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Cluster Quality in Track Fitting for the ATLAS CSC Detector. IEEE - NSS San Diego, 30 October 2006. David Primor 1 , Nir Amram 1 , Erez Etzion 1 , Giora Mikenberg 2 , Hagit Messer 1 1. Tel Aviv University – Israel 2. Weizmann Institute of Science - Israel. Outline.

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cluster quality in track fitting for the atlas csc detector

Cluster Quality in Track Fitting for the ATLAS CSC Detector

IEEE - NSS

San Diego, 30 October 2006

David Primor1, Nir Amram1, Erez Etzion1, Giora Mikenberg2, Hagit Messer1

1. Tel Aviv University – Israel

2. Weizmann Institute of Science - Israel

outline
Outline
  • The CSC local tracking problem
  • The algorithms approach
  • The use of cluster quality
  • Fitting comparison
  • Conclusions

E. Etzion, Cluster Quality for tracking at ATLAS CSC

the atlas detector
The ATLAS detector

The Muon spectrometer

The CSC detector

E. Etzion, Cluster Quality for tracking at ATLAS CSC

the csc signals
The CSC signals

The maximum charge distribution over the strips:

2.54 mm

5.08 mm

The signal shape in time for a single strip:

[ns]

E. Etzion, Cluster Quality for tracking at ATLAS CSC

muon tracks
Muon tracks

E. Etzion, Cluster Quality for tracking at ATLAS CSC

muon tracks in a presence of high radiation background
Muon tracks in a presence of high radiation background

E. Etzion, Cluster Quality for tracking at ATLAS CSC

the tracking problem
The tracking problem
  • Estimating the number of tracks
  • Estimating the hits positions
  • Associating hits and tracks
  • Estimating the track parameters

E. Etzion, Cluster Quality for tracking at ATLAS CSC

the detect before estimate approach

Input: Raw Data

Output/Input: Rough tracks

Output: Fine tracks

Track finding

Cluster finding and parameter estimation

Line fitting

The detect-before-estimate approach

Activity detection within time interval

Stage 1

Stage 2

E. Etzion, Cluster Quality for tracking at ATLAS CSC

mwpc and fitting techniques
MWPC and fitting techniques
  • In order to study the possible contribution of the hit clusters quality, we simulate general MWPC detector.
  • Discuss the benefits of using the quality and compare different fitting techniques.
  • Utilize the ATLAS CSC line fitting to demonstrate the cluster quality ideas.

E. Etzion, Cluster Quality for tracking at ATLAS CSC

the simulation
The simulation
  • The simulation produced muon tracks with random parameters (5000 events)
  • The muon leaves a cluster of hits in each layer it crosses.
  • There are two types of hit clusters: clean clusters with probability and dirty ones with probability . The clean cluster has a position error distribution The dirty one has a position error distribution
  • We chose:

E. Etzion, Cluster Quality for tracking at ATLAS CSC

calculating the cluster quality
Calculating the cluster quality
  • A “clean” cluster is:
  • Contains only “in time” strips.
  • Well separated from other clusters.
  • Follow the Matheison distribution.
  • A “dirty” cluster is:
  • Contains “mask” strips or
  • not well separated from other clusters or
  • does not Follow the Matheison distribution.

In time + mask hit

[x 25 ns]

E. Etzion, Cluster Quality for tracking at ATLAS CSC

equal detection probability
Equal detection probability
  • We assume that the probabilities of dirty and clean hit detection are identical:

E. Etzion, Cluster Quality for tracking at ATLAS CSC

dirty clusters rate
Dirty clusters rate

From test beam data (about 3KHz/cm2 radiation background)

About third of the muon clusters are “dirty”

E. Etzion, Cluster Quality for tracking at ATLAS CSC

calculating the quality the model
Calculating the quality – The model

Spatial signal Matheison shape

noise

The Model:

Amplitude

Hit position

The ML:

E. Etzion, Cluster Quality for tracking at ATLAS CSC

calculating the quality
Calculating the quality

The solution:

The quality:

E. Etzion, Cluster Quality for tracking at ATLAS CSC

quality of clusters
Quality of clusters

Possible threshold value

E. Etzion, Cluster Quality for tracking at ATLAS CSC

different fitting methods
Different fitting methods
  • Least Squares (LS) – all points are used with equal weights in the track fitting process.
  • WLS – the “dirty” clusters gets reduced weight than the “clean” clusters, according to the optimal solution:
  • Robust fitting – iterative procedure which recalculate the weights according to the residual between the hits and the estimated track.
  • Iterative LS – omitting the point with the higher residual in each iteration.
  • Restricted LS – taking only the “clean” clusters.

E. Etzion, Cluster Quality for tracking at ATLAS CSC

simulation results for different layer number
Simulation results for different layer number

Good probability

Residual between real and estimated track

Quality prob.

Number of layers

E. Etzion, Cluster Quality for tracking at ATLAS CSC

discussion number of detection layers
Discussion- number of detection layers
  • The use of the hit quality improves the fitting results.
  • Good fitting results, in a presence of radiation background, can be achieved using more then 7 layers. If the number of layers is less then 6, the performance is reduced.
  • The iterative and Robust fitting techniques improve the LS fitting results when the number of layers is greater than 5.
  • The ATLAS CSC has only 4 layers…

E. Etzion, Cluster Quality for tracking at ATLAS CSC

simulation results for different contamination level radiation background
Simulation results for different contamination level (radiation background)

Number of layers = 8

Residual between real and estimated track

E. Etzion, Cluster Quality for tracking at ATLAS CSC

discussion radiation background level
Discussion- radiation background level
  • The use of the hit quality improves the fitting results.
  • There is no significant performance difference for results of contamination factor between 0 to 30%, when the fitting techniques use the hit quality (WLS, Robust+WLS, Restricted).
  • The performance of the algorithms that use the hit quality is similar.
  • The LS fitting technique gets the worst results.

E. Etzion, Cluster Quality for tracking at ATLAS CSC

simulation results for different probability of detection
Simulation results for different probability of detection

Residual between real and estimated track

Number of layers = 8

E. Etzion, Cluster Quality for tracking at ATLAS CSC

discussion detection probability
Discussion- detection probability
  • The use of the hit quality improves the fitting results.
  • The probability of detection affect only the techniques that use the hit quality.
  • If the detection probability is lower then 0.8 the fitting performance is reduced significantly.

E. Etzion, Cluster Quality for tracking at ATLAS CSC

fitting results for test beam data with photon interference source
Fitting results for Test Beam data with photon interference source:

Track fitting efficiency – less then 5 sigma (of the chamber resolution) from the real track

E. Etzion, Cluster Quality for tracking at ATLAS CSC

discussion csc
Discussion - CSC
  • The track fitting can be significantly improved using the cluster quality based on time shape and the likelihood to the ideal Matheison shape.
  • The restricted method gets the best results (using only the clean clusters).
  • Where there are less than two clean cluster for a track candidate, it is not possible to produce high quality track. The clean cluster should be used, however, in the overall muon spectrometer track fitting.
  • While the CSC has only 4 layers. Depending on the background level of the LHC, larger number of layers could improve tracking efficiency

E. Etzion, Cluster Quality for tracking at ATLAS CSC