cms b-tagging and tracking commissioning and...

CMS b-Tagging and Tracking Commissioning andCosmics

Jean-Roch Vlimant∗†University Of California Santa BarbaraE-mail: [email protected]

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/

mailto:[email protected]

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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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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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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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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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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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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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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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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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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