1. ATLAS ATLAS muon CSC clustering David Adams Brookhaven National Laboratory June 15, 2006 Muon Software Updated 11:00 EDT June 15, 2006

2. ATLAS D. Adams CSC clustering Muon softwareJune 15, 20062 Procedure Create ROOT tree with simulation track positions Input is a HITS dataset (G4 output) Use only primary tracks (all or mostly muons) Coordinates where track crosses each CSC wire plane Create ROOT tree with reconstructed clusters Input is an RDO dataset –CSC clusters are not saved in the ESD (would be easier if they were) Run a clustering algorithm –Same as that used by Moore Create ROOT tree with reconstructed clusters matched to simulated positions Inputs are the above simpos and cluster trees For each simulated position, find the nearest clusters in the same event and chamber

3. ATLAS D. Adams CSC clustering Muon softwareJune 15, 20063 Available data So far, have generated trees for one dataset csc11.005115.JimmyZmumuM150.simul.HITS.v11004205.root High pT muons, data available at BNL Simpos files http://www.usatlas.bnl.gov/~dladams/muon/simpos Cluster files http://www.usatlas.bnl.gov/~dladams/muon/cluster Combined files http://www.usatlas.bnl.gov/~dladams/muon/cscperf Warning: code is still evolving and it is likely that some of tree structures will change Plan is to generate trees for selected single muon samples

4. ATLAS D. Adams CSC clustering Muon softwareJune 15, 20064 Software The code to produce the trees is in CVS: Simpos in user/dadams/CscHitValidation Cluster in user/dadams/CscClusterValidation Combined in user/dadams/CscClusterPerformance Build in the usual CMT manner Job options in share directory Scripts testX (X=1,2,…) in run directory to run test job May need to change input file in job options

5. ATLAS D. Adams CSC clustering Muon softwareJune 15, 20065 Preliminary results Sample has 103.2 k entries Following tables give the number of entries passing various cuts z is the coordinate along the r-direction y is the coordinate along the phi direction  z = z_cluster - z_simpos (residual)  is the error;  Z/  is the pull cut# entries  z < 10 cm 99.7 k96.5%  z < 1 cm 97.0 k94%  z < 1mm 87.4 k81%  z/  < 10 86.2 k83% cut# entries  y< 10 cm 102.9 k99%  y < 1 cm 100.4 k97%  y < 1mm 46.7 k45%  y/  < 10 57.3 k55%

6. ATLAS D. Adams CSC clustering Muon softwareJune 15, 20066 Residual for r coordinate Residual (mm)

7. ATLAS D. Adams CSC clustering Muon softwareJune 15, 20067 Pull for r-coordinate

8. ATLAS D. Adams CSC clustering Muon softwareJune 15, 20068 Residual for phi coordinate Residual (mm)

9. ATLAS D. Adams CSC clustering Muon softwareJune 15, 20069 Pull for phi coordinate

10. ATLAS D. Adams CSC clustering Muon softwareJune 15, 200610 Conclusions Clustering needs improvement Low efficiencies for loose cuts Distributions far from Gaussian –Central peak for which errors are overestimated –Long tails for which errors are understimated Highest priority is to find a way to tag spoiled clusters Recalculate coordinate and/or assign larger error Look at widths or double peaking –Latter, see Ben’s talk at last week’s Moor meeting Like to study any other existing clustering algorithms Can/does MuonBoy write clusters to storegate?

11. ATLAS D. Adams CSC clustering Muon softwareJune 15, 200611 Appendix: Add cut on z Many of the misreconstructed clusters are near the r-edges for the chambers. Adding the cut abs(z) < 500 mm, leaves 93.2 k entries Table below show the efficiencies with this cut applied Efficiencies for r-clusters are improved cut# entries  z < 10 cm 92.9 k99.7%  z < 1 cm 91.9 k98.6%  z < 1mm 84.5 k91%  z/  < 10 86.2 k89% cut# entries  y< 10 cm 92.8 k99.6%  y < 1 cm 90.4 k97.0%  y < 1mm 41.7 k45%  y/  < 10 51.5 k55%