Study of LEPSana Tracking

1. Study of LEPSana Tracking • Using real data: merging two (or more) events into one to check the successful reconstruction of originally found tracks in the individual events. Effect of noise hits and intrinsic tracking chamber inefficiency are included. • Using MC: simulated  events and feed the hit information into LEPSana to check whether well-accepted tracks are reconstructed. Test tracking ability in an ideal case to find out intrinsic problems if there is any.

2. Selection of One-Track Events for Merging • cut 1 ntrk=1 • cut 2 ntof=1 • cut 3 nhit_ssdraw<3 • cut 4 nhit_dcraw<25 • cut 5 0.01<pm(1)<1.1 • cut 6 abs(py(1)/pz(1))>0.01 • cut 7 -1000<vtz(1)<-840 • cut 8 adc_sqr(1)>0.08 • cut 9 tof(1)>13.

3. Causes of Inefficiency of Reconstruction after loss • Closeness of SSD hits. Two SSD hits are grouped into one cluster. • max_plan_hit. No allowance of inefficiency of one drift plane if more than two planes have the same maximum number of hits. • Exclusion of small clusters belonging to another big cluster. Tracking fails if accidental noise is presented on the plane where the hit by the true track is missing. • More….

4. Merging events from r21579 and r21580 together • Total: 4463 events. • Correcting problem (2): 22 events fails. 22/4463=0.5% . CPU time does not increase. • Correcting problem (2)+(3): 8 events fails. 8/4463=0.18% . CPU time increases about a factor of 2.

5. Testing by MC

6. Problems found by MC Test • Non-converging chi2 in RKTracker.F reaching maximum number of iteration. • Large angle track fails the cuts chan(DC*x) - chan(DC*xp)<=1 in DriftCluster.F. • The eeblocker geometry glitch. • More…