1 Cluster Quality in Track Fitting for the ATLAS CSC Detector 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 IEEE - NSS San Diego, 30 October 2006

IEEE-NSS, E. Etzion, Cluster Quality for tracking at ATLAS CSC2 Outline The CSC local tracking problem The algorithms approach The use of cluster quality Fitting comparison Conclusions

IEEE-NSS, E. Etzion, Cluster Quality for tracking at ATLAS CSC3 The ATLAS detector The Muon spectrometer The CSC detector

IEEE-NSS, E. Etzion, Cluster Quality for tracking at ATLAS CSC 4 The CSC signals The maximum charge distribution over the strips: The signal shape in time for a single strip: [ns] 2.54 mm 5.08 mm

IEEE-NSS, E. Etzion, Cluster Quality for tracking at ATLAS CSC Muon tracks

IEEE-NSS, E. Etzion, Cluster Quality for tracking at ATLAS CSC Muon tracks in a presence of high radiation background

IEEE-NSS, 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

IEEE-NSS, The detect-before-estimate approach Activity detection within time interval Track finding Line fitting Cluster finding and parameter estimation Stage 1 Stage 2 Input: Raw Data Output/Input: Rough tracks Output: Fine tracks E. Etzion, Cluster Quality for tracking at ATLAS CSC

IEEE-NSS, 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

IEEE-NSS, 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

IEEE-NSS, 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. E. Etzion, Cluster Quality for tracking at ATLAS CSC In time + mask hit [x 25 ns]

IEEE-NSS, 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

IEEE-NSS, Dirty clusters rate About third of the muon clusters are “dirty” From test beam data (about 3KHz/cm2 radiation background) E. Etzion, Cluster Quality for tracking at ATLAS CSC

IEEE-NSS, Calculating the quality – The model Spatial signal Matheison shape noise Amplitude Hit position The Model: The ML: E. Etzion, Cluster Quality for tracking at ATLAS CSC

IEEE-NSS, Calculating the quality The solution: The quality: E. Etzion, Cluster Quality for tracking at ATLAS CSC

IEEE-NSS, Quality of clusters Possible threshold value E. Etzion, Cluster Quality for tracking at ATLAS CSC

IEEE-NSS, Different fitting methods 1.Least Squares (LS) – all points are used with equal weights in the track fitting process. 2.WLS – the “dirty” clusters gets reduced weight than the “clean” clusters, according to the optimal solution: 3.Robust fitting – iterative procedure which recalculate the weights according to the residual between the hits and the estimated track. 4.Iterative LS – omitting the point with the higher residual in each iteration. 5.Restricted LS – taking only the “clean” clusters. E. Etzion, Cluster Quality for tracking at ATLAS CSC

IEEE-NSS, Simulation results for different layer number Number of layers Residual between real and estimated track E. Etzion, Cluster Quality for tracking at ATLAS CSC Good probability Quality prob.

IEEE-NSS, Discussion- number of detection layers 1.The use of the hit quality improves the fitting results. 2.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. 3.The iterative and Robust fitting techniques improve the LS fitting results when the number of layers is greater than 5. 4.The ATLAS CSC has only 4 layers… E. Etzion, Cluster Quality for tracking at ATLAS CSC

IEEE-NSS, Simulation results for different contamination level (radiation background) Residual between real and estimated track Number of layers = 8 E. Etzion, Cluster Quality for tracking at ATLAS CSC

IEEE-NSS, Discussion- radiation background level 1.The use of the hit quality improves the fitting results. 2.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). 3.The performance of the algorithms that use the hit quality is similar. 4.The LS fitting technique gets the worst results. E. Etzion, Cluster Quality for tracking at ATLAS CSC

IEEE-NSS, 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

IEEE-NSS, Discussion- detection probability 1.The use of the hit quality improves the fitting results. 2.The probability of detection affect only the techniques that use the hit quality. 3.If the detection probability is lower then 0.8 the fitting performance is reduced significantly. E. Etzion, Cluster Quality for tracking at ATLAS CSC

IEEE-NSS, 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

IEEE-NSS, 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