3–2008 JINST 3 P08003

a Centre for Particle Physics, Department of Physics, University of Alberta,

v Joint Institute for Nuclear Research, JINR Dubna, RU - 141 980 Moscow, Russia
w KEK, High Energy Accelerator Research Organization, 1-1 Oho,

Tsukuba-shi, Ibaraki-ken 305-0801, Japanx Kyoto University of Education, 1 Fukakusa, Fujimori,fushimi-ku, Kyoto-shi, JP - Kyoto 612-8522, Japany Oliver Lodge Laboratory, University of Liverpool,

P.O. Box 147, Oxford Street, UK - Liverpool L69 3BX, United KingdomJožef Stefan Institute and Department of Physics, University of Ljubljana,
SI-1001 Ljubljana, Slovenia
aa Department of Physics, Queen Mary, University of London,
Mile End Road, UK - London E1 4NS, United Kingdom
ab Lunds Universitet, Fysiska Institutionen, Box 118, SE - 221 00 Lund, SwedenacSchool of Physics and Astronomy, University of Manchester,UK - Manchester M13 9PL, United Kingdom
ad INFN Milano and Università di Milano, Dipartimento di Fisica,
via Celoria 16, IT - 20133 Milano, Italy
ae P.N. Lebedev Institute of Physics, Academy of Sciences,
Leninsky pr. 53, RU - 117 924 Moscow, Russia
a f Moscow Engineering & Physics Institute (MEPhI),
Kashirskoe Shosse 31, RU - 115409 Moscow, Russia
ag Lomonosov Moscow State University, Skobeltsyn Institute of Nuclear Physics,
RU - 119 992 Moscow Lenskie gory 1, Russia
ah Max-Planck-Institut für Physik, (Werner-Heisenberg-Institut),
Föhringer Ring 6, 80805 München, Germanyai
Nikhef National Institute for Subatomic Physics,
Kruislaan 409, P.O. Box 41882, NL - 1009 DB Amsterdam, Netherlandsa j Okayama University, Faculty of Science, Tsushimanaka 3-1-1, Okayama 700-8530, Japan
ak LAL, Univ. Paris-Sud, IN2P3/CNRS, Orsay, France
al Department of Physics, University of Oslo, Blindern, NO - 0316 Oslo 3, Norwayam
Department of Physics, Oxford University, Denys Wilkinson Building,
Keble Road, Oxford OX1 3RH, United Kingdom
an University of Pennsylvania, Department of Physics & Astronomy, 209 S. 33rd Street,
Philadelphia, PA 19104, United States of America
ao Petersburg Nuclear Physics Institute, RU - 188 300 Gatchina, Russia
ap Institute of Physics, Academy of Sciences of the Czech Republic,
Na Slovance 2, CZ - 18221 Praha 8, Czech Republic
aq Charles University in Prague, Faculty of Mathematics and Physics, Institute of Particle andNuclear Physics, V Holesovickach 2, CZ - 18000 Praha 8, Czech Republicar Czech Technical University in Prague, Zikova 4, CZ - 166 35 Praha 6, Czech Republic
as Rutherford Appleton Laboratory, Science and Technology Facilities Council, Harwell Scienceand Innovation Campus, Didcot OX11 0QX, United Kingdom
atUniversity of California Santa Cruz, Santa Cruz Institute for Particle Physics (SCIPP),
Santa Cruz, CA 95064, United States of America
au University of Sheffield, Department of Physics & Astronomy,
Hounsfield Road, UK - Sheffield S3 7RH, United Kingdom
av Insitute of Physics, Academia Sinica, TW - Taipei 11529, Taiwan
aw
University of Tsukuba, Institute of Pure and Applied Sciences,1-1-1 Tennoudai, Tsukuba-shi, JP - Ibaraki 305-8571, Japanz

2008 JINST 3 P08003–4–

ax INFN Gruppo Collegato di Udine and Università di Udine, Dipartimento di Fisica,

A BSTRACT: The ATLAS (A Toroidal LHC ApparatuS) Inner Detector provides charged particle
tracking in the centre of the ATLAS experiment at the Large Hadron Collider (LHC). The Inner
Detector consists of three subdetectors: the Pixel Detector, the Semiconductor Tracker (SCT), andthe Transition Radiation Tracker (TRT). This paper summarizes the tests that were carried out at the
final stage of SCT+TRT integration prior to their installation in ATLAS. The combined operationand performance of the SCT and TRT barrel and endcap detectors was investigated through a seriesof noise tests, and by recording the tracks of cosmic rays. This was a crucial test of hardware andsoftware of the combined tracker detector systems. The results of noise and cross-talk tests on
the SCT and TRT in their final assembled configuration, using final readout and supply hardware
and software, are reported. The reconstruction and analysis of the recorded cosmic tracks allowed
testing of the offline analysis chain and verification of basic tracker performance parameters, suchas efficiency and spatial resolution, in combined operation before installation.
K EYWORDS : Particle tracking detectors; Solid state detectors; Transition radiation detectors;
Large detector systems for particle and astroparticle physics.

∗ Corresponding Author.

c 2008 IOP Publishing Ltd and SISSA

http://www.iop.org/EJ/jinst/

2008 JINST 3 P08003

via delle Scienze 208, IT - 33100 Udine and
INFN Gruppo Collegato di Udine and ICTP, Strada Costiera 11, IT - 34014 Trieste, ItalyayInstituto de Física Corpuscular (IFIC), Centro Mixto UVEG-CSIC, Apdo. 22085, ES-46071
Valencia; Dept. Física At., Mol. y Nuclear, Univ. of Valencia and Instituto de Microelectrónica
de Barcelona (IMB-CNM-CSIC), 08193 Bellaterra, Barcelona, Spainaz University of British Columbia, Dept of Physics,
6224 Agricultural Road, CA - Vancouver, B.C. V6T 1Z1, Canadaba
University of Wisconsin, Department of Physics, Madison, WI 53706, United States of America
bb Yale University, Department of Physics,
PO Box 208121, New Haven CT, 06520-8121 , United States of America
1 Currently at Gaziantep University, Turkey
2 Currently at Dogus University, Istanbul
3 also at H. Niewodniczanski Institute of Nuclear Physics PAN, Cracow, Poland† Deceased
E-mail: heinz.pernegger@cern.ch

Contents
Introduction

2

2.

Experimental setup
2.1 Trigger
2.1.1 Barrel trigger setup
2.1.2 Endcap trigger setup

5
7
7
7

3.

Detector readout and event reconstruction
3.1 Detector control system
3.1.1 SCT DCS
3.1.2 TRT DCS
3.2 Data acquisition
3.2.1 SCT DAQ
3.2.2 TRT DAQ
3.2.3 Trigger and synchronization
3.3 Simulation and reconstruction software
3.3.1 Detector description
3.3.2 Simulation
3.3.3 Reconstruction
3.3.4 Conditions database
3.4 Monitoring
3.4.1 Event filter monitoring
3.4.2 SCT specialized monitoring
3.4.3 TRT specialized monitoring

8
8
9
11
13
13
13
14
15
15
15
17
20
21
21
23
24

4.

Tests with random triggers and calibration runs
4.1 SCT noise performance
4.1.1 Results from module calibration
4.1.2 Results from random triggers
4.2 TRT noise studies

26
26
26
27
33

5.

Tests with cosmic triggers
5.1 Alignment and calibration
5.1.1 Alignment of the SCT and TRT
5.1.2 Alignment of the SCT
5.1.3 Comparison with the photogrammetry measurements
5.1.4 Relative alignment between the SCT and TRT
5.1.5 Alignment of the TRT
5.1.6 Alignment summary
5.1.7 TRT drift time calibration

36
36
36
37
38
39
40
42
42

–1–

2008 JINST 3 P08003

1.

5.2
5.3

5.4

46
49
50
50
53
55
55
56

Conclusions57

1. Introduction
The ATLAS [1] tracking system, the Inner Detector (ID) [2], consists of three separate subdetectors:
the Pixel Detector, the Semiconductor Tracker (SCT), and the Transition Radiation Tracker (TRT).
Together they provide tracking of charged particles for momentum measurement as well as primaryand secondary vertex reconstruction in a pseudorapidity range of |η | < 2.5.1 The left-handed
coordinate system is defined with +x pointing horizontally to the LHC centre, +y vertically up,
and the z-axis along the LHC beam line. The Pixel Detector and the SCT are based on silicon pixel
and strip detectors as active elements. The TRT consists of many layers of gaseous straw tubes
which are interleaved with transition radiation foils. The layout of the Inner Detector is shown infigure 1. The figure shows one quadrant of the Inner Detector, in this case one quadrant of thebarrel and the top half of Endcap A. Detector elements in the ATLAS experiment are identified as“A” for sections on the +z side of the interaction point and as “C” for sections on the −z side of thethe interaction point. Each of the subdetectors consists of a barrel section and two endcap sections.
The radial and longitudinal envelopes are summarized in table 1. The entire tracker is surrounded
by a solenoid, which provides a magnetic field of 2 T with the field direction parallel to the beamaxis. A typical track generates three space points in the pixel detector, traverses eight silicon strip
detectors (to give four space points) and 36 straw tubes.
The SCT consists of four concentric barrels for tracking at mid-pseudorapidity and two endcaps which extend tracking to the forward regions. Each endcap is comprised of nine disks surrounding the beam axis. The overall layout parameters are summarised in table 2. The basic
building blocks of the SCT are the silicon strip modules for barrels and endcaps [3, 4]. In thebarrel the strip direction is approximately parallel to the solenoid field and beam axis; in the endcap the strip direction is radially outwards and perpendicular to the magnetic field direction. The
barrel sensors have a constant strip pitch of 80 µ m, the endcap modules sensors are wedge-shaped
and have variable pitch, which widens towards larger radii. Most modules consist of four siliconsensors [5]: two sensors on each side are daisy-chained to give 768 strips of approximately 12
centimetres in length. A second pair of identical sensors is glued back-to-back with the first pair
1 The pseudorapidity is defined as η = − ln [tan(θ /2)], where θ is the relative angle to the +z LHC beam axis.

–2–2008 JINST 3 P08003

6.

Tracking performance
SCT performance
5.3.1 Timing
5.3.2 Efficiency
5.3.3 SCT resolution
TRT performance
5.4.1 TRT efficiency
5.4.2 TRT resolution

Table 1. Radial and longitudinal envelopes of the Pixel, SCT and TRT detector sections.

Pixel
SCT Barrel
SCT Endcap
TRT Barrel
TRT Endcap

45.5 < r < 242 mm|z| < 3092 mm
255 < r < 549 mm|z| < 805 mm
251 < r < 610 mm
810 < |z| < 2797 mm
554 < r < 1082 mm|z| < 780 mm
617 < r < 1106 mm
827 < |z| < 2744 mm

at a stereo angle of 40 mrad to provide space points. The inner modules of the endcap disks areshorter and consist of just two sensors with the same stereo angle. The module strips are read out
by 12 radiation-hard ABCD3TA frontend readout chips [6]. The readout ASICs are integrated ona multi-layer Kapton-Cu flex circuit and are connected to the silicon strips by wire bonds. The
flex circuit is mounted above the silicon sensors in the barrel modules and at the end of the siliconsensors in the endcap modules. The hit information is “binary”: only the channel address of a hit
strip is read out optically from each module side to the counting room; no analogue information of
the signal is provided during normal running.
The TRT barrel contains up to 73 layers of straws interleaved with polypropylene fibres andthe TRT endcap consists of 160 straw planes interleaved with polypropylene foils, which provide
transition radiation for electron identification. In the TRT barrel the straw direction is parallel tothe beam direction, in the endcap the straw direction is radially outwards. The barrel TRT [7] isdivided into three rings of 32 modules each, supported at each end by a space frame, which is
the main component of the barrel support structure. Each TRT endcap [8] consists of 20 wheels
with eight straw layers per wheel. In the endcap, the straw axis is radial and the anode wire is–3–2008 JINST 3 P08003

Figure 1. Schematic drawing of one quadrant of the ATLAS Inner Detector.

Table 2. Layout parameters of the SCT Barrel and Endcap detectors.

silicon area [m2 ]SCT Endcaps
26.7
9 disks/endcap
3.0
1976
17
580
1.2 . |η | < 2.5
300 < r < 520

Table 3. Layout parameters of the TRT Barrel and Endcap detectors.

number of straw layers
number of readout channel
drift time resolution Xe [µ m]drift time resolution Ar [µ m]
pseudorapidity coverageradial coverage [mm]TRT Barrel
73105088130190|η | . 1.0
563 < r < 1066

TRT Endcaps
2× 1602×122880130190
1.0 . |η | < 2.0
644 < r < 1004

read out at the endcap’s outer circumference. Layout parameters of the TRT barrel and endcaps
are summarised in table 3. Each straw is based on a 4 mm diameter polyimide drift (straw) tube,
the cathode, and a 31 µ m diameter tungsten anode wire plated with 0.5-0.7 µ m gold. The wireis supported at both straw ends by end-plugs. In the barrel the anode wire is electrically split
in two halves by an insulator in the wire middle. In the barrel both ends of the anode wire are
directly connected to the frontend electronics and kept at ground potential, while the cathode tube
is operated at ≈ -1500 V. In the two endcaps only one end is read out. The TRT will operate in
the experiment at a gain of 2.5 × 104 with a gas mixture of 70% Xe + 27% CO2 + 3% O2 . For the
purpose of this test, the TRT was operated with argon instead of xenon as the active gas, due tothe high costs of xenon and complexity of recirculation and filtration of the active gas system. The
readout electronics [9] will measure the drift time of the primary ionization clusters to the anodewire and, by applying two thresholds, allow the separation of signals originating from electrons.
This paper describes the final tests which were carried out on the assembled SCT and TRT,
after they were integrated together to form the SCT+TRT barrel and endcap sections of the ID, andbefore they were installed in the ATLAS experimental cavern. The combined tests reported here
were carried out from April to June 2006 for the Barrel detectors and from October to December
2006 for Endcap C detectors. The tests mark the end of the integration process for the SCT and TRT
in the Inner Detector Integration facility at the ATLAS SR1 surface building. The pixel detector
followed a separate integration path in SR1, where the pixel barrels and endcaps were integrated
with the beam pipe and its support structure. The completed pixel detector was installed in the
ATLAS cavern after the mechanical installation of the SCT and TRT in ATLAS was completed.

–4–2008 JINST 3 P08003

number of layers
number of channels [106 ]number of modules
rφ resolution [µ m]z resolution [µ m]
pseudorapidity coverageradial coverage [mm]

SCT Barrel
34.4
4 barrels
3.2
2112
17
580
|η | . 1.2
299 < r < 514

2. Experimental setup
The tests described in this note were performed at the CERN Inner Detector Integration facility
at the ATLAS SR1 with the fully assembled SCT and TRT barrel and with one of the two SCT
and TRT endcaps. Specifically, the endcap which is now installed on “Side C” of the ATLAS
experiment was used.
Each SCT and TRT barrel or endcap was first assembled independently. For the SCT thisprocess was carried out at different assembly sites, while for the TRT the entire process was undertaken at SR1. The completed SCT barrel and endcaps were then inserted into the corresponding
TRT subdetectors.
For the insertion of the SCT into the TRT, the SCT was supported on a cantilever frame asshown in the foreground of figure 2 (left) for the barrel. Meanwhile the completed TRT was transferred into the final support and lifting frame, which was also used for the transport to the pit, andwhich can be seen in the background of figure 2 (left). The TRT was then guided on rails over the
SCT (shown in figure 2 (right) for the endcap). During the movement the alignment and concentricity of the subdetectors were repeatedly verified, as was the electrical isolation of the SCT, TRT
and support tooling. The clearance between the barrel portions of the SCT and the TRT is typically
1 mm. After the insertion was completed, the SCT was positioned on rails inside the TRT with amechanical precision of ≈250 µ m and a survey was carried out. The mechanical survey of the SCT
and TRT barrel shows a horizontal displacement of ∆x=0.3 mm and a rotation around the y-axis of
0.221 mrad of the SCT with respect to the TRT barrel.
For the final tests on the completed barrel, two opposite sectors of the SCT and TRT werecabled and tested. The connected sectors comprised 1/8 of the TRT and 468 modules, equivalent toapproximately one quarter, of the SCT barrel as shown on the right side of figure 3. For Endcap C–5–2008 JINST 3 P08003

Due to the integration and installation requirements for the ID, the pixel detector did not participate
in the tests reported here.
The goals of the combined tests with the SCT and TRT were to test the noise performance ofthe combined detectors, to verify the absence of cross-talk, and to obtain experience in combined
detector operation in preparation for pit commissioning activities. The performance checks consisted of a series of noise tests with random triggers and calibration-mode noise tests, followed bydata taking to record cosmic ray tracks in the SCT and TRT. The detector sections used for thetests were limited by the size of the test system available on the surface integration facility. In thetests approximately one quarter of both the SCT Barrel and Endcap C were powered and read out
together with one-eighth of the TRT Barrel and one-sixteenth of the TRT Endcap C. Other sectionsof the detectors were not cooled, powered, or read out.
The full ATLAS offline software was used to analyze the cosmic ray data. Key parts of thesoftware were tested during the combined operation. These software packages, such as the monitoring, the reconstruction chain, and the calibration and alignment algorithms, will also be used fordata taken with colliding beams. The reconstruction and analysis of the recorded tracks allowed us
to test the software chain and correct inconsistencies (e.g., in the detector description). The offline
software was also used to check if the performance of the detector (efficiencies and resolutions) arewithin specifications.

Figure 3. Left: photo of the ID Barrel setup for the cosmic test; right: the configuration of module groups
chosen for this test.

one quadrant of the SCT, consisting of 247 endcap modules, and 1/16 of the TRT Endcap Wheels
A and B were connected to the supply and readout system. “Wheel A” denotes the twelve 8-planestraw wheels closest to the barrel (labelled 1 to 12 in figure 1) and “Wheel B” the eight 8-planestraw wheels farthest from the barrel (labelled 1 to 8 in figure 1). Care was taken to emulate thefinal setup in the pit, in particular for the service routing and detector grounding. Detectors andsupport structures were electrically isolated from each other. The detector grounds were connected
together in a star-point configuration similar to the one in the final configuration.
After the completion of the combined test of the SCT+TRT barrel detectors, they were installed
as one package in the ATLAS cavern in August 2006. The SCT+TRT sections of endcap A were
installed in the ATLAS cavern in May 2007 and the SCT+TRT sections of endcap C in June 2007.

–6–2008 JINST 3 P08003

Figure 2. Insertion of the SCT barrel into the TRT barrel (left) prior to their combined test in SR1. The rightphotograph shows the insertion of the SCT endcap into the TRT endcap before the SCT+TRT endcap were
tested together.

2.1 Trigger
The following scintillator setups were implemented to record cosmic rays in the active barrel andendcap portions of the SCT and TRT.
2.1.1 Barrel trigger setup

2.1.2 Endcap trigger setup
For the endcap setup, two of the three scintillators from the barrel setup were reused and placed
on top of the endcap while a third scintillator of size 50 cm × 60 cm was placed vertically in frontof the endcap as shown in figure 6. This layout balances the competing demands of selecting longtracks in the endcaps and having an acceptable rate of tracks. Information about the time-of-flight
was not available in this setup. The trigger rate for coincidence of the endcap trigger scintillators
was 0.7 Hz. Due to the low scintillator trigger rate additional data samples were recorded using
TRT endcap frontend signals to enhance the track sample in the SCT for SCT studies at a trigger
rate of 25 Hz. The scintillator trigger resulted in a rate of reconstructed tracks of 0.17 tracks/s, theTRT frontend trigger in a rate of 1.38 tracks/s. A total of 2.5 million events were recorded for the
endcap test.
2 Eljen Technology, PO Box 870, 300 Crane Street, Sweetwater TX 79556, USA–7–2008 JINST 3 P08003

For the barrel setup, cosmic rays were triggered using three layers of Eljen EJ-200 scintillators2 ofdimension 144 cm × 40 cm × 2.5 cm, as shown in figure 4.
The three scintillators were arranged so that a wide angular distribution of the cosmic raymuons would hit the scintillators as well as the instrumented sectors of the TRT and SCT. One of
the scintillators was placed above the detector (HSC1), one of them below (HSC2), and another
under the concrete floor (HSC3). The cosmic trigger was formed by looking for coincident hits
in the top and middle layers. The output of the third scintillator was also recorded to allow for an
optional offline energy cutoff of around 170 MeV (corresponding to a 15 cm thickness of concrete)by requiring an additional coincidence with the third scintillator.
The time of an output pulse emerging from one of the two Hamamatsu R2150 phototubes attached to both ends of the scintillators may differ from the other by a few nanoseconds dependingon where the particle intersects the scintillator. Therefore, a LeCroy Model 624 Octal Meantimer
was used to equalize the photon transit time by providing an output pulse at a fixed time, independent of where the impact occurred. In this way a 0.5 ns time resolution was achieved in thetime-of-flight measurement. The scintillator coincidence rate, which was used to trigger the detector readout, was 2.4 Hz. A total of 450000 events were recorded for the barrel test.
Figure 5 shows that the time-of-flight is independent of the energy of the cosmic muon forenergies above 600 MeV. The time-of-flight from the top scintillator (HSC1) to the bottom scintillator (HSC3) was used to cut away noise events (i.e., fake triggers) which can be recognized by anunphysical value for the computed time-of-flight. Other than in the rejection of noise triggers, thescintillator data were not used to select specific event samples. No momentum selection, based on
scintillator data, was carried out during the offline analysis.
In addition, the charge deposited can be measured. This is important for ensuring that theevent has been triggered by a charged particle. The charge resolution is on the order of 100 fC.

[cm]

400
HSC1

300
8

7

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concrete floor

-100
HSC3

-200

-200 -150 -100 -50

0

50

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150 200[cm]

Figure 4. The boxes marked as “HSC1”, “HSC2”, “HSC3”, one above the Inner Detector and two below,
represent the scintillators used for trigging on cosmic rays in the barrel region. Each scintillator was readout with two photomultipliers, one at each end. The hatched box just below HSC2 represents the concrete
floor.

3. Detector readout and event reconstruction
3.1 Detector control system
The Detector Control System (DCS) serves two purposes during detector operation. It allows–8–2008 JINST 3 P08003

200

15

time of flight [ns]22

20

ToF top-bottom 5.5m
18

16

14

ToF top-middle 3.9m

12

500

1000

1500

2000

2500
3000
Energy [MeV]

Figure 5. Theoretical time-of-flight for a muon from top to middle (dashed line) and bottom (solid line)
scintillators.

z [cm]y [cm]

sideview (view from interaction point)400
trolley350

detector
scintillator

6

7

8

9

10

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14

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26 25 24 23

50

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concrete floor-200 -150 -100 -50

050

100 150 200

x [cm]-50-200 -150 -100 -50

050 100 150 200

x [cm]

Figure 6. The green boxes show the top two scintillators with their corresponding support structures (blue)
and the vertical scintillator.

control of the powering of the detector and ensures detector safety by monitoring several parameters
and taking action based on their values.
3.1.1 SCT DCS
For the SCT tests in SR1, the DCS system was divided into two main parts: the Power Supply
Control and the Environmental Monitoring System.
The largest part of the SCT DCS [12] was the power supply system. Each detector module waspowered and controlled by two independent and floating low voltage (LV) power supplies. These
were used to power the readout chips and the opto-communication circuits. The high voltage (HV)
supplies provided the depletion voltage for the sensors. Figure 7 shows the digital and analogue–9–2008 JINST 3 P08003

10

number of modules

80
Mean 5.535

70

RMS 0.1186

60
50
40

20
10
0
4

4.5
5
5.5
6
6.5
module analog+digital power [W]7

Figure 7. Electrical power consumption for the active SCT barrel modules during the combined test.

power consumption for the barrel SCT modules. In addition, the LV supply took care of poweringand reading out Negative Thermal Coefficient (NTC) temperature sensors located on the hybrid(two sensors, one on each side of the module, for the barrel and one sensor per module for the
endcap). These parameters were read out using a CAN bus [13] by a DCS computer running a
PVSS project [14]. The Power Supply DCS also used a Distribution Information Management(DIM) [15] system to allow a direct communication between DAQ and DCS systems (DDC). The
DAQ could then operate the power supply system and receive information about the actual state of
the system. The full data set available from the DCS systems was recorded and a subset of thosedata, the one related to module operation, were transfered into the ATLAS database system usedby DCS (Oracle and COOL). From there the module-operation related data (e.g. voltages, currents,
temperatures) is available for checks of the detector operation status and for offline analysis.
The second part of the SCT DCS was the environmental monitoring system. This measured
the value of temperature and humidity sensors distributed in the detector. Three types of temperatures were measured with NTC-type temperature sensors, which are permanently mounted on thedetector. Two sensors are located at the exhaust of each cooling pipe of the whole detector. Other
sensors are attached to the mechanical structure of the detector to monitor the possible deformation due to temperature changes. Finally, temperature sensors are located next to the Frequency
Scanning Interferometer (FSI) [16] on-detector components to measure the gas temperature insideof the detector volume. In addition, radiation-hard Xeritron3 humidity probes are installed on thebarrel and endcaps. Combined with a temperature measurement, they are used to calculate the dew
point inside of the thermal enclosure. Condensation on the silicon modules was avoided by actively
3 XN1018 Xeritron series, Hygrometrix Inc. Alpine CA 91903, USA.

– 10 –

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30

3.1.2 TRT DCS
The DCS of the TRT was set up to control and monitor the hardware components that are essential
for correct operation of the detector. There are five basic groups of such components:• Low Voltage power supplies - to feed the frontend and active patch panels electronics• High Voltage power supplies - to bias the gaseous detectors
4 During operation in the experiment the SCT will be supplied with nitrogen as environmental gas.

– 11 –

2008 JINST 3 P08003

monitoring the dewpoint of the SCT environmental gas. We used dry-air as environmental gas during the tests reported here, which was supplied through filtration and de-humidification stages witha dewpoint of about -45 ◦ C.4 The gas was supplied at room temperature (typically around 19 ◦ C),
without pre-cooling and at a flow rate of 30 l/min to the detector. The environmental gas is thendistributed uniformly inside the active detector volume by ducts or pipes in the vicinity of the (cold)cooling pipes.
The SCT detector was cooled using an evaporative C3 F8 cooling cycle [17]. The coolingplant was constructed as a prototype system for the final Pixel and SCT cooling system and usedduring the macro-assembly and integration of the SCT detector in SR1. For the combined test
described here, a total of twelve cooling loops were operated simultaneously in the barrel and ninecooling loops in the endcap. The cooling temperature was higher than the final operation coolingtemperature (approximately -25 ◦ C) due to the constraints of the environmental dewpoint near thepipes leading from the detector to the cooling regulation rack in the test setup. Cooling pipe temperatures were approximately +12 ◦ C during the barrel tests and approximately +5 ◦ C during theendcap tests. This results in hybrid temperatures of +24 ◦ C to +28 ◦ C. The temperature sensors,which measure these hybrid temperatures, are mounted close to the hottest point of the module.
Sensor temperatures are not directly measured, however FEA simulations and measurements suggest that the hottest point on the unirradiated sensors is approximately 6 ◦ C warmer than the coolingtemperature for barrel modules [3] and approximately 8 ◦ C warmer than the cooling temperaturefor endcap modules [4].
The cooling pipe temperatures, as measured by the environmental system, are also used totrigger the interlock system. The main purpose of the interlock system is to protect the silicon detector modules from overheating if the cooling stops. The system is fully implemented in hardware
for maximum reliability. An Interlock Box (IBOX) [18] compares the temperature sensor value
to a threshold and sends a signal to the appropriate power supply crate to turn it off in less than
one second if the threshold is reached. The mapping of the sensors to the power supply crates ismade through an interlock matrix, which maps the temperature sensor to the relevant power supply
channels.
Since the beginning of the macro-assembly activities, the DCS system has evolved to copewith changing requirements from the different detector testing phases. The system showed good
performance and stability and, in cases, protected the detector during failures of supply systems
or operational errors. The final SCT DCS also has a Finite State Machine (FSM) which handles
the transmission of state information, adds high level safety checks, and oversees the passing ofcommands to the hardware from the ATLAS central DCS.

• Temperature sensor - for checks of the FE electronics operation temperature• Cooling system - C6 F14 cooling of the FE electronics• Active gas system

5 W-IE-NE-R, Plein & Baus GmbH, Müllersbaum 20, D - 51399 Burscheid, Germany
6 CAEN S.p.A., Via Vetraia, 11, I-55049 Viareggio (LU), Italy– 12 –

2008 JINST 3 P08003

Only the temperature sensors mounted on the detector parts (cooling plates, electronics boards,
modules shells) were of the final type. All other systems consisted of the hardware available atthe time. While the interfaces to the hardware were specifically built for the SR1 setup, the higher
layers of control software were prototypes of the final ATLAS system, hence the setup was used toqualify the different procedures and actions of the final system. The TRT was operated during thetests in SR1 with an active gas mix of Ar:CO2 in the ratio of 70%:30%.
Three PL500 power crates manufactured by Wiener Plein & Baus Electronics5 were used toprovide low voltage to the TRT. They contained eight channels each allowing us to create an LV
partitioning similar to the final architecture planned for the experiment. The control of the crates
was done using the CAN bus as the physical layer and using the OPC server as a higher-level
control. The output of the power supplies (analogue positive/negative and digital voltages) fed
the LV patch panels (LVPP), which in turn distributed voltage lines to the individual electronicboards on the detector. The LVPP boards are controlled remotely via another CANbus branch
and the CANOpen OPC protocol. The LVPP boards contain a standard ATLAS control module,
ELMB [20], which serves as the measuring instrument for currents and voltages in individual lines
with its analogue segment, and as the communication driver, allowing the setting of the DACs forvoltage adjustment and a way to switch the regulators on or off via digital ports.
A CAEN 1527 system along with four 1833 boards6 was used to provide high voltage to the
TRT straws. The software was built with the use of the CERN JCOP FrameWork toolkits [19].
The setup allowed for the control and monitoring of voltages and currents and was also capableof performing predefined actions on HV channels or modules consisting of several consecutive
operations. For operation in SR1 we used a typical HV of -1500 V applied to the straw tubes.
The TRT detector is equipped with several temperature sensors that monitored sensitive pointsof the apparatus. The temperatures of frontend electronics boards are monitored by NTC sensors,
while cooling elements and detector mechanics are monitored with PT1000 probes. The signals are
fed to the ELMB module, which is read out via the CANbus and CANOpen OPC server. Limits are
set on the temperature values, with the system configured to give a warning after crossing the firstthreshold and to switch the low voltage power supplies off when a dangerous temperature limit isreached.
For the TRT setup in SR1 the cooling unit was controlled via the local programmable logic
controller (PLC) and the control DCS software had only monitoring functions. However the status of the unit was coupled to the low voltage system through a hardware-based interlock system,
allowing the powering down of the detector in the case of a cooling failure. For the barrel a temporary interlock system using several NTC temperature sensors, which monitored on-barrel electronic temperatures, was connected to special comparator logic which switches the power supplies
off when the temperature threshold is exceeded.

3.2 Data acquisition
3.2.1 SCT DAQ

3.2.2 TRT DAQ
The TRT data acquisition system was composed of two software tools: XTRT [24] for calibration and parameter tuning, and the TRT DAQ for the collection and storage of data generated by
an external trigger source. Each tool was responsible for coordinating the actions of the frontend
electronics, which are mounted at the end of the straw chambers, and the “backend” electronics,
prototypes of two different 9U VME modules located off-detector. A series of patch panels mediated the communcation between the frontend and the backend. They provided signal compensation
and replication for signals going to the frontend, and further compensation for signals returning to
the backend.
The TRT makes use of two custom ASICs on the frontend electronics to detect both minimum
ionizing particles (low threshold) as well as transition radiation (high threshold). The analogue
7 Common Object Request Broker Architecture define by the Object Management Group enables software compo-

nents written in multiple computer languages and running on multiple computers to work together.

– 13 –

2008 JINST 3 P08003

Control, readout, and online calibration of the SCT were performed using the SctRodDaq [21],which was developed within the ATLAS TDAQ software framework [23]. Multiple c++ and Javaapplications can communicate across multiple processors using CORBA.7 The hardware part of
the SctRodDaq system in SR1 was comprised of several PCs, a 6U VME crate for Trigger Timing
Control (TTC) specific modules, and a 9U VME64x crate housing 12 Readout Driver Modules(RODs) [25], 12 Back of Crate cards (BOCs), and a Timing Interface Module (TIM). The RODs
are used for the main control and data handling, whereas the BOCs provide the ROD interface tothe frontend modules and to the rest of the ATLAS DAQ chain. The TIM provides the interface to
the ATLAS TTC system. Each ROD/BOC pair is connected optically to up to 48 SCT modules,
and to a single Readout Supervisor (ROS) PC.
The SCT uses a binary readout architecture based on the ABCD3TA chip [6] whereby a hit isregistered if a pulse height from the silicon microstrip detector channel exceeds a preset threshold.
SctRodDaq operates in two modes. For calibration of the SCT modules, triggers are generated
internally by the RODs or by the TIM and sent to the modules. Known test charges are injected by
the frontend chips and the occupancy (the fraction of triggered events for which the pulse heightexceeds the threshold) is determined as a function of threshold. The resulting threshold scan data
are extracted from the RODs via VME readout and analysed online to generate an optimised calibration, which is then applied for cosmics or physics data taking. For running in physics or cosmics
mode, the triggers (originating from collisions or cosmics) are sent to the frontend chips via the
TTC system. Hit information arising from noise or cosmics are channeled event-by-event to the
ROS for subsequent event building. The data are then analysed offline or online for track reconstruction and tracking efficiency. For the tests presented here, we recorded for each hit the information in the triggered bunch-crossing cycle plus the hit information of the bunch-crossing cycle
prior to and immediately after the trigger bunch crossing cycle. This “hit pattern” is then presented
in its binary form as e.g., “011” for a signal recorded in the trigger time bin (second bit), none inits preceding clock cycle, and a signal above threshold in the cycle after the triggered time bin.

3.2.3 Trigger and synchronization
The trigger system used for both noise tests and cosmic ray tests was based on the ATLAS TTC
system hardware. This hardware facilitates the distribution of clock, reset, and trigger signals to
one or more subsystems via fibreoptic links between a TTC Crate and a Readout Driver Crate. Each
TTC “partition” contains hardware sufficient to trigger some subset of a single subsystem. Different
partitions can be daisy-chained together by connecting the master components of each partition,
called the Local Trigger Processors. In the SR1 system tests, there were separate partitions for the
TRT and for the SCT. In this case, one partition acted as the master, and the other as the slave. The
distinction between master and slave was made in software, so the same cable connections could
be used in either configuration.
To test systematic noise effects introduced by either subsystem on the other, two different noise
triggers were used. The first was a trigger at a fixed frequency (variable from a fraction of a Hz toseveral MHz), while the second was a pseudo-random pulser, the average frequency of which was
tunable between several Hz and several kHz. Neither trigger was synchronous with the 40 MHz
clock used by the subsystems, so a synchronization of the trigger signal was performed in the TTC
crate. For noise data, the phase of the trigger with respect to the clock was not monitored.
For tests with cosmic rays, the trigger was generated in one of two different ways. For thebarrel tests, the scintillators described in section 2 provided a coincidence trigger designed to illuminate the active readout regions. For the endcap, in addition to the scintillator trigger, the TRT– 14 –

2008 JINST 3 P08003

ASDBLR chip [26] provides a ternary output corresponding to three states: below threshold, lowthreshold crossed, low and high threshold crossed. The analogue output currents are sent to acompanion DTMROC chip [27], which digitizes the analogue data and, upon receipt of a trigger,
returns the data for 16 straws to a ROD backend module.
The XTRT program was used extensively for testing of prototype and production frontend
electronics. The tests included scans of timing and threshold parameters, the injection of test
pulses to test the response of the frontend to known signal shapes and amplitudes, and the use of an“accumulate mode” in the digitizing chips to test the integration of the electronics with the gas andhigh voltage systems. The results of all tests, as well as a record of the hardware used in each test,
were stored in a MySQL database, which provided read-only access to a web interface for easybrowsing of the results.
While XTRT was designed to track the response of the hardware as various parameters were
scanned, the TRT DAQ was designed to take data at a fixed point in parameter space. The TRT
DAQ is a collection of c++ libraries and database objects, which are used to initialize and run
an instance of the ATLAS TDAQ software framework [23]. The TRT DAQ is configured via a
database, which contains instructions for all of the necessary ATLAS DAQ infrastructure as well
as specific parameters used for running the TRT back- and frontend electronics. The TRT specificparameters were extracted from the database which held the results of the XTRT scans.
Because the TRT DAQ was an instance of the ATLAS TDAQ framework, it scaled easily tocope with the amount of readout hardware available. For the barrel and endcap tests, the system
was composed of two 9U VME crates for TRT backend hardware, one 6U VME crate for ATLAS
TTC hardware, a PC dedicated for event building and storage, and a PC used to control the TDAQ
infrastructure.

3.3 Simulation and reconstruction software
The offline software infrastructure developed in ATLAS to simulate and reconstruct the LHC data
was used for the first data taken by the Inner Detector in SR1. Thanks to the very flexible underlying
ATHENA [28] framework, the necessary adaptations in the algorithms could be easily integrated.
These adaptations were required in order to take into account the fact that particles are not producedin the middle of the detector and that they are not synchronized with the readout clock, but randomly
generated.
3.3.1 Detector description
The detector description is a common service that provides the geometry (including alignment
corrections) and material information. It is used in the simulation to create the Geant4 [29, 30]
geometry and to emulate the response of the electronics. In addition, it is used by the different
steps of the reconstruction. A specific detector description was created for the SR1 setup with nomagnetic field.
For track fitting purposes, a simplified version of the geometry was created from the same
service. This tracking geometry, which is the result of simplifying the material description intolayers and volumes, allows for fast navigation. However, no direct measurement of the momentum
can be done from the SR1 reconstructed tracks due to the lack of a magnetic field and therefore
material effects cannot be taken into account during tracking, leading to an underestimation of theuncertainty of the measured track quantities.
3.3.2 Simulation
A simulation of cosmic ray events for the detectors as set up in SR1 was provided in order tofirst test the full reconstruction chain before data taking started and later to compare the data withsimulated events.
The data flow of the simulation (figure 8) contained the following steps:– 15 –

2008 JINST 3 P08003

self-trigger functionality was exploited. In this mode, the digital chips on the TRT frontend provided a trigger if any of their associated straw channels went over threshold. These triggers werecollected by the TRT-TTC module, and used to create a multiplicity based trigger for the two subsystems. The efficiency and fake rates were almost entirely determined by the selection of frontend
thresholds, so that it was possible to obtain a reasonably pure and efficient trigger.
In the case of cosmic ray triggers, unlike the case of noise triggers, the phase of the asynchronous trigger signal with respect to the clock plays a crucial role. The trigger must be synchronized with the 40 MHz clock before it reaches the frontend electronics, so a measurementof this phase must be made before the data are collected. To determine this phase, two methods
were employed. In the first, the TRT-TTC module was given a trigger delay measurement (TDM)
module, which received an asynchronous trigger signal and stored the resulting measurement in
the TRT event fragment written to disk. In the second method, a TDC was used to measure thephase of the scintillator trigger, the results of which were stored in an independent event fragment.
Both methods were limited to scintillator triggers; no phase measurement was possible for the TRT
self-trigger.

p.d.f6

10

Mean = 6480
RMS = 1.423e+04

5

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0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
cos θ

Figure 9. Energy and angular distributions of the generated muons.

• Generator
A cosmic Monte Carlo generator was used for generating the incoming muons from cosmic
ray events in the simulation. It generates single muons with a position and momentum vector from a user-defined spatial horizontal window. The generated muons have energy andpolar-angle θ distributed as shown in figure 9. These are based on measurements of thedifferential vertical muon cross section [31] and analytical calculation ([32] and references
therein) extrapolated to low muon energy. The cosmic generator used for this simulation
generates single muons but no showers. No corrections were made for the test location on
the surface (elevation, latitude, or building roof). The concrete floor below the detector andabove trigger scintillator HSC3 was however included in the simulation.
The generator was set up to generate muons in a window right above the upper scintillator.
To improve simulation speed, their direction was restricted so that they would go through theacceptance of the middle or vertical scintillator, for the respective barrel and endcap setups.
• Simulation
The simulation toolkit Geant4 was used in order to simulate the passage of particles throughthe detector. Geant4 receives the muon position and momentum vectors from the generator– 16 –

2008 JINST 3 P08003#entries / 20 MeV

Figure 8. Cosmic ray simulation data flow.

and propagates the particle through a very detailed three-dimensional model of the detector,
simulating the energy depositions throughout the detector.
• Trigger
A trigger algorithm was used to select those events which generated hits in both upper andmiddle scintillators (Barrel setup) or vertical scintillator (Endcap setup), respectively, andonly those were written to disk.
• Digitization

The SCT digitization algorithm is described in ref. [33]. The algorithm loops over the deposited energy in the detector elements and determines if the collected charge would pass thecomparator threshold of 1 fC. During the digitization a uniform noise level of 1500 electrons
ENC is added to the simulated signal data of all individual channels. It is also time-aware,in the sense that it integrates charge collection over one bunch crossing. Charges deposited
after this are not taken into account for the hit decision procedure.
The SCT digitization is interfaced to the conditions database for determining which modules,
chips, and channels should be enabled for a given run number. Measured detector characteristics like channel specific noise and gain as well as configuration parameters such as appliedbias voltages and thresholds are also available from the conditions database, but those were
not used in this test.
Concerning the TRT, the digitization process simulates the drift of charges from ionizationof the gas to the central wire, and records the drift time and time over threshold. It also
adds the additional time introduced by the propagation from the impact point on the straw
to the readout at the end of the straw. In addition, it simulates noise modeled from ATLAS
Combined Test Beam (CTB) data [34]. To account for the use of argon instead of xenon in
the combined tests, drift times were reduced by a factor of two-thirds and the detection ofhigh-signal hits was disabled.
The default ATLAS TRT digitization assumes that the particles originate from the interaction
point, and for each straw there is a time offset T0 taking this into account. However, in cosmic
ray events, the particles enter the top of TRT and leave through the bottom, so a different
timing scheme was applied.
3.3.3 Reconstruction
A diagram representing the data flow of the reconstruction chain is shown in figure 10. It is designed to work with both simulated and real data provided that for the real case the data given out
by the detectors are decoded and the corresponding RDOs created. The different steps of the chain
are described below in more detail. The inner detector and tracking Event Data Model classes aredescribed in more detail in [35] and [36].

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The digitization converts the simulated deposited energy to the Raw Data Objects (RDOs),
which form the input objects to the reconstruction. It simulates effects such as the finite readout resolution of the detectors and the electronic response. During this process the detector
elements which were not read out were masked.

• Byte Stream converters
The byte stream converters are responsible for decoding the different ROD event fragments
and creating a raw data object (RDO) for each recorded signal. The result is an SCT RDO
per consecutive group of strips or an individual strip that had a signal (depending on whether
the ROD is configured to run in “condensed” or “expanded” mode), and a TRT RDO per hit
straw. All the information written by the different detectors describing the signal is stored in
the RDO objects.
The converters use the ATHENA cabling service to map the ROD channel numbers to thecorresponding detector elements.
Since the cosmic trigger was not synchronized with the 40 MHz readout clock the timedifference between the trigger and the next clock edge is also stored in the TRT RDO in thecase of the barrel setup to allow for a drift time correction.
Specific converters and RDO classes were developed in the same framework in order to store
the ADC and TDC information produced in the different scintillator photomultipliers.
• Data preparation
The raw data objects created by either the simulation or the real detectors are translated into
positions in space using the detector geometry information and calibrations.
For the SCT, clusters containing one-dimensional measurements in the sensor plane are produced. The stereo angle between the two different sides of a layer is used to form twodimensional measurements, which combined with the known radial positions of the layers
determine space points.
For the TRT, the drift time information is translated into the corresponding radius using
the R-t calibration curves, parametrized by a third degree polynomial. For simulations, theparametrization is an integrated part of the reconstruction software and depends on the T0
configuration used in digitization. For real data the calibration constants are provided by theprocedure described in section 5.1.7.

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Figure 10. Cosmic ray reconstruction data flow.

• Track finding and fitting in the barrel setup
Since there was no magnetic field in the cosmic setup, the muons moved in straight lines
through the detector. Also, the tracks do not originate at the centre of the detector. For
the barrel test, the CTB tracking program [33], which was developed with such scenarios in
mind, was used as default track finder. Straight line tracks are used as the fit model, meaning
four track parameters to fit: φ , the angle in the transverse plane, θ , the angle with the z axis,
d0 , the point of closest approach to origin in the transverse plane, and z0 , the intersection
point with the z axis.

The pattern recognition in the SCT works with space points, and in the cosmic rays configuration it looks for at least three space points on a line. If a good combination is found, thetrack is extrapolated to modules with no space points, and eventual clusters close to the trackin these modules are added. The track can then optionally be extrapolated to the TRT, where
hits close to the track are added. The pattern recognition can also work in standalone TRTmode without an SCT seed. The number of hits on a line is then required to be at least 20.
By default, tracks provided by the CTB tracking are fitted by a global χ 2 fitter, and canoptionally be refitted by a Kalman filter technique that is being used by default in the Inner
Detector reconstruction. Since the momentum of the incoming muons cannot be measured
with the SR1 setup, material effects are not taken into account in the fit.
• Track finding and fitting in the endcap setup
The CTB tracking was developed specifically for a barrel-like geometry. It was extended
to deal with hits in the SCT endcap but not with hits in the TRT endcap. A different trackfinding strategy was therefore used for the endcap setup, where the CTB tracking was onlyused to find SCT only tracks.
For the TRT endcap, a dedicated pattern recognition algorithm had to be developed as the
ATLAS default pattern recognition assumes that tracks originate from the interaction point,
which is in general not the case for the cosmic muons. As the TRT endcap does not provide
a measurement of the radial position of a hit, the pattern recognition only provides TRT
segments within the z − φ plane. These TRT segments are then fitted with a Kalman filter
assuming the direction as given by the scintillator layout in order to provide full tracks.
In a second step, the TRT-only tracks are then fed into a backtracking algorithm that starts
with the TRT track and tries to extend it into the SCT and collect all matching hits. In
addition, another algorithm combines SCT-only tracks found by the CTB tracking and the
TRT-only tracks.
In the final step, ambiguities that arise due to the different ways a track can be found areresolved and only the best candidate is kept.
• Output data
The output of the reconstruction can be stored in several formats: An ntuple containing
information from tracks, SCT clusters, TRT drift circles, raw data from detectors, and TDC– 19 –

2008 JINST 3 P08003

CTB tracking consists of a pattern recognition part, which is possible to seed from either
SCT or TRT hits, and a track fit part based on a χ 2 minimization method.

and ADC data from the scintillators. The data can also be stored in an ESD (Event Summary
Data) format, where the full objects (TrackParticles [35], Tracks and associated hits) arestored. A third option is to write data in an XML [38] format used by the standard ATLAS
event display Atlantis [37].
3.3.4 Conditions database• DCS Conditions
DCS conditions are measurements made of the temperatures, voltages, and currents by the
ATLAS DCS system PVSS [14]. Environmental factors such as temperature, humidity, andmodule power parameters were recorded in PVSS at regular intervals and subsequently uploaded to the COOL database to be made available to the offline analysis.
• Configuration
The SCT is configured from an XML file containing all the data acquisition configuration(including cabling), the power supply configuration, and the individual module and chip
configurations. The configuration files for each run were transformed via XSLT [39] to aform appropriate for uploading to the COOL database, and were subsequently used in theanalysis to identify, for example, masked channels.
• Calibrations
The calibration data are taken as part of a dedicated calibration run and used to determine
the noise levels of the modules and optimal noise settings. The calibration is “published”
to the online information service and subsequently uploaded to COOL using the Conditions
Database Interface (CDI). For the TRT, calibration data includes the drift R − t relationships
for the straws.
• Data Stream Conditions
The data stream itself contains information about errors arising from the modules or their
RODs. This can reveal a faulty communication channel and thus determine whether the data
stream itself is trustworthy.
• Data Quality
As a result of analysis, online or offline, it may be determined (by recording the channel
efficiency) that a channel or module has become noisy during the run, or is no longer giving
data. The data monitoring algorithms write this information to a local database for use during
the interpretation of the data. This local database can subsequently be merged with the main
COOL database for use in all further analyses.

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In this context, Conditions are those factors which will influence the datastream interpretation.
The ATLAS standard database for the storage of conditions is the COOL database, which represents a technology independent schema for the storage of conditions. This permits their retrieval
by run/event number or by timestamp during the analysis. The various conditions data used aredescribed below:

• Alignment
The alignment data were stored using a different schema and allow precise location of thespace points from hits. This subject is treated more fully in the Alignment sections.
3.4 Monitoring
3.4.1 Event filter monitoring• Event Monitoring (Emon)
Emon provides event sampling. User programs can request event fragments from a specificsampling source.
• Online Histogram Service (OHS)The OHS handles histogram objects and in particular ROOT [42] histograms. It is used
to share information among histogram providers and subscribers. The functionalities forproviders are: create, update, and delete. Subscribers can subscribe to a particular histogramin OHS and be notified about a change in its state.
• HistoSender
HistoSender is a service of the High Level Trigger (HLT) infrastructure that collects histograms from the ATHENA histogram service and publishes them in the OHS.
• Online Histogram Present (OHP)
OHP [43] is the ATLAS histogram presenter based on QT [44] and ROOT. It can browse
histograms published in OHS and display them in automatically updating tabs with userdefined graphical options.
The EF segment, controlled remotely by the DAQ system, runs on a dedicated server so that
PT processes do not share resources with other DAQ subsystems. The server also hosts a replica of
the offline, online, and HLT software in order to reduce as much as possible the startup time of the
EF segment.
During SR1 commissioning, only a part of the ATLAS TDAQ system was in place, as shownin figure 11. In particular, there was no SFI during SR1 data taking, so that event data were passed– 21 –

2008 JINST 3 P08003

A monitoring system is needed in the data acquisition system to assess the quality of the data sent
to permanent storage. In the ATLAS Data Acquisition and Data Flow system, the Event Filter (EF)is the third level of the trigger process, receiving completely assembled physics events from theSub-Farm Input (SFI). Data are transmitted by the Event Filter Data Flow (EFD) to the Processing
Task (PT), where the trigger algorithms run.
The EF is therefore the natural place to perform the monitoring of high level physics quantities,
and cross-checks among different detectors. A key feature of the EF monitoring is its capability ofproviding data quality checks even before data is stored to disk.
The EF monitoring is based on a monitoring framework [40] that is provided by the ATLAS
Trigger and Data Acquisition (TDAQ) system. Fundamental services provided by the monitoring
framework [41] are:

Detector
Data / ROD

to the PT not by the EFD, but via the Emon service from the Event Builder (EB). The PT is
synchronised with the DAQ transition states via the EF Process Steering Control (EF PSC), part of
the HLT interface.
Subdetector and global monitoring. The inner detector monitoring tools are all implemented
as ATHENA algorithms, and hence are run offline and as online Athena PT processes, as describedabove. There are separate SCT and TRT packages, and a global tool that aims to examine issues ofsynchronization.
The SCT Monitoring tool plots hit maps at both the module and strip level as well as monitors
module and track efficiencies, cluster sizes, hit correlations between module sides and between
layers, track residuals and pulls, tracking hit information (such as number of hits on a track, trackχ 2 , etc), byte stream errors, and noise occupancies. The software provides a number of algorithms
for monitoring detailed detector information, and is commonly configured to produce a subset of
the total information for online use due to the more limited resources in the online environment.
Full details of the package development are contained in Reference [45].
The TRT monitoring tool produces histograms that show timing-related information for the
straws like the measured drift time in the straws or the time-over-threshold for low level and highlevel hits. Further histograms include plots of the R − t relation (drift distance versus drift time– 22 –

2008 JINST 3 P08003

Figure 11. Schematic description of the EF segment as used during SR1 tests.

in the straw) for track hits, number of low-level and high level hits on a track as well as the track
residual, which is the difference between measured and predicted hit positions. Another set of
histograms monitors geometric quantities like the track’s impact parameter or its azimuthal angle.
Additionally, various straw, noise, and track hitmaps are available.
The list of histograms produced by the global monitoring tool includes the synchronization
between the SCT and TRT readout, the ∆φ of the perigee parameters of the SCT and TRT versus
event number (see, for example, figure 12), the mean number of SCT and TRT segments, thenoise occupancy of the SCT and TRT, and the number of hits found on global tracks. Figure 12
shows the difference of azimuth angle of track segments in the SCT and TRT versus event number,which is used to monitor the synchronization of readout between the subdetectors. It is clearlyvisible that the synchronization between the readout of SCT and TRT subdetectors was lost around
event 7600 and the run was stopped. The example shown in figure 13 gives the variation of thenoise occupancy on the TRT Endcap C with the event number. In this case, the noise occupancy
increased from around 2% to 6% due to a trip of the analogue low voltage regulators. The global
monitoring is also capable of extrapolating tracks from the SCT to the TRT in order to examine theTRT straw efficiencies.
3.4.2 SCT specialized monitoring
Most of the low level information about the SCT modules can be extracted directly from the byte
stream file using an SCT standalone, monitoring program (sctComTool) with an event stream decoder which unpacks the ROD fragments. As a result the hit strip numbers, trigger delay, andreadout errors are available. The byte stream has enough information to assign a hit to a module.
The low level monitoring includes occupancies at strip, chip, and module levels; dependencies– 23 –

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Figure 12. Difference between the SCT and TRT reconstructed track φ parameter as a function of the event
number for one run of the combined barrel test. The synchronization between the readout of SCT and TRT
subdetectors was lost at event ≈7600 and the run was stopped.

TRT Noise occupancy

TRT noise occupancy vs event number
0.1
0.09
0.08
0.07
0.06
0.05
0.04

0.02
0.01
0
0

2000

4000

6000

8000

10000
12000
Event number

Figure 13. TRT Endcap C noise occupancy as a function of the event number. A LV trip occurred around
event 1100.

of single and double hit8 occupancies on the trigger delay; and readout errors. A study of theoccupancy dependence on the trigger delay provides two types of results:• If the trigger is random then the only recorded signals are due to noise. Different occupancies
at different trigger delays can indicate the presence of correlated noise with other subsystems.
• If the readout is triggered by real particles passing through the detector then the maximum
occupancy at certain trigger delay will indicate the right time for readout. The double hit
occupancy takes into account the position correlation between hits at both sides of a moduleand therefore such occupancy should give better noise background rejection. This leads tomore pronounced dependence of the double hit occupancy from the trigger delay.
The hits from tracks in the SCT will strongly bias the measured noise occupancy if these hits
are not subtracted. Therefore a special procedure was developed which determines the occupancy
at module level on an event-by-event basis and subtracts the hits which were identified as belonging
to space points.
3.4.3 TRT specialized monitoring
During the SR1 tests a TRT standalone monitoring tool (TRTViewer) was extensively used.
TRTViewer runs at the ROD-Crate-DAQ (RCD) level and is a detector oriented tool which was
specially developed as a flexible debugging and a fast diagnostics instrument. It is based on a
ROOT framework and contains some primitive tracking based on the least squares method, which
is necessary for the time tuning of the TRT parts and checks of the TRT basic functions.
The TRTViewer concept is based on the visualization of the basic detector characteristics according to the physical location of the elements: straws, chips, electronics boards, and detector
8 A double hit is a coincidence between two sides of a module.

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0.03

5000040000

Number of straws

number of hits

Entries
643779Mean
0.005103
RMS
0.4106
χ2 / ndf
376.9 / 11
Constant
5.546e+04 ± 106
Mean0.001916 ± 0.000334
Sigma
0.1968 ± 0.0004

900
800
700

hTSEffi

Entries 9302
Entries 9302
Mean 0.8846
Mean 0.8846
RMS 0.1434
RMS 0.1434

600
500

30000

400
300

20000

200
10000

0
-2.5

100
-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

0
0

2.5

0.2

0.4

0.6

0.8

1

Straw efficiency (any Low Level hits)

Residual all hits on track [mm]Figure 16. Online histogram of the distance between Figure 17. Online histogram of the straw efficiency
for the cosmic tracks.
a track and a reconstructed hit in the TRT.

modules. It includes the presentation of DCS and DB parameters, raw data for each channel, noisecharacteristics, TRT performance parameters like straw particle efficiency, and tracking performance statistics. The tool can work directly online, either with received data from the sampler
during DAQ operation or with recorded data. It allows the evaluation of the performance of all of
the hundreds of thousands of detector channels practically at the same time, thus allowing one to
examine the misbehaviour of the detector components. Raw data presented by the TRTViewer as
an Event Display for the Barrel ID and EC TRT during the SR1 tests are shown in figure 14 andfigure 15. The barrel picture shows a cosmic muon event detected during a combined TRT/SCT
cosmic run and contains TRT and SCT information. Blue points for the TRT correspond to thestraws which have the hits. Blue and red points for SCT correspond to the stereo strips which havethe hits.
Examples from the online monitoring results of the TRT tracking properties during a combined
cosmic run are shown in figure 16 and figure 17. For these the internal tracking code was used

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Figure 14. Cosmic muon event in the barrel ID visu- Figure 15. Cosmic muon event in the EC-TRT visualized with TRTViewer.
alized with TRTViewer.

as described above. The tests were performed with an Ar/CO2 gas mixture. Figure 16 shows
the distribution of residuals between reconstructed track positions and straw space points. The
precision of the drift time measurement achieved by the online monitoring tool is about 200 µ m.
This is close to the value obtained for a single straw with a silicon microstrip telescope in the test
beam for the Ar/CO2 mixture [46]. Figure 17 shows the straw hit efficiency. The low efficiency tailcomes from straws with limited statistics. All parameters are within the TRT specification.

4. Tests with random triggers and calibration runs

The electrical performance of the SCT was monitored through all stages of construction and integration. The main test points were
• During module construction• After mounting of modules to complete single barrels and disks• Assembly of disks in the endcap cylinder• After final assembly of the SCT and TRT
The test procedure and results of these tests are described in detail in references [3, 4], and [47].
Results presented here will focus only on the tests done at SR1 after the final SCT and TRT assembly. The aim was to examine the noise performance of the combined system and check forcross-talk. The noise can be measured either through module calibration or by running the DAQ
system in physics mode using a random trigger.
4.1.1 Results from module calibration
The tests perfomed in calibration mode consisted of a full characterization of the analogue performance using the built-in calibration circuitry. Two main parameters were verified in these tests:
the number of defective channels and the noise performance (as equivalent-noise-charge ENC ornoise occupancy). Defective channels with different fault signatures were categorized using thesetest results. These and further calibration tests are described in more detail in reference [17].
In the setup phase of the module readout, each module was configured and triggers were sent
to it. The correct communication was verified by sending, reading back, and comparing a known
test pattern of varying length to the ABCD3TA mask registers [21].
After module setup the digital function of the module was verified. First, the proper functionof the digital pipeline of the ABCD3TA chip was checked by issuing a “Level 1 Accept” (L1A)
trigger after a chip reset. The delay between trigger and reset was varied to scan all pipeline cells.
The hybrid of the SCT modules has the capability of by-passing the readout of a chip in case it isfaulty. The functioning of this bypass scheme was checked by bypassing each chip of the module
one at a time.
Following the successful completion of digital tests the analogue performance was tested foreach channel. In the “3-point-gain” test, three test charges (1.5 fC, 2.0 fC, and 2.5 fC) were injectedin each channel and its gain was measured by a threshold scan for each test charge. From the curve– 26 –

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4.1 SCT noise performancenumber of chips

Mean 1680

250

RMS 74.69

200

150

100

0

1400

1500

1600

1700

1800 1900 2000
ENC noise [e-]Figure 18. Distribution of average equivalent noise charge (ENC) per readout chip for the SCT barrel sector
during combined testing as measured by the SCT calibration.

of the threshold scan data, the gain was extracted as the threshold for 50% efficiency. A fit of acomplementary error function to the threshold scan yields the output noise at 2.0 fC, which wasused to extract the equivalent noise charge (ENC) as output noise divided by the channel gain.
Figure 18 shows the average ENC noise per chip of the two sectors used in the combined tests. The
ENC was measured at a hybrid operation temperature of around 28 ◦ C. The choice of temperature
was driven by practical constraints of the test setup for dew point control. For final operation in theexperiment we envisage a hybrid temperature of ≈0 ◦ C, which will reduce the ENC noise [17].
With this noise test it was possible to identify defective channels (noisy or dead) as well as
bonding defects (noise too high or low). The tests also allowed a comparison of the number of defective channels with results from earlier integration studies. No significant increase in the numberof bad channels was observed. From measurements carried out on single barrels and disks during
the previous integration stage, we determined the percentage of defective channels as 0.2% in the
SCT barrel and 0.3% in the SCT endcaps [17, 22].
The noise occupancy was measured with a threshold scan without charge injection. We define
the noise occupancy as the single channel noise occupancy per bunch-crossing. This strip noise
occupancy is usually averaged over a readout chip (128 channels), or a module side (768 channels),
or a full module (1536 channels). We quote the noise occupancy (number of noise hits per channel
and L1A trigger) for a threshold of 1 fC, which will be the operational threshold in the experiment.
The goal for the SCT is to have a noise occupancy of less than 5×10−4 . Figure 19 shows the noise
occupancy averaged over each chip of the two sectors used in the combined tests.
4.1.2 Results from random triggers
The noise tests performed in SR1 included runs at high trigger rates, tests of different grounding
schemes, and tests of FE noise pickup during the active readout cycle of the TRT. In these tests the– 27 –

2008 JINST 3 P08003

50

number of chips

90

Mean 4.376e-05

80

RMS 2.629e-05

70
60
50
40

20
10
00

0.02

0.04

0.06

0.08 0.1 0.12 0.14
chip noise occupancy

×10-3

Figure 19. Distribution of average noise occupancy per readout chip for the SCT barrel sector during
combined testing as measured by the SCT calibration.

readout of the detectors was triggered by a pulser at fixed frequency or a pseudo-random trigger.
Unless otherwise stated, the results shown here were obtained by running the ATHENA offline
code on data that were taken with the SCT module thresholds set to 1.0 fC as given by the response
curve calibration.
A simple illustration of the noise behaviour of the barrel SCT can be seen in figure 20, whichshows the strip number for all hits in the first 10000 events of a run in which both the SCT and TRT
were powered and read out. The strip channel number goes from 0 to 767, covering all strips on
one side of a module. Hits from both sides of the module are therefore superimposed on the plot.
The spikes represent the noisy strips present in the fraction of the SCT that was read out. They
appear on a background of uniform noise.9 These nine noisy channels (out of a total of 718848)
match those identified as problems in the calibration. Both subdetectors were triggered using apulse of 50 Hz and the thresholds of the SCT modules were set to 1 fC. The expected modulationof the noise occupancy within a chip (128 channels) is also apparent in figure 20. The higher noise
toward the middle of the chip is due to power distribution inside the chip.
Several tests were performed in SR1 in order to investigate specific noise effects. These aresummarised below, and the results of the noise are given for each.
Trigger rate scan. For both the barrel and the endcap, physics mode runs were taken with different trigger rates in order to check if this has any effect on the noise occupancy. The module
thresholds were set to 1 fC, and all other settings, such as the presence or absence of the TRT, were
not changed between runs.
Tables 4 and 5 contain a summary of the averaged noise occupancies measured for each layer
9 In this sense, the plot shows the typical behaviour of a module side, with some smearing due to the cumulative effectof superimposing all module sides on the same plot.

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30

Number of hits

10000

8000

6000

4000

00

100

200

300

400

500

600
700
Strip number

Figure 20. The strip number distribution for all hits in the SCT.
Table 4. The mean (RMS) barrel noise occupancy (scaled by 105 ) for runs taken with different trigger rates.

Layer 0 Side 0
Layer 0 Side 1
Layer 1 Side 0
Layer 1 Side 1
Layer 2 Side 0
Layer 2 Side 1
Layer 3 Side 0
Layer 3 Side 1

500 Hz4.87 (3.13)
4.94 (3.28)
4.36 (1.83)
4.92 (2.39)
4.26 (2.16)
4.89 (2.51)
4.90 (3.34)
4.75 (2.93)

5 kHz
4.94 (3.14)
5.01 (3.28)
4.33 (1.81)
4.89 (2.37)
4.26 (2.12)
4.90 (2.49)
4.89 (3.31)
4.78 (2.96)

50 kHz4.92 (3.17)
4.97 (3.23)
4.33 (1.80)
4.84 (2.33)
4.22 (2.15)
4.88 (2.44)
4.86 (3.30)
4.78 (2.98)(disk) side of the barrel (endcap). Layers are numbered in the barrel starting with “0” for the
innermost barrel, Barrel 3, to Layer 3 for the outermost barrel, Barrel 6. The trigger rate does notappear to affect the noise occupancy. The two sides or each module are denoted as “Side 0”, thetop side of the module, and “Side 1”, the module side which faces the support structure.
Noise threshold scan. An important test of module behaviour is the noise occupancy as a function of threshold. In the low noise regime, occupancy is related to the threshold by the following
empirical description [48]:


t2
(4.1)
N ∝ exp − 22σwhere t is the module threshold, N is the noise occupancy, and σ is the equivalent noise charge ofthe module.
Physics mode runs were taken with different thresholds for both the barrel and the endcap.
The barrel runs were triggered from a pulse at a frequency of approximately 500 Hz, with the TRT
clocked but not read out.

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2000

Table 5. The mean (RMS) endcap noise occupancy (scaled by 105 ) for runs taken at different trigger rates.

Number of barrel chips or endcap modules

3

10

1 kHz
3.78 (1.87)
3.87 (2.21)
5.04 (2.53)
4.49 (3.12)
4.50 (2.84)
4.16 (2.77)
5.53 (3.46)
5.47 (2.47)
3.66 (2.04)

10 kHz3.58 (1.76)
3.86 (2.15)
4.76 (2.35)
4.17 (3.04)
4.46 (2.49)
4.16 (2.63)
5.29 (3.24)
5.47 (2.72)
3.69 (2.01)

50 kHz3.57 (1.75)
3.87 (2.19)
4.74 (2.37)
4.48 (2.87)
4.11 (2.59)
3.92 (2.68)
5.33 (3.37)
5.40 (2.71)
3.60 (1.96)

100 kHz3.41 (1.69)
3.78 (2.09)
4.73 (2.37)
4.23 (2.92)
4.09 (2.68)
4.12 (2.61)
5.25 (3.30)
5.31 (2.73)
3.58 (1.88)

1586 eOuter endcap : 1575 e
Middle endcap : 1551 e
Inner endcap : 1105 e

Barrel :

102

10

1
600

800

1000

1200

1400

1600

1800

2000

Noise (electrons)

Figure 21. Distribution of the ENC values (in number of electrons) for all module sides obtained in noise
threshold scan.

Equation 4.1 indicates that a plot of ln(N) versus t 2 should be a straight line, and its slope
allows the equivalent noise charge for each module to be extracted. The distribution of the averaged ENC values obtained for all module sides of the SCT barrel and endcap modules is given in
figure 21. Barrel modules and outer and middle endcap modules have approximately 12 centimeterlong strips, where the strips on two wafers are bonded together. Endcap inner modules have about
6 centimeter long strips on one wafer. The shorter strip length implies a smaller load capacitanceon the frontend input, which in turn implies lower noise. An example of one of the straight line
fits is given in figure 22. The ENC values obtained are roughly equivalent to those reported fromcalibration runs, though an exact comparison is difficult to make due to differences in the way thedata were analysed.
The noise occupancy specification limit of 5 × 10−4 demands that the total equivalent noise
charge should not exceed 1900 e− [49]. All of the ENC values presented here are within this– 30 –

2008 JINST 3 P08003

Layer 0 Side 0
Layer 1 Side 1
Layer 2 Side 0
Layer 3 Side 1
Layer 4 Side 0
Layer 5 Side 1
Layer 6 Side 0
Layer 7 Side 1
Layer 8 Side 1

ln(N)

-6
-8

-10
-12

-16
-18

0.8

0.9

1

1.1

1.2

1.3

1.4

1.5

2

2

t /(fC)

Figure 22. A plot of ln N versus t 2 for one module side of the barrel SCT. A straight line has been fitted (seeequation 4.1), with a slope p0 = −2.6 ± 0.2 (fC)−2 and intercept on the y-axis p1 = −7.7 ± 0.2.

limit once a small number of known noisy strips with a noise occupancy greater than 5 × 10−3 aremasked off.
For the barrel, two different grounding schemes were investigated with the above method, andit was found that these did not affect the noise occupancy. A similar test was not performed on theendcap.
Trigger timing delay scan. Since the TRT and SCT were operated together for the first time,
an important point to investigate was whether the TRT induces any noise in the SCT. The TRT
starts reading out about 3.25 µ s after a trigger. The SCT was therefore triggered about 3 µ s after
triggering the TRT to search for pickup induced in the SCT by the TRT readout cycle. The BOC
Tx coarse delay, which is the delay between incoming trigger and SCT L1A, was then scanned insteps of 75 ns in order to add extra delay between the triggering of the SCT and the TRT, and thetime delay was written into the byte stream. If no noise pickup occurs, the noise occupancy should
be flat when plotted against time delay, as no extra noise occupancy is observed when the TRT isread out.
Plots of the barrel noise occupancy versus time delay are given in figure 23 for a run in which
the TRT was off, with its FE electronics unpowered, and for a run in which the TRT FE electronics
was clocked and read out. The analysis was performed with strips masked off if their noise occupancy was greater than 5 × 10−3 . In the analysis we required the hits to be recorded in the correct
middle time bin, denoted by the “X1X” hit-time pattern. The noise occupancy is flat in each plot,
and thus it is concluded that there is no evidence for noise pickup from the TRT. Furthermore, nodependance on the bit pattern of the hits was observed. Plots of the endcap noise occupancy are
given in figure 24. Again, there is no evidence of noise pickup resulting from the TRT. Also, there
was no dependance on the bit pattern of the hits for the endcap.

– 31 –

2008 JINST 3 P08003

-14

1.15

Relative noise occupancy

Relative noise occupancy

1.2

TRT off

1.1
1.05
1

1.2
1.15

TRT on
1.1
1.05
1

0.95

0.95

0.9

0.9

0.85

0.85
5

10

15

20

0.8
0

25
30
Trigger delay [25ns]

5

10

15

20

25
30
Trigger delay [25ns]

1.2

Relative noise occupancy

Relative noise occupancy

Figure 23. Noise occupancy (normalised to the first bin) versus time delay for the SCT barrel. The TRT was
off for the plot on the left, and thus the data provide a calibration plot with no noise pickup from the TRT.
The right-side plot was obtained from a run with the TRT on.

1.15
1.1

TRT off

1.05
1

1.2
1.15
1.1
1.05
1

0.95

0.95

0.9

0.9

0.85

0.85

0.8
0

5

10

15

20

25
30
Trigger delay [25ns]

TRT on

0.8
0

5

10

15

20

25
30
Trigger delay [25ns]

Figure 24. Noise occupancy (normalised to the first bin) versus time delay for the SCT endcap. The left-side
plot represents a run when the TRT was off, the right-side plot represents a run when the TRT was on.

Final noise occupancies. The distribution of noise occupancies for all module sides is shown infigure 25, excluding, for reasons of scale, the inner endcap modules, which were found to have amean noise occupancy of 1.6 × 10−7 . The module thresholds were set to 1 fC and the analysis was
performed with strips with an occupancy greater than 5 × 10−3 masked off.
All occupancies are below the design specification of 5 × 10−4 , though for the barrel data
there is a slight increase from previous tests, where barrels were operated individually (a meanof 4.5 × 10−5 is quoted in reference [3], compared to 4.76 × 10−5 here). Although it is possible
in principle that this small increase will scale with the number of modules that are connected,
previous noise tests performed with differing numbers of modules on the final SCT barrels showedno evidence that the performance degrades in the large system [3]. It would thus appear that the
SCT is running comfortably within its design specification.

Channels on Side 1 show slightly higher noise occupancy than Side 0, as seen in figure 26
for barrel data. The plots show data with the module thresholds set to 1 fC as obtained from the– 32 –

2008 JINST 3 P08003

0.80EC Outer: 5.2e-05
EC Middle: 3.8e-05

10

1
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

×10
0.5

-3

Noise occupancy

Number of module sides

Number of module sides

Figure 25. The distribution of noise occupancies for all module sides of the SCT detector, excluding theinner endcap modules.

50

40

30

50

40

30

20

20

10

10
×10
0.4 0.45 0.5
Noise occupancy

-3

0
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

×10
0.4 0.45 0.5
Noise occupancy

-3

0
0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Figure 26. The distribution of noise occupancies for the active modules of the SCT barrel detector for Side0 (left) and for Side 1 (right).

response curve calibration. The Side 0 distribution has a mean of 4.62 × 10−5 and an RMS of
2.70 × 10−5 , while the Side 1 distribution has a mean of 4.90 × 10−5 and an RMS of 2.80 × 10−5 .
This is consistent with results obtained during module production.
4.2 TRT noise studies
Some of the most important issues during the combined tests were noise studies for different detector configurations and different grounding schemes. Particular attention was given to the possibilityof inducing noise in the TRT from the SCT during combined operation.
TRT grounding and Faraday cage efficiency issues were addressed during standalone tests. It
was found that a proper ground connection between the frontend boards and the Faraday cage is
an important factor for the suppression of clock pickup noise. These connections must be made– 33 –

2008 JINST 3 P08003

Number of module sides

Barrel: 4.7e-05

102

8A

7A

Figure 27. Noise occupancy map for the Barrel TRT sectors participating in the noise tests. Yellow coloured
straws have noise occupancies about 1.3% and black coloured straws have noise occupancy above 4%.

with a spacing of not more than 3 cm to make a frequency cut at the level of above 100 MHz.
After the implementation of this system, the noise level for most of the TRT channels was close tothat defined by thermal fluctuations. A typical picture of the TRT noise hit map for the modules
participating in the test is shown in figure 27. Yellow coloured straws have a noise occupancy
of about 1.3% and black coloured straw have a noise occupancy above 4%. One finds a groupof straws in the TRT module Type 1,10 which can be associated with one DTMROC chip witha systematically large noise occupancy. That is a known problem of these particular boards andis related to the board layout in which the 40 MHz clock line has a capacitive coupling with the
ASDBLR inputs. Details of the noise distribution across the channels are shown in figure 28. Mostof the channels have noise at the level of 1%, but clock pickup affected channels have noise levelsof up to 15%. Although that is a substantial noise level, it does not severely affect the physics
performance.
A series of tests were performed to evaluate the change in TRT noise during the combined
operation of the SCT and TRT. The first test aimed to see the effect of SCT frontend power on TRT
noise occupancy. The noise occupancy differences for one of the TRT stacks (Stack 7) for sides A
and C are shown in figure 29. Positive values indicate an increase in noise between the reference(when the SCT was off) and when the SCT was on. Negative values indicate a decrease in noise.
All deviations are within standard noise behaviour.
Following the ATLAS grounding convention, all detectors must have an independent groundconnection at one controlled point. In order to evaluate what happens if an uncontrolled groundshort appears during installation in the pit, a special test was carried out. A worst case groundconnection was artificially created between the analogue ground plane in the TRT and the SCT
power return. A comparison of the TRT noise occupancy for this run with the reference one isshown in figure 30. No statistically significant effect was found during these tests.
A particular subject of interest was the effect of the SCT readout cycle on the TRT noise
10 TRT barrel modules are stacked in layer with Type 1 modules forming the innermost layer, Type 2 modules the– 34 –

2008 JINST 3 P08003

8A

7A

Occupancy

0.22
0.2
0.18
0.16
0.14
0.12
0.1
0.08
0.06

0.02
0
0

200

400

600

800

1000

1200

1400

1600

Straw number with any low level bit on

Figure 28. Noise occupancy distribution across the channels for one of the TRT Stacks (Stack 7 on Side A).
Typical noise occupancy is about 1.3%.

Stack7A - Change in Occupancy

102

Stack7C - Change in Occupancy

Fit Mean = -0.01492
Fit Sigma = 0.13945

102

Mean = -0.01253
RMS = 0.19429

10

10

1

1

10-1-10

-8

-6

-4

-2

0

2

4
6
8
10
Change in Occupancy (%)

10-1-10

Fit Mean = 0.03591
Fit Sigma = 0.13050

-8

-6

-4

Mean = 0.04354
RMS = 0.24695

-2

0

2

4
6
8
10
Change in Occupancy (%)

Figure 29. Comparing noise occupancy when the SCT is off with a run when SCT is on. The horizontal
axis shows the difference of occupancy with SCT on minus occupancy with SCT off in percent. The verticalaxis gives the number of straws.

occupancy. Two types of tests were performed. In the first, TRT noise measurements were madeduring SCT operation at a trigger rate of 50 kHz. In order to increase SCT frontend digital activity,
the SCT threshold was set to 0.8 fC. No change in the TRT noise occupancy was found. Anothertest aimed to look at exactly what happens during the SCT readout cycle. The time at which
the SCT frontend chips transmit their header words to the backend corresponds to the maximum
amount of frontend digital activity. Data were taken using different TRT delays with respect to
SCT trigger. Figure 31 shows the typical occupancy difference with respect to the reference run.
Again, no noticeable change was observed.

middle layer, and Type 3 modules the outermost layer.

– 35 –

2008 JINST 3 P08003

0.04

Stack7A - Change in Occupancy

102

Stack7C - Change in Occupancy

Fit Mean = -0.01025
Fit Sigma = 0.12558

102

Mean = -0.01763
RMS = 0.17109

10

10

1

1

10-1-10

-8

-6

-4

-2

0

2

10-1-10468
10Change in Occupancy (%)

Fit Mean = -0.00512
Fit Sigma = 0.12605

-8

-6

-4

Mean = 0.00047
RMS = 0.29187

-2

0

2

4
6
8
10
Change in Occupancy (%)

Stack7A - Change in Occupancy

102

Stack7C - Change in Occupancy

Fit Mean = 0.02258
Fit Sigma = 0.19930

102

Mean = 0.06263
RMS = 0.44481

10

10

1

1

10-1-10

-8

-6

-4

-2

0

2

4
6
8
10
Change in Occupancy (%)

10-1-10

Fit Mean = 0.07140
Fit Sigma = 0.20713

-8

-6

-4

Mean = 0.09096
RMS = 0.33160

-2

0

2

4
6
8
10
Change in Occupancy (%)

Figure 31. Comparison of the TRT noise occupancy during SCT readout cycle with the TRT standalone
noise occupancy.

5. Tests with cosmic triggers
5.1 Alignment and calibration
5.1.1 Alignment of the SCT and TRT
The determination of the position of the detector elements in space is crucial in order to make fulluse of the intrinsic resolution of the tracking device. The relative alignment of the sensors in amodule is given by the mounting precision during the module assembly [3], which used tolerances
well below the sensor spatial resolution. The mounting precision of modules on disks and barrels is
less well known compared to the intrinsic resolution. This would lead to a significant degenerationof the tracking performance and needs to be corrected in the alignment step.
The position of the detector elements can be determined with track based alignment algorithms, based on the optimization of hit residuals. A perfectly aligned detector returns a residual
distribution centered around zero and with a width consistent with the quadratic sum of the intrinsic resolution and the uncertainty originating from the track fit. A misaligned detector setup
leads to a off-centered residual distribution with an increased width originating from the additional
misalignment uncertainty.

– 36 –

2008 JINST 3 P08003

Figure 30. Comparison of the TRT noise occupancy when the TRT analogue ground plane is connected to
the SCT power return with the TRT standalone noise occupancy.

5.1.2 Alignment of the SCT
Various alignment approaches were applied to align the SCT barrel in the SR1 cosmic setup [51 –54]. All approaches are based on the optimization of hit residuals. Figure 32 shows the residual distribution for all modules located in a certain SCT layer obtained with the Global χ 2 approach [52].
In this approach each module is aligned for six degrees of freedom. Modules with fewer than
150 hits were kept stable. It is clearly visible that the residual distribution is distorted before thealignment corrections are applied. After the alignment the mean for all layers is centered aroundzero and the width of the residual distribution is reduced compared to the width obtained with the– 37 –

2008 JINST 3 P08003

Track based alignment algorithms use the fact that the fit of the track trajectory is overconstrained and the deviations from the helix or the straight line in case of no magnetic field areminimized with respect to the translational and rotational degrees of freedom of the detector [50].
Any deviation from the description of a perfect trajectory will appear as a misalignment of thedetector. Therefore the track selection processes for track based alignment use tracks with highmomentum to avoid kinks in the trajectory originating from multiple coulomb scattering (MCS)
effects. As described above, the test setup was not inside a magnetic field. The momentum of thevarious charged particles could not be determined and no selection criteria based on the momentum could be applied. The momentum spectrum of particles from cosmic rays is dominated by low
momentum particles, which suffer substantially from multiple scattering. The width of the residual distribution, the quadratic sum of the intrinsic resolution, and the uncertainty from the trackreconstruction, is therefore dominated by multiple scattering. In particular, the impact of misalignment effects on the residual width is small compared to the multiple scattering effects. The track
fit algorithm normally applies the measured momentum to estimate the contribution from multiple
scattering to the overall track position uncertainty. Since no momentum measurement is available,
the uncertainty cannot be calculated and the multiple scattering contribution is set to zero. This
leads to a significant underestimation of the uncertainty on the track position.
The track based alignment approaches are based on the minimization of hit residuals. Thisis a necessary, but not sufficient requirement. The vertical track topology of the charged particles originating from cosmic ray events can lead to degeneracies in the solution of the alignment,
for example, a shearing of the detector in the Y Z-plane cannot be recovered. In addition, verticalmovements along the track trajectory of the two shells are not constrained. These deformations,so called weak modes, cannot be recovered by the alignment approaches. No track control sample with a different track topology was available to verify the quality of the alignment constants
independently. Therefore the conclusion on the obtained alignment constants is only limited.
Each SCT module has six degrees of freedom: three translations and three rotations. The
translation perpendicular to the strips is expected to be determined best. Overall for the SCT
6 × 467 = 2802 degrees of freedom have to be aligned. The TRT has no sensitivity along the wire,
leading to five degrees of freedom per module times 12 TRT modules in the readout (overall 60
degrees of freedom).
Several internal alignment approaches were developed and applied to data taken with the barrel
SCT and the barrel TRT. The tracks used for the alignment were reconstructed using the standard
ATLAS reconstruction software framework ATHENA and the alignment algorithms were also implemented as a part of ATHENA.

6000

NominalSCT Layer 0

5000

number of hits

number of hits

Globalχ2 Aligned

7000

4000

Globalχ2 Aligned
9000

NominalSCT Layer 1

8000
7000
6000
5000

3000

4000

2000

3000
2000

1000

1000
-0.1

0

0.1

Globalχ2 Aligned

14000
12000

NominalSCT Layer 2

0
-0.3

0.2
0.3
residuals [mm]10000

number of hits

-0.2

-0.2

-0.1

0

0.1

0.2
0.3
residuals [mm]Globalχ2 Aligned

10000

8000

NominalSCT Layer 3

8000
6000

6000
4000
4000
2000

2000
0
-0.3

-0.2

-0.1

0

0.1

0.2
0.3
residuals [mm]

0
-0.3

-0.2

-0.1

0

0.1

0.2
0.3
residuals [mm]Figure 32. The SCT biased residuals for the different layers of the SCT are shown before (red) and after(blue) the alignment.

misaligned setup. The other alignment approaches returned similar results. As mentioned previously, the width of the residuals distributions is dominated by multiple scattering and, in particular,
effects due to detector misalignments might not be noticeable. In addition, the contribution to thetrack uncertainty originating from the track fit is largely underestimated, since the contribution
from multiple scattering is not calculated.
All alignment algorithms used an iterative approach to get a stable result. Figure 33 shows
the residual quantities as a function of iteration. The mean as well as the resolution of the residual
distribution converges to a stable number after three iterations. Convergence was reached when the
movements of the detector were smaller than the corresponding statistical uncertainty. The initial
shift of the mean was removed and the width of the resolution σ got significantly smaller. The
width of the residuals varies between layers because the track error varies along the track, e.g., due
to multiple scattering.
The magnitude of the shifts obtained in the local X direction (orthogonal to the strip direction)is generally of the order of 100 µ m. The local Y alignment constants (along the strip direction) areof the order 600 µ m.
5.1.3 Comparison of alignment results with the photogrammetry measurements
Due to the fact that no magnetic field was used during SR1 data taking all tracks coming fromcosmic rays are straight lines. Thus, this setup was ideal to determine the relative rotations of the
SCT detector barrels in φ . A similar measurement was performed analysing the photogrammetry
data [56]. The first set of the three photogrammetry measurements, which was taken in November
2005 [55], was precise enough to determine relative barrel rotations. However, these measurements– 38 –

2008 JINST 3 P08003

number of hits

0-0.3

Residual mean (µm)

Residual sigma (µm)

100

Layer 0Layer 1
Layer 2
Layer 3All layers

90
80
70
60
50

15

Layer 0Layer 1
Layer 2
Layer 3All layers

10

5

0

40
-5
30
20

-10

10
-15

0

1

2

3

4

5

6

7

8

0

Iteration

1

2

3

4

5

6

7

8

Iteration

Figure 33. This figure shows the resolution σ of the biased residual distribution (left), and the mean (right),
for the four different SCT layers and all layers combined obtained after each iteration.

were taken before the insertion of the SCT into the TRT. The change from the four point mounting
on the temporary SCT support to the mounting of the SCT on rails inside the TRT may have led
to significant global distortions of the SCT. This type of distortion may create systematic, radiusdependent barrel rotations.
Figure 34 shows the measurement of the photogrammetry data in comparison with the measurements performed by the three track based alignment algorithms. The track based algorithms
determined the same relative barrel rotations within the errors of the measurement. The resultsfrom the photogrammetry differ from the alignment results in two of the layers. It is not quite clear
whether this difference arises from actual barrel movements or from the fact that the systematic
uncertainties of the photogrammetry measurements are not perfectly understood. However, thesystematic deformations mentioned above may explain the difference.
Furthermore, the relative barrel shifts in the global Z direction were analysed. Again, it appears
that all alignment algorithms determine the same systematic distortions within the uncertainties.
Since all alignment algorithms used the same reconstructed tracks and hit residuals as input, asystematic bias in the reconstruction could also lead to a common systematic bias for all alignment
approaches. Correlation for the φ and global-Z alignment could therefore lead to relative shifts in Z.
5.1.4 Relative alignment between the SCT and TRT
The relative misalignment between the barrel TRT and the barrel SCT is expected to have a larger
impact on the track reconstruction than the misalignment of the internal components of these detectors. It is also possible that a large misalignment between the two detectors could affect the trackfinding efficiency leading to a poor convergence of the alignment procedure.
A survey measurement of the relative position of the four SCT layers and the TRT was performed “in situ” (see table 6). A special procedure was implemented to determine the relative
SCT-TRT misplacement with tracks reconstructed from cosmic data. It proceeds as follows:• TRT and SCT tracks are fitted separately and the SCT tracks are extrapolated to the first TRT
straw layer;• A 2D residual is formed from the difference in the track coordinates in the plane perpendicular to the straw layer surface (the TRT has no sensitivity along the wire direction);– 39 –

2008 JINST 3 P08003

0Shift [mm]Rotation [mrad]

0.25
0.2

0.25
0.2
0.15

0.1

0.1

0.05

0.05

0

0

-0.05

-0.05

-0.1

-0.1

-0.15

-0.15

-0.2

-0.2

-0.25

0

-0.25

0.5

1

1.5

2

2.5

3

3.5 4
Layer

0

0.5

1

1.5

2

2.5

3

3.5 4
Layer

Figure 34. For each SCT layer the relative rotation of the whole SCT barrel in φ is shown in mrad (left)
and the relative Z shift in mm (right). Photogrammetry measurement are represented by green triangles,
Robust Alignment results by blue circles, Local χ 2 Alignment results by black squares, and Global χ 2
Alignment results by red squares. The alignment sets have been transformed into a common reference frameand normalised to layer 3.
Table 6. Relative translation and rotation between the SCT barrel and the TRT barrel as determined fromthe survey measurements.

∆x [mm]-0.42∆y [mm]+0.33∆rotX [mrad]+0.154∆rotY [mrad]
0.165• A χ 2 is constructed and minimized with similar techniques used in the alignment algorithms.
Because of the SR1 setup (tracks almost vertical and no TRT sensitivity along z) only themisplacement along x (∆x) and the rotations around the y (∆rotY ) and z (∆rotZ) axes could
be calculated.
This alignment procedure was applied to tracks collected from two different runs, one at the beginning and one at the end of a stable period of data taking. The two resulting SCT-TRT misalignment
values are consistent within their estimated uncertainties. Moreover, the track-based measurements
are found to be consistent with the survey measurements mentioned before.
5.1.5 Alignment of the TRT
After correcting for the relative alignment between the barrel SCT and TRT detectors, an internal– 40 –

2008 JINST 3 P08003

0.15

rotation x 500

translation x 2000
1000

y [mm]y [mm]

1000

500

500

0

0

-500

-500

-1000
-1000

-500

0

500

1000

-1000
-1000

x [mm]

-500

0

500

1000

x [mm]

Figure 35. Measured translations (left) and rotations (right) of TRT modules. The direction and size of thearrows in the left plot represent the direction and size of the translations. The rotation of the module in theright plot is proportional to the angle of the bar with the horizontal axis.

alignment of the TRT modules is carried out. In contrast to the SCT internal alignment described in
section 5.1.2, where only SCT tracks were used, the TRT alignment employs extended SCT-TRT
tracks. Since TRT tracks do not provide information outside the xy-plane, SCT hits are needed toobtain the z coordinate of the track. Consequently, for a correct calculation of the TRT misalignment, the SCT modules need to be precisely aligned. A framework was developed to align TRT
and SCT modules simultaneously and two of the approaches used in the SCT standalone alignment (section 5.1.2) have been implemented in the framework. The result of this procedure is analignment that is consistent across both systems. The alignment constants obtained for the SCT
are comparable, in terms of residuals, to the results from the other SCT alignment methods alreadypresented. Therefore, we will only report the TRT results.
The alignment of the TRT was limited to only the two most sensitive degrees of freedom,
namely the direction perpendicular to the wire in the straw layer plane (∆X ) and the rotation aroundthe wire axis (∆rotY ).
The limited statistics and the nearly parallel track trajectories did not allow us to perform the
TRT module alignment for the other weakly constrained degrees of freedom. The convergence of
the alignment constants after successive iterations was reasonable, with the residual distributions
centered at zero after alignment. However, in contrast to the SCT case, the width of the TRT
residual distributions only marginally improved, due to the fact that for the TRT the alignment
corrections are small (< 50 µ m) with respect to the width of the residual distribution. In figure 35
the TRT module displacements returned by the alignment algorithm are shown graphically withthe values scaled for clarity. As can be seen, layer 0 and layer 2 modules are rotated in the same
direction while layer 1 modules are rotated in the opposite direction (both by approximately 300400 µ rad). Such a pattern is consistent with an elliptical distortion of the TRT (or possibly theSCT), but a conclusive interpretation cannot be obtained from the SR1 cosmic data.
A study of possible TRT module deformations was also carried out. TRT module deviations
from the rigid body approximation are expected (caused by, for example, construction imperfec-– 41 –

2008 JINST 3 P08003

1 mrad

0.1 mm5.1.6 Alignment summary
Tracks reconstructed from cosmic ray events were used to determine the alignment of the barrel
SCT, the barrel TRT, and the SCT with respect to the TRT. The calculated barrel alignment constants give a first indication as to the magnitude of the detector misalignments. The results forthe alignment are limited by multiple scattering of low momentum tracks. In addition the vertical
topology of the tracks from cosmic ray events leads to weak modes of the detector, which cannot
be resolved by track based alignment algorithms.
The three SCT alignment algorithms return consistent results. The magnitude of the shifts
in the local X direction is generally of the order 100 µ m. The local Y alignment constants areof the order 600 µ m. The comparison of the rotation around the global Z-coordinate between
the track based alignment and photogrammetry measurements is not conclusive due to systematic
uncertainties.
The alignment of the aligned SCT relative to the TRT is determined using tracks passing
through both the SCT and the TRT. The alignment result is consistent with the survey measurementof the inner detector.
After the internal SCT alignment and the alignment of the SCT relative to the TRT an internalalignment of TRT modules is performed. For this, SCT tracks extended to the TRT are used. The
various TRT modules move by about 50 µ m and are rotated by about 300 to 400 µ rad aroundthe wire axis. Internal deformations of the TRT modules are studied, but no final conclusion ispossible.
5.1.7 TRT drift time calibration
Drift time model. The TRT measures the drift time, the time between the LHC bunch clock andthe arrival of the signal to the anode wire, in time-to-digit counts (TDC). A measured TDC value isa function of the position and orientation of the charged particle trajectory with respect to the drift
cell. Since the TRT drift cells are symmetric around the wire axis, the drift time is only a functionof the distance to the anode wire. The model used for calibration therefore reduces to:11
TDC(r) = α (tdrift (r) + t0 ).
11 The propagation time and time-of-flight contributions were not taken into account.

– 42 –(5.1)

2008 JINST 3 P08003

tions, or mechanical stress) and if they are larger than the detector resolution, a correction must
be applied. In terms of alignment, they effectively produce a hit displacement depending on thehit position inside the module. Given the limited amount of available data, we limited our study
to the residual dependence on the hit position along the straw axis. Because the measurement of
this coordinate is not provided by the TRT, it was derived by extrapolating SCT tracks to the TRT
straw layer under study (as a consequence, remaining alignment deviations in the SCT can also
appear as module deformations in the TRT). For modules with larger statistics (layer 1 and layer 2)
we actually observed significant deviations from zero. Some patterns are consistent with module
twists, others show more complicated deformations. A final conclusion on the magnitude of suchdeformations and their impact on track reconstruction will require more extensive data sets fromp-p collisions.

measured drift time

0.00 < d < 0.10 [mm]0.60 < d < 0.70 [mm]

300
300

40

200

200

100

20

100

0

0

20

0

0

drift time [ns]1.30 < d < 1.40 [mm]020drift time [ns]1.90 < d < 2.00 [mm]

400

100

200

50

-20
-2

-1

0
1
2
reconstructed distance [mm]

0

0

20

drift time [ns]

0

0

20

drift time [ns]Figure 36. TRT barrel time-to-distance distribution (left) and the drift time distribution in 4 distance intervals(right). The smooth curves in the right figures represent the result of the fit with a Gaussian. The blue points
in the left plot represent the result of the fitted mean for each distance bin.

where α = 8/25[ns−1 ], r represents the distance to the anode wire, tdrift is the drift time, and t0 is achannel dependent offset.
Drift time calibration procedure. The drift time calibration procedure uses reconstructed tracks
to translate the TDC value into the best possible estimate of r. For each hit, the measured drift
time is computed and the rt relation determined from the distribution shown in figure 36. The
2-D distribution in figure 36 is divided in 0.1 mm bins in r and 3.125 ns bins in t. In each rbin the maximum of the drift time distribution is determined by fitting a single Gaussian to the 5bins centered around the most occupied bin, in order to reduce sensitivity to the tails as shown infigure 36 (right).
The rt relation is then parametrized using a third degree polynomial. The t0 offsets are determined from the average drift time residual of reconstructed hits, computed from the measured drift
time and the inverse of the rt relation. The corrected t0 ’s are obtained by subtracting the average
drift time residual. Since our method relies on reconstructed tracks while the track reconstruction
itself relies on a proper calibration, the procedure depends on a reasonable starting point for both
the rt relation and t0 . We have found that the procedure could be bootstrapped with a single constantt0 and a simple linear rt relation. The procedure was robust against the choice of the initializationand converged typically in 2 iterations.
Results obtained with SR1 cosmics data
Barrel. Since no reliable momentum estimate is available, tracks are reconstructed without taking
into account MCS effects. To reduce the effect of MCS, reconstructed tracks are split into their top
and bottom segments. For each segment we require at least 20 TRT hits.

– 43 –

2008 JINST 3 P08003

150

20000

10000

0
0

5

10

15

20

χ2/ndof1

expected error on mean

width of residual distribution

number of tracks

30000

σ from fit [-0.5,0.5]
rms [-2,2]

0.5

0
0

5

10

15

20

χ2/ndof

0.1

0.05

0

0

5

10

15

20

χ2/ndof cut

In the calibration procedure we require for each hit a time-over-threshold of at least 2 ×
3.125 ns and a maximum time residual of 15 ns. The latter is chosen large, approximately 5 times
the single hit resolution, to allow recovery from a poorly chosen starting value for t0 in the first
iteration. To improve the quality of the selected track segments, a cut is made on the χ 2 /ndof of
the track. To prevent a bias in the calibration the χ 2 /ndof is first reduced by the contribution fromthe selected hit. The resulting χ 2 /ndof distribution is shown in figure 37.
The centre plot in figure 37 shows the width of the unbiased residual distribution in bins
2
χ /ndof. The width is measured both as an RMS on the interval ±2 mm and as the σ of a Gaussian
fit on the interval ±0.5 mm. The latter result shows that a single hit resolution of around 200 µ m is
obtained.
To motivate the choice of a particular χ 2 /ndof selection we calculate the uncertainty on themean of the residual distribution as a function of the χ 2 /ndof cut. The uncertainty is estimated√as σ (mean) = RMS/ N − 1. The rightmost plot in figure 37 shows the result, normalised for ahypothetical set of 100 hits. As one can see contribution from hits with χ 2 /ndof > 10 actuallydegrades the resolution on the mean.12 Therefore only hits for which χ 2 /ndof is smaller than 10
are selected in order to reach an optimal sensitivity for calibration. Although this may seem tobe an unrealistically large value, it already rejects a significant fraction of the segments because
multiple scattering effects are ignored.
For the cosmics data we use a single rt relation for all straws. The drift time offset t0 is
determined for each straw separately. The uncertainty in the t0 is the RMS of the drift time residual
distribution divided by the square root of the number of entries (minus 1). Since the RMS of thedrittime residual distribution is itself proportional to the resolution, the fractional contribution ofp
the uncertainty in t0 to the hit resolution is about 1 + 1/N − 1 ≈ 1/2N. To keep this contribution
below 1% one would need about 50 hits per straw. For the calibration of the cosmics data we haverequired a minimum of 25 hits, based on considerations discussed below. For straws not fullfilingthe above requirements, we use the average value of the t0 of the corresponding TRT module as acompromise between the statistical and systematic uncertainties.
It is generally assumed that the time offsets of straws within one “DTMROC” unit (16 straws
12 The reason that we lose sensitivity, even though we add information is that in the extraction of the mean we do not

use the fact that the uncertainty varies with χ 2 /ndof.

– 44 –

2008 JINST 3 P08003

Figure 37. Distribution for χ 2 /ndof (left), width of the unbiased residual distribution in bins of χ 2 /ndof(centre), and estimate of the uncertainty on an estimate mean for a hypothetical set of 100 hits (right).

0.0780.076

average t 0

drift velocity

0.08

2980

3000

3020

3040

3060

3080

3100

2980

3000

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3040

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3100

2980

3000

3020

3040

3060

3080

3100

32

31.5

31

drift time
residual

0-0.5

Run number

Figure 38. Drift velocity for 0.2 < |d| < 1.8 (top), average straw t0 (middle) and average drift time residuals
with respect to the calibration of run 3099 (bottom) as a function of run number.

connected to one amplifier) are equal to within 0.5 ns. If this is the case, it would certainly makesense to combine the data from straws in a DTMROC. Unfortunately, the mapping to DTMROCs
was not available within ATHENA during the analysis of the combined test data.
Calibration constants derived from a single run were used for the reconstruction of all data.
To test the stability of the calibration the average drift velocity and the average drift time residuals
as a function of the run number was studied. Results from these studies are shown in figure 38.
From those results it is clear that the average drift time offset is stable to within ±0.5 ns. For thecosmics data this is sufficiently small with respect to the hit resolution to justify the use of a single
calibration.
Endcap. To achieve the best calibration of the TRT, good quality tracks are a very important
ingredient. For the endcap setup, the tracks were of a worse quality compared to those in the barrel
setup. The average number of hits per tracks in the endcap was about 6, compared to 20 in thebarrel. In order to make the endcap tracks longer, the drift radius error was set to a higher value,
about 240 µ m. An immediate consequence is an increase in the rate of noisy hits as shown infigure 39.
To reduce the noise rate, only hits for which the time-over-threshold was within the interval
3 × 3.125 ns and 12 × 3.125 ns were used. To better enhance the track quality, the χ 2 /ndof was
required to have a maximum of 20, and the time residual to have a maximum of 25 ns.
For the endcap setup, no TRT trigger phase was measured. Therefore, the phase was determined from the tracks themselves. For each track, the average drift time residual of the measured
drift time and the drift time from the track was calculated. The trigger phase was obtained byrequiring this average to be 0.
Another issue was that in a fraction of events leading signal edges were missing due to large
time offsets in some frontend boards, which were not timed in with respect to each other. Events– 45 –

2008 JINST 3 P08003

0.5measured drift time

50
40
30
20

0.00 < d < 0.10 [mm]
120

900
800
700
600
100 500
400
300
200
80 10000.60 < d < 0.70 [mm]
1200
1000
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200

-10-5 0 5 10 15 20 25 30 35

0

10

60

1.30 < d < 1.40 [mm]
1000
40

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-10
20

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-20
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2reconstructed distance [mm]

400

0

0

-10-5 0 5 10 15 20 25 30 35

drift time [ns]1.90 < d < 2.00 [mm]
450
400
350
300
250
200
150
100
50
0

-10-5 0 5 10 15 20 25 30 35

drift time [ns]Figure 39. TRT endcap time-to-distance distribution (left) and the drift time distribution in 4 distanceintervals (right). The smooth curves in the right figures represent the result of the fit with a Gauss. The blue
points in the left plot represent the result of the fitted mean in each distance bin.

for which the leading edge was missing were disregarded.
Following the same iterative procedure as for the barrel, average drift time residuals of reconstructed hits were determined and used to correct for the t0 ’s. After two iterations, the obtained
t0 ’s became stable, and the obtained residual of hits and reconstructed tracks was about 200 µ m, as
shown in figure 40.
5.2 Tracking performance
As mentioned in section 3.3.3, the CTB tracking package was the default tracker in the cosmics
reconstruction. It was designed for scenarios where tracks do not point to the interaction point,
and where the initial alignment and calibration is either very rough or not available at all. For
example, it can find tracks in the TRT without using the drift time information at all. The outputof the algorithm consists of several different track collections. There are collections for the full
setup and for each individual subdetector (SCT, TRT). Furthermore there are separate collections
for the upper and lower part of the setup. The results in this section are based on global tracks, i.e.,
tracks where the hits in the entire setup were used. Figure 41 gives an example of such a global
reconstructed track. Figure 42 shows a shower in the upper TRT sector, reconstructed using only
the TRT hits in that sector.

– 46 –

2008 JINST 3 P08003

0-10-5 0 5 10 15 20 25 30 35

drift time [ns]drift time [ns]

number of hits8000

χ 2 / ndf

313.8 / 3

cnstnt8020 ± 62.2

mean 0.001242 ± 0.001439

7000

sigma

0.1997 ± 0.0020

1

2
3
4
5
Residual (mm)

6000
5000

3000
2000
1000
0
-5

-4

-3

-2

-1

0

Figure 40. Residual of reconstructed hits obtained after two iterations.

Event: JiveXML_2850_00023

Run: 2850

Event: 23-100

0

Y (cm)

100

ATLAS Atlantis 2006-06-02 17:45:23 MET DST

-100

0

X (cm)

100

Figure 41. A reconstructed cosmic track with both SCT and TRT hits.

– 47 –

2008 JINST 3 P08003

4000Events

25000
20000
Data
MC

15000
10000
5000
0

0

2

4
6
8
Number of tracks

Figure 43. Number of reconstructed tracks, for real and simulated data.

The performance of the pattern recognition was monitored mainly using event displays, andthe cuts were tuned accordingly. Figure 43 shows the number of reconstructed tracks for a given
run and for a simulated data sample. The muons in the simulated data sample were generated withan energy between 200 MeV and 200 GeV. About 25% of the recorded events do not have any
reconstructed tracks. Most of these events are caused by noise in the scintillator trigger setup, and– 48 –

2008 JINST 3 P08003

Figure 42. A cosmic shower with several tracks, reconstructed in the TRT.

Number of tracks

Number of tracks

6000
Data
MC

4000

2000

MC

2000

1000

0

5
10
15
20
Number of SCT hits on track

0

0

50
100
Number of TRT hits on track

Figure 44. Number of hits on track, for real and simulated data.

can be recognized by an unphysical value of the computed time-of-flight between two scintillator
planes. In the current analysis we keep only those events in which the measurement of the time-offlight gives a physical result. After applying this cut, it can be seen in figure 43 that the simulationand the real data agree fairly well on the number of reconstructed tracks. Inspection with the event
display shows that the events without a reconstructed track are either empty, or have a showerthat produces so many hits that no reconstruction can be attempted.13 Cosmic showers were notincluded in the simulated data, only single muons were considered.
Figure 44 shows the number of SCT and TRT hits on the reconstructed tracks. For this plot the
input sample was restricted to events with only one reconstructed track. The number of SCT hits
is almost always an even number, because each SCT module has two slides. The distribution of
the number of TRT hits has two peaks, because one half of the lower TRT sector was not read out
during data taking. Thus, tracks that pass through that region have half as many TRT hits comparedto the tracks that pass through the active region. The number of TRT hits on track is slightly higher
in real data than in the simulation. Apart from that, the agreement between data and simulation isreasonable.
5.3 SCT performance
The data collected with cosmic rays triggers can be used to check the performance of the SCT
detectors in terms of efficiency and resolution. Results are only shown for the barrel data since
the number of SCT hits associated with tracks in the endcap setup is too small to allow this kindof study. Tracks reconstructed taking only SCT hits as input are used in order to avoid any effectcoming from TRT-SCT misalignments. The SCT alignment corrections obtained with the global
χ 2 method are used unless otherwise specified since those led to better results. Comparisons withother alignment corrections and with simulation data are also shown.
13 CTB tracking skips the event if there are more than 120 space points in the SCT, or more than 500 hits in the TRT.

– 49 –

2008 JINST 3 P08003

0

Data

3000

×10

Entries/hit pattern

3

450
400
350
300
250
200
150

111

110

101

100

011

010

001

0

000

50

Hit patternFigure 45. SCT hit pattern.

5.3.1 Timing
The SCT was run in the expanded mode in the cosmic runs, which provides time information for
the triggered time bin as well as the preceding and following bins. Figure 45 shows the distributionof the hit patterns for a given run. The 010 hit pattern is dominant, indicating that the trigger istimed well with respect to the SCT modules. The 20 ns shaping time of the ABCD3TA chip will
also give rise to hits in adjacent time bins as the pulse duration above threshold can be longer thanthe 25ns bunch-crossing time window. Hit pattern 001 comes from cases where the hit is registeredin the next time bin due to a muon arriving late in the clock cycle. The majority of hits in each
event are noise hits, and thus not correlated with the trigger. Notice that there are also some hitswith the hit pattern 000. As the readout chip should only output hits from fired channels, this is nota valid hit pattern. Three modules showed this phenomenon in the tests at SR1, on a whole module,
on a chip, and on a single channel, respectively.
5.3.2 Efficiency
The efficiency of a given SCT module side was computed using the following method. For eachreconstructed track in an event that remains after a quality cut, the algorithm removed any hits
located on the SCT barrel layer under investigation, or the ith layer. A track refit was then performed
excluding these hits. From the perigee parameters of this new track, an extrapolation to the ith layer
was performed to obtain the intersection point or “predicted position” of a hit on a module. If thisintersection point was within the sensitive area of the module it was included in the denominatorof the efficiency calculation. If a SCT cluster was found to be located within a set distance fromthe predicted position, an entry was made for the numerator.
Various track quality cuts and fiduciality requirements were made before a hit prediction entered the denominator of the efficiency calculation:• the track must have 10 or more SCT hits
• the track fit must have a χ 2 /ndof ≤ 24

– 50 –

2008 JINST 3 P08003

100

Unbiased Hit Efficiency

1
0.998
0.996
0.994
0.992
0.99

2mm roadwidth

0.986

1.5mm roadwidth
1.0mm roadwidth

0.984

0.5mm roadwidth

0.982
0.98
0

99% eff
0.5

1

1.5

2

2.5

3
3.5
4
layer and side

Figure 46. The hit efficiency for the different barrel layers and sides from simulated cosmic data with pT
greater than 200 MeV. Also shown is the specification efficiency of 99% (dashed blue line).

• the track must have an incidence angle on the module of less than 20 degrees in the azimuthal
direction• the track intersection point on the module must be at least 2 mm from the bond gap and 1.5
mm from the edges of the module• the track intersection point must be at least 1.5 mm from any masked strips or chips
In order for a hit to be entered into the numerator of the efficiency calculation, in addition to
the above requirements, a hit must be found within a certain road width around the predicted hit
position. By default, and unless specified otherwise, this road width was 2 mm.
In order to choose a proper value for the cut applied to the distance from the predicted track
position and the cluster position, the simulation was used. Due to the lack of magnetic field, thisdistance can be quite large for tracks corresponding to low momentum cosmic rays. Figure 46
shows the average efficiency per SCT layer, for different distance cuts, for all simulated particles.
In order to neglect MCS effects, a cut of 2 mm was then chosen to compute efficiencies from realdata. The result obtained in this case is shown in figure 47, where after alignment the unbiased hit
efficiency in all barrel layers is measured to be within specifications, i.e., greater than 99%.
Since the cosmic trigger is not synchronized with the readout clock, the collected data canalso be used to study the dependence of the SCT efficiency with the arrival time of the particle withrespect to the readout time. This difference in time was measured and since the SCT is recording
the information of three consecutive bunch crossings, the measurement can be done over a time
range of 75 ns. Figure 48 shows the SCT efficiency obtained in the barrel section as a functionof the arrival time of the particle. The efficiency remains high for a range of 4 ns, compared to– 51 –

2008 JINST 3 P08003

0.988

Unbiased Hit Efficiency

1
0.998
Cosmic Data
0.996
0.994
0.992
0.99

0.986

Nominal alignment

0.984

Global Chi2 alignment

0.982
0.98
0

0.5

1

1.5

2

2.5

3
3.5
4
layer and side

Hit Efficiency

Figure 47. The unbiased hit efficiency measured for the different barrel layers and sides from cosmic data,
before and after alignment.

10.8
1XX+01X+001

0.6

001
01X
1XX

0.4
0.2
0

-70

-60

-50

-40

-30
-20
-10
0
SCT Time - Trigger Time (ns)Figure 48. The hit efficiency measured as a function of time between the SCT RDO hit and the scintillator
trigger. A road width of 2 mm and global χ 2 alignment corrections are used. The SCT time was 0 ns if the
SCT RDO hit pattern was 1XX, 25 ns if 01X, and 50 ns if 001.

approximately 20 ns from the test beam studies [57], due to the time-of-flight of the cosmic rays
not being taken into account.

– 52 –

2008 JINST 3 P08003

0.988

average Residual mean [mm]

0.002
0.001
0
-0.001-0.002-0.003
0

0.5

1

1.5

2

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3

3.5
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layer and side

0.1
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0

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layer and side

Figure 49. The unbiased residual means for the different layers and sides of the SCT modules for simulation(triangle) and data using global χ 2 alignment (squares). A road width of 2 mm is used.

5.3.3 SCT resolution
The SCT detector resolution can be extracted from the σ of the SCT residual distribution of a given
module side after subtracting the track prediction and alignment corrections uncertainties. However, as mentioned above, the track uncertainty is not well estimated for the case of low momentum
particles. Figure 49 shows the mean and σ of the unbiased residuals measured for data (using
global χ 2 alignment constants) and Monte Carlo simulation. For both the data and simulation themeasured residuals are much larger than the expected detector resolution of 23 µ m due to the effectof multiple scattering.
Figure 50 shows for simulated data the σ of the SCT residual and pull distribution as a functionof the true particle energy. The σ of the residual distribution increases for low energy tracks andfor this case the σ of the residual pull distribution is no longer 1 due to the already mentioned
wrong track uncertainty estimation. For high momentum tracks, a σ of around 28 µ m is obtained.
After subtracting in quadrature the track uncertainty contribution (which is estimated to be around
16 µ m), a value of 23 µ m is obtained in agreement with the intrinsic detector resolution.
In order to determine the detector resolution from the data, the σ of the residual distribution
as a function of the unbiased track χ 2 (i.e., removing the contribution to the χ 2 of the hit underevaluation) was measured, for side 1 of modules in layer 2. These modules were chosen to maximise statistics, and minimize track errors. The result obtained is shown in figure 51 for real and– 53 –

2008 JINST 3 P08003

average Residual sigma [mm]0.003

SCT Resolution as a function of energy

SCT pulls as a function of energy

σ [mm]σ [mm]

0.11
0.1

3.5

0.09
3
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0.07
0.06

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2000 4000 6000 8000 10000 12000 14000 16000 18000 20000
E [MeV]

Residual Sigma (mm)Figure 50. σ of SCT unbiased residuals (left) and pull distributions (right) as a function of true particle
energy.

0.12
0.1

data
MC

0.08
0.06
0.04
0.02
0

0

1 2 3

4 5

6 7

8 9

Figure 51. Width of the SCT unbiased residuals, as a function of the unbiased track χ 2 per number ofdegrees of freedom, for real and simulated cosmic data.

simulated data. When the χ 2 tends to zero, the contribution of low momentum tracks should be
negligible. A σ of 24 ± 1 µ m (20 ± 2 µ m) is obtained for real (simulated) data for tracks with a χ 2
per degrees of freedom of less than 1. For these tracks the contribution to the residual from track
uncertainty is negligable. Alignment uncertainties have not been considered resulting in slightly
larger residuals in real data than in simulation. The low measured residuals are due to the large
number of double clusters present in both data and simulation. Approximately one in three of theclusters in the SCT is a two hit cluster, in both data and simulation. This means that most tracks
intercepting the module near the edge of the strip will produce two strip clusters. This effectively
reduces the resolution of the one-strip cluster measurement.

– 54 –

2008 JINST 3 P08003

2000 4000 6000 8000 10000 12000 14000 16000 18000 20000
E [MeV]5.4 TRT performance
5.4.1 TRT efficiency• Full SCT+TRT tracks were reconstructed and, to suppress events generated by cosmic showers, only events with a single track were selected,• SCT track segments from the top and bottom sectors were combined and the resulting SCT
tracks were required to be of high quality (χ 2 /ndof < 5),• SCT tracks were extrapolated to the TRT. The distance between the extrapolated track intersection at the last straw layer (corresponding to the maximum lever arm) and the closest hit
in the same straw layer was required to be less than 0.3 mm.
Monte Carlo studies have shown that this selection procedure reduces the presence of tracks
at the low end of the momentum spectrum. These tracks are most affected by multiple scattering.
Most importantly, no requirements were made on the quality of the TRT segments themselves, inparticular no cuts were applied to the χ 2 of the TRT (or SCT+TRT) tracks. In this way the bias
on the efficiency induced by the track selection is kept to a minimum for layers other than the laststraw layer.
For each straw the efficiency was defined as the ratio between the number of “expected” hits
(i.e., the number of tracks passing closer than a certain distance R to the straw wire) and the total
number of recorded hits. In figure 52 the distribution of the straw efficiencies (calculated for
R = 2 mm, the straw radius) is shown. To reduce statistical fluctuations only straws with at least 40
expected hits were considered. The mean of the efficiency distribution is approximately 93%.
Due to the statisical nature of the ionization process, the efficiency of a straw tube depends
on the path length of the track inside the straw. Straws with a track passing close to the edge are
expected to be less efficient than straws having tracks passing well inside their volume. In figure 53
the efficiency profile as a function of the track distance R to the wire is shown. The efficiency isvery close to one if the tracks pass well inside the straw (R . 1.7 mm) while it drops for tracks close
to the straw boundary. The shape of the efficiency profile for distances close to the tube boundary
is determined by properties of the ionization gas and by the track extrapolation uncertainties. A
non-zero efficiency for R significantly outside the straw radius is almost exclusively due to noise.

– 55 –

2008 JINST 3 P08003

The data collected in the SR1 test were also used to study the efficiency of the TRT straw tubes.
Because of the limited statistics in the endcap, this study was only performed for the barrel.
It is important to note that, given the lack of track momentum information, an accurate measurement of the straw efficiency with cosmic data is difficult. Multiple scattering effects cannot be
accounted for and hence there is a high uncertainty on the relative position of the passing particle
with respect to the straw tubes. On the other hand, knowing whether a particle passes through theactive region of a straw or not, is essential in determining its efficiency.
For this reason particular care was devoted to selection of tracks for this study in order toreduce the uncertainties resulting from multiple scattering. The detector geometry was correctedusing the procedure described in section 5.1.5 for simultaneously aligning the SCT and TRT.
The track selection proceeded as follows:Efficiency Distribution

0.4

0.2

0

0

0.5

1
Straw Efficiency

Straw efficiency

Straw efficiency

1

0.5

1

10-1

10-2
0

-2

0
2
Track Distance (mm)

0

10

20
Track Distance (mm)Figure 53. Left: Straw efficiency profile as a function of the track-wire distance R in real data. The different
distributions denote the TRT module types (inner, middle, and outer). Right: Same distribution as in the leftplot, shown on a logarithmic scale and for extended R values. Red is simulation, black is real data.

A random noise throughout the detector is expected to contribute to the efficiency independentof the track distance R to the straw. However, in real data an R-dependent noise is observed. As
is clear from the right plot in figure 53, the efficiency for straws with a track outside their physicalvolume (R & 2 mm) but close to the straw (R . 10 mm) is higher than that expected from random
noise. This can be attributed to channel cross-talk, where a high signal amplitude registered in
one straw induces a noise signal on neighboring straws, in particular to straws connected to thesame readout chip or HV pad. This effect has been already observed in TRT prototypes in test
beam data [59]. The peculiar structure observed in real data (efficiency dip at R ∼ 3.7 mm) is
generated by the arrangement of the straws inside the TRT modules and by the straw geometricgrouping of readout chips/HV pad. As can be noticed from the efficiency profile obtained with
Monte Carlo events, such cross-talk effects are not included in the current version of the TRT
simulation. However the simulation correctly reproduces the magnitude of the random noise, whichfrom this study is shown to be of the order of 2% (in agreement with that quoted in section 4.2).
5.4.2 TRT resolution
As in the case of the SCT, the hit resolution of the TRT can be extracted by subtracting the track
uncertainty from the width of the residual distribution, in quadrature. Due to the large number of– 56 –

2008 JINST 3 P08003

Figure 52. Efficiency distribution for straws having at least 40 “expected” hits for a distance R < 2 mm.

pull

resolution (mm)

1

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4

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0
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5000

10000 15000 20000
E (MeV)

0
0

5000

10000 15000 20000
E (MeV)

Figure 54. Width of the TRT unbiased residuals and pulls, as a function of energy, for simulated tracks.

hits on a TRT track, the calculated track uncertainty is fairly small compared to the hit resolution.
The contribution from multiple scattering to the residuals is also relatively small, since the TRT
hit resolution is a factor 10 higher than the SCT hit resolution. Thus, the width of the residual
distribution is a good measure for the TRT hit resolution.
Figure 54 shows the widths of the residual and pull distributions as a function of energy, forsimulated tracks in the upper half of the TRT. The plots are nearly flat except at the lowest energybin, showing that the effect of multiple scattering is indeed limited. It should be noted though thatthe tails increase significantly at low momentum.14 The hit resolution assumed in the track fit is
170 microns, which explains why the pulls are always larger than 1 since the actual hit resolution
is close to 200 microns.
Figure 55 shows the residual distribution for real and simulated tracks in the upper half of the
TRT, and the width of the residual distributions per module type (inner, middle and outer). For the
distributions the input data sample was restricted to events where the SCT track has a reduced χ 2smaller than 2, in order to remove low energy tracks from the sample. The small dependence of theresidual width on the module type is due to the uncertainty of the predicted track position, whichis smallest near the middle of the track. It is seen that the TRT performance is slightly better inreal data than in the simulation. It should be remembered that the TRT resolution shown above wasobtained with an Argon-based gas mixture. In ATLAS, where a Xenon-based gas mixture will be
used, the expected resolution is of the order of 130 microns [46].

6. Conclusions
The combined operation of the ATLAS Semiconductor Tracker (SCT) and Transition Radiation
Tracker prior to their installation in ATLAS were investigated. This test concluded the integration
14 Tails are not taken into account here, only the core of the distributions is fitted.

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2008 JINST 3 P08003

0.8

3000

µ

0.00 ± 0.00

σ

0.20 ± 0.00

residual width (mm)Number of hits

4000

Data
MC

2000

0
-2

Data

0.4

MC

0.3
0.2
0.1

-1

0
1
2
r = Rtrack - Rhit (mm)

0

0

1

2
module layer

Figure 55. Comparison of the real and simulated TRT unbiased hit residuals.

process of these two tracking subdetectors and provided the first opportunity to test the combined
tracking of SCT and TRT with cosmic rays in a large test stand at the surface integration facility.
The four months combined operation of the SCT and TRT allowed the commissioning and thoroughtesting of the hardware for detector supply, control, and readout systems for the SCT and TRT in a
final detector configuration. The experience of this operation led to numerous improvements in thedetector control, calibration, and physics readout software.
The noise performance of SCT and TRT in the final detector configuration was tested. The
tests showed on the SCT a noise occupancy of 4.7×10−5 and equivalent noise charge of about
1650 electrons for warm operation with hybrid temperatures approximately 25 ◦ C above their final
cold operation temperature in the experiment. These results are consistent with previous assembly
test results on single barrels or disks and individual modules during production, showing that thesimultaneous operation of four barrels (nine disks) did not lead to a noise increase. The noise of
the TRT barrel and endcap was investigated through a series of test runs with the SCT off and with
the SCT operated synchronously with the TRT. The tests showed no increase of noise on the TRT
through a series of tests with SCT in on and off states. The frontend electronics noise of SCT and
TRT during the active readout cycle of the respective other subdetector was tested and the absenceof cross-talk between SCT and TRT in the combined test was confirmed.
During the combined tests of barrel and endcap large samples of cosmic ray events werecollected. Those events were used to commission the online monitoring software and offline trackreconstruction software chain, which will be used for LHC collisions. The online monitoring was
operated at the event filter level as well as standalone. Reconstructing the cosmic tracks in SCT
and TRT, individually and combined, allowed the successful testing of raw data processing, trackreconstruction, and detector alignment methods with real data for the first time. This has led to theextraction of first preliminary results for basic tracking performance parameters. A single plane
efficiency of 99% and spatial resolution matching expectations from simulation for the SCT were

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2008 JINST 3 P08003

1000

0.5

found. The TRT drift time resolution of ≈200 µ m agrees with the expected value for an argonbased gas mixture. Similarly, the measured straw efficiency is in agreement with expectations.

Acknowledgments

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We are greatly indebted to all the technical staff who worked on the integration and testing of the
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