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CMS b-Tagging and Tracking Commissioning andCosmics
Jean-Roch Vlimant∗†University Of California Santa BarbaraE-mail: vlimant@cern.ch
The construction of the CMS experiment was completed in 2008, and started taking data recordingparticles coming from cosmic showers in the year 2008 and 2009. We describe how chargedparticle are reconstructed with the CMS inner tracking system. Performance from simulation iscompared to the one observed in cosmic data, showing that startup conditions are very close toideal design. Jet flavor tagging at CMS is presented, along with simulated performance, expectedperformance at startup and plans for commissioning of jet b-tagging.
XXth Hadron Collider Physics SymposiumNovember 16 – 20, 2009Evian, France
∗Speaker.†For the CMS collaboration. Special thanks to Kevin Burkett.
c© Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike Licence. http://pos.sissa.it/
CMS b-Tagging and Tracking Commissioning and Cosmics Jean-Roch Vlimant
Figure 1: Layout of the layers of silicon sensors in the CMS inner tracking system. Single sided modules(red), stereo modules (blue) and pixel modules (green) are grouped in the different tracker sub-systems.Tracker inner barrel (TIB), tracker outer barrel (TOB), tracker inner disk (TID), tracker endcap (TEC) arelabeled on this quadrant representation.
1. Track Reconstruction at CMS1
The CMS experiment is a all-purpose detector [1] which consists , from outside inward, of2
a muon system concentrating the return flux of the central solenoidal magnetic field, a hadronic3
calorimeter, an electromagnetic calorimeter, a pre-shower (end cap only) and an inner tracking4
system. We will concentrate on track reconstruction using the later.5
1.1 The CMS Inner Tracker6
The CMS inner tracking system consists of a silicon strip tracker and a silicon pixel detector7
(see fig. 1) placed within a superconducting solenoid. The magnetic field operates at 3.8 Tesla over8
the tracking volume.9
The pixel detector has 3 barrel layers and 2 end cap disks on each side, with a total of about10
66 million channels from pixels of size 100×150 µm2. It provide precise 3D measurement points11
close to the interaction point, useful for reconstruction of primary and secondary decay vertices, as12
well as very low pT track reconstruction.13
The strip tracker has 10 barrel layers and 12 end cap disks on each side, with a total of about14
9.3 million channels from silicon strip which pitch ranges from 80 to 150 µm. 4 of the barrel layers15
and 3 rings of the end cap disks have double sided modules mounted with a stereo angle to provide16
3D measurement points. The rest of the modules provide 2D measurement points.17
The inner tracking system [1] therefore provide a few (up to about 30 with double sided and18
module overlap) but precise measurement points. An inconvenience of CMS silicon tracker is the19
rather large, for a tracking device , amount of dead material involved for support, cooling and20
cabling [1].21
1.2 The CMS Tracking Software22
The CMS collaboration has developed several tracking algorithms, some for general track23
reconstruction and some for the reconstruction of tracks in specific cases. This paper concentrates24
on the main algorithm in the CMS reconstruction: the combinatorial track finder (CTF) used in25
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CMS b-Tagging and Tracking Commissioning and Cosmics Jean-Roch Vlimant
iterative steps. The different steps work on different subsets of single hits, with different set of26
track parameter phase space boundaries set. For simple events as cosmic events, with mainly a27
muon track in the detector, a dedicated algorithm [2] has been design to perform brute force single28
track pattern recognition (called cosmic TF). Results from this algorithm are compared to results29
from the main algorithm in section 3.2.30
The "Road Search" algorithm is able to reconstruct general purpose tracks in CMS, but is31
not part of the reconstruction because the gain in efficiency, on top of the CTF, is not worth the32
computing consumption. The gaussian sum filter algorithm is used [3] specifically for electron33
track reconstruction. The deterministic annealing filter and the multi-track finder are used [3] to34
perform track reconstruction within the dense environment of jets.35
Combinatorial Track Finder36
The CTF algorithm consists of 3 stages37
1. Seeding Pair or triplet of hits are used in combinatorics, using compatibility with the inter-38
action region or primary vertex depending on the iterative step.39
2. Pattern recognition From the initial track parameters of the seeds, hits are searched for in40
reachable layers, and added, using the Kalman update, to the trajectory candidate based on41
the chi squared compatibility between the hit and the predicted track positions.42
3. Final fit The collection of hits found in pattern recognition is refitted and smoothed to pro-43
vide the best track parameter estimation at each measurement point.44
The module alignment, the non-uniformity of the magnetic field, a detailed description of the ma-45
terial budget are taken into account during these processes.46
Iterative Tracking47
The main limitation of the CTF algorithm is the combinatorial explosion when trying to re-48
construct too many tracks at the same time. In order to limit the combinatorics, while increasing49
the track parameter phase space reach, after completion of the first iteration of CTF (3 stages de-50
scribed above), hits belonging to high quality tracks are removed from the hits available to be used51
in finding track in the next steps. Additional iterations of the CTF is then performed (6 in total).52
2. Expected Performance from Simulation53
The tracking efficiency is measured from simulation of various proton collisions final states54
and shown in figure 2. The resolution on transverse momentum, transverse and longitudinal impact55
parameters are shown in figure 2 and 3.56
The efficiency to reconstruct muon tracks (10 GeV pT) is very close to 100%, while it is a57
bit lower for pions (10 GeV pT) because of a larger chance of nuclear interaction resulting in too58
few hits belonging to the track. The track efficiency for pions decreases in the end-cap because of59
the large amount of material within the pixel detector volume.The probability to reconstruct a fake60
track in a top pair production event (used as a benchmark for crowed event) is low but increases in61
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CMS b-Tagging and Tracking Commissioning and Cosmics Jean-Roch Vlimant
Figure 2: CMS track reconstruction efficiency and fake rate (left) for different type of particles and trans-verse momentum resolution (right) for muon track of 1, 10, 100 GeV of transverse momentum. Both plotsare as a function of pseudo rapidity.
Figure 3: Transverse (left) and longitudinal (right) impact parameter resolution for muon track of 1, 10, 100GeV of transverse momentum, as a function of pseudo rapidity.
the region of transition between barrel and disk, where the amount of crossed material is maximum,62
increasing the chance of nuclear interaction and conversions, incresaing the combinatorics.63
The transverse momentum of muons is measured to the percent level up to 100 GeV. This64
resolution degrades gradually due to the change of strip pitch in the TEC, and degrades even more65
in the forward region because of the smaller lever arm when the track exits through the last layer66
of TEC.67
The good coverage of the pixel detector allows to measure transverse impact parameter with a68
resolution of 10 to 100 microns, depending on transverse momentum.69
The longitudinal impact parameter is measured with slightly worse resolution, 20 to 600 mi-70
crons, and gets a factor two worse for vertical collision tracks because of the presence of mono71
pixel cluster which single hit resolution is worse without charge sharing.72
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CMS b-Tagging and Tracking Commissioning and Cosmics Jean-Roch Vlimant
Figure 4: Distribution of the mean of residuals on the expected hit position in the transverse local coordinatefor pixel barrel (left) and tracked outer barrel (middle). Table of the RMS of the distribution of median ofresiduals and number of modules for all sub-part of the inner tracker (right).
Figure 5: Single hit efficiency in the pixel (left) detector as a function of detector coordinate in the firstbarrel layer (crossed modules are know to have hardware problems) and in the strip (right) detector as afunction of the layer for data taken with magnetic field off and on, and adding modules with known issues.
3. Observed Performance with Cosmic Data73
During the year 2008 and 2009 the CMS experiment has been taking cosmic data for com-74
missioning [4]. Two campaigns of stable data taking with the solenoid powered were performed,75
during each of which about 107 tracks passing through the tracker were recorded.76
3.1 Alignment and single hit performance77
The alignment of the CMS inner tracker was performed using cosmic data [5] and compared78
with alignment in simulated data (see fig 4). The precision of the alignment obtained is very close79
to the ideal geometry of the tracker. Tracking performance, presented in next sections, is expected80
to be very close to the design performance.81
Single hit efficiency is evaluated from cosmic data [6, 2] propagating tracks to all reachable82
modules of the tracker, and checking that there is a corresponding hit on the module. To avoid83
edge effect, the propagated state is required to be well within the module boundaries. The single84
hit efficiency (see figure 5) is around 97% and 99% in the pixel and strip detector respectively. The85
inefficiencies are traced down to module with hardware issues, for which reparation is underway.86
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CMS b-Tagging and Tracking Commissioning and Cosmics Jean-Roch Vlimant
Figure 6: Barrel pixel single hit resolution observed in cosmic data in the direction transverse (top left) andalong (top right) to the beam pipe. The resolution expected from simulation is 22 µm and 28 µm in X and Yrespectively. At the bottom, table of barrel strip single hit resolution observed in cosmic data and simulation,as a function of the angle with respect to the normal to module surface. The resolution is optimum formedian normal angles for which charge sharing is optimum [check that again please].
Single hit resolution was evaluated in the pixel [6] and the strip tracker [2] using the "double87
difference method". In regions where two hits per layer are expected, the RMS of the difference88
between the difference of hit position and the difference of track predicted position, contains a com-89
ponent from the track position uncertainty and a component from the single hit position resolution.90
Single hit position resolution is extracted assuming the track position uncertainty from the track fit91
itself. Results are reported in figure 6, showing good agreement with simulation. Performance is92
close to design [1].93
3.2 Track Reconstruction Efficiencies94
Several methods were used to estimate the track reconstruction efficiency in CMS using cos-95
mic data [2], also comparing results from two different algorithms for cross checks. Results are96
presented in figure 7, showing overall good agreement with simulation and very good track recon-97
struction efficiency.98
• Muon Matching Method. Track reconstruction can be performed independently in the inner99
tracking system and in the muon system. The stand alone muon track reconstruction in the100
muon system is used to probe the reconstruction in the inner tracking system.101
• Top-Bottom Tracking Method. Cosmic track reconstruction is performed independently102
in both hemisphere of the tracker, and track from the top is used to probe the presence of a103
corresponding track in the bottom (and vice-versa).104
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CMS b-Tagging and Tracking Commissioning and Cosmics Jean-Roch Vlimant
Figure 7: Cosmic track reconstruction efficiency, observed in cosmic data and simulation, as a function oftrack transverse momentum, for the CTF and the single track pattern algorithm, using the muon matching(left), top-bottom (middle) and collision like recconstruction (right) methods. Efficiency for the top-bottommethod is given separately for the bottom (middle top) and the top (middle bottom).
Figure 8: Transverse impact parameter (left) and momentum (right) resolution on cosmic track reconstruc-tion as a function of transverse momentum resolution observed in cosmic data and simulation.
• Collision Like Reconstruction Method. The CTF algorithm applied to cosmic event recon-105
struction is used with a specific seeding from outer layers of the tracker, different from the106
configuration for reconstructing tracks from collision (using innermost layer of the tracker).107
Collision like tracking can be ran in cosmic events and reconstruct the "upper" and "lower"108
part of a cosmic track crossing the tracker as two independent tracks. Each track is used to109
probe the presence of a corresponding track on the other side.110
3.3 Track Parameter Resolution111
The resolution on the reconstructed track parameters is estimated using a method that applies112
only to cosmic tracks. The track of a muon crossing the tracker from top to bottom is split into two113
tracks at the point of closest approach. The two tracks obtained therefor measure the same ideal114
track parameters and the width of residual (divided by√
2) give an estimation of the resolution of115
track reconstruction (seed figure 8). The resolution on transverse momentum and transverse impact116
parameter in cosmic data is observed to be in good agreement with simulation and very close to the117
design resolution.118
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CMS b-Tagging and Tracking Commissioning and Cosmics Jean-Roch Vlimant
4. Jet b-Tagging at CMS119
Except for the top quark, quarks and gluons produced at vertexes hadronise into colorless jets120
of hadronic particles. The flavor of the initial quark at the origin of a jet can be identified with121
characteristics of the jets using tagging algorithms. Because of its intrinsic lifetime and how it122
decays semi-leptonically, the b-jets can be distinguished from light quark jets. Identification of123
b-jets is primordial in many physics analysis to improve the signal to noise ratio.124
4.1 CMS Tagging Algorithms125
The algorithms described below are available in the high energy physics community they have126
been refined and extended for the use in CMS [7]. They are described in order of complexity.127
• Track Counting Requiring n tracks within a jet to have large impact parameter significance128
is a robust way tagging a b-jet. With two tracks, one gets high efficiency while with three129
tracks provides high purity.130
• Track Probability Calculating the combined probability that each track within the jet is131
compatible with the primary vertex provides a handle on discrimination of b-jets from other132
jets.133
• Soft Lepton Tagging The presence of a lepton within a jet is an indication of the b-flavor134
of a jet. The momentum of the lepton relative to the jet can be use alone or combined with135
other information into a neural net to discriminate light flavor jet from b-jets.136
• Secondary Vertex Tagging Secondary vertex within jets can be reconstructed. The distance137
significance between the primary and secondary vertex is a simple method of b-tagging.138
Combining information from the secondary vertex into a likelihood ratio is a bit more com-139
plex but provides better performance (see figure 9).140
4.2 Expected Performance141
b-tagging performance cannot be determined from cosmic data because of the absence of b-jets142
in remnant of cosmic shower reaching the CMS detector. The performance of b-tagging expected143
from simulation [7] is summarized in figure 9. Simple and robust algorithms like track counting144
have a good expected performance, a bit worse than complex method like secondary vertex tagging.145
As b-tagging methods highly depends on efficient tracking with good resolution, it is expected that146
the observe performance of b-tagging will agree with these performance from simulation.147
4.3 Commissioning with Collision Data148
As soon as enough collision data with jet activity will be recorded, performance of b-tagging149
algorithm can be measured.150
• Negative Tag Sample [8] A sign can be given to the transverse impact parameter of a track151
within a jet with respect to the jet axis. Tracks with negatively signed impact parameters are152
likely to come from resolution effect on track from the primary vertex of the jet. Therefore,153
a sample of jets constructed with negative impact parameter is enriched in light-flavor jets,154
and provides a simple way to estimate the mis-tag rate.155
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CMS b-Tagging and Tracking Commissioning and Cosmics Jean-Roch Vlimant
Figure 9: Probability of mistagging a light-quark jet as b-jet versus the efficiency of tagging a b-jet as such,plotted for several b-tagging algorithms in CMS reconstruction (not all described in the text, see [7] fordetails).
• Muon-in-Jet sample [9] The presence of a muon inside a jet is an indication of b-flavor.156
Therefore, a sample of jets constructed with the request of a muon being present is enriched157
in b-jets. A template fit applied based on the transverse momentum of the muon relative to158
the jet (prelT ), before and after the application of the tagger allows a determination of the b-159
tagging efficiency. The so-called "system 8" method uses two stages of b-tagging with loose160
and tight requirements to solve a system of equations providing an estimation of taggers161
efficiency.162
• Top Pair Production Sample[10] Top pair production sample is bound to have at least two163
b-jets because the lifetime of the time quark is smaller than the hadronisation time-scale and164
the top quark decays mainly electro-weakly into a b quark and a W boson. From the top pair165
decays channel classification, the all hadronic (when both W decay into quarks) channel has166
a very low signal to noise ratio and is not used. Therefore, a sample selected based on the167
signature of at least a leptonic decay of a W boson, with the addition of a b-tagged jet and a168
kinematic fit of the final state objects to top pair decay products, is enriched in at least one169
b-jet which can be use to estimate the b-tagging efficiency.170
5. Conclusions171
Track reconstruction with the CMS inner tracking system has been presented. The tracker172
performance observed during cosmic data taking has been shown to be close to the design perfor-173
mance, and in good agreement with simulation. CMS has a variety of algorithm to determine the174
b-flavor of the quark from which jets originate. Methods to evaluate b-tagging performance with175
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CMS b-Tagging and Tracking Commissioning and Cosmics Jean-Roch Vlimant
LHC collision data have been presented. In regards to the tracking performance in cosmic data, it176
is expected that its performance with collision data will be close to the design performance, which177
has been presented.178
References179
[1] CMS Collaboration, The CMS experiment at the CERN LHC, JINST 3 (2008) S08004.489.180
[2] The CMS Silicon Strip Tracker Operation and Performance With Cosmic Rays in 3.8 T Magnetic181
Field, submitted to JINST (2009).182
[3] Track Reconstruction in the CMS Tracker, CMS Note 2005/14.183
[4] The CMS CRAFT Exercise, submitted to JINST (2009).184
[5] Alignment of the CMS Silicon Tracker During Commissioning with Cosmic Ray Particles, JINST185
2009.186
[6] Performance of the CMS Pixel Tracker with Cosmic Rays, submitted to JINST (2009).187
[7] A note BTV-09-001.188
[8] The CMS Collaboration, Evaluation of udsg Mistags for b-tagging using Negative Tags, BTV-07-002.189
[9] The CMS Collaboration, Performance Measurement of b tagging Algorithms Using data containing190
muons within Jets, BTV-07-001.191
[10] S. Lowette, J. DÕHondt, J.Heyninck, P. Vanlaer, Offline Calibration of b-Jet Identification192
efficiencies, CMS Note 2006/013.193
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