1. ITS “parallel” tracking
Marian Ivanov

2. ITS tracking Parallel TPC tracking included to the ESD schema
ITS - Momentum resolution improvement
Full Hijing
dPt/pt ~ 0.8 % old( non parallel) TPC tracking
dPt/pt ~ 0.75 % new (parallel) TPC tracking
ITS tracking efficiency improvement
But also increase of the fake tracks amount

3. ITS clusterer Why so many fakes ?  Change in the AliITSclustererV2
Many of them not real fakes – only cluster labels were not properly assigned
 Change in the AliITSclustererV2
Track labels assigned according digits label in full cluster
Before only at the cluster center
Decrease of fake ratio - mainly for high pt tracks
Clustering algorithm not modified
Plan to implement:
new unfolding
Write cluster shape information – as a analog to the TPC cluster

4. Old ITS tracker Required all 6 clusters on the track
But cluster funding efficiency is not 100 % (~98.5% for full event)
Forbids cluster sharing between the track
But clusters are (with some probability) shared
Asymmetric algorithm for cluster removal
First track take all the clusters
If is fake, this algorithm by default produce another fake track
Big plus
ITS tracker – recursive tracking schema implemented
Tree of the track hypothesis checked
Decision which track hypothesis to accept – made according position chi2

5. New ITS tracking (1) Based on old ITS tracking Differences:
Cluster sharing allowed (fNShared data member in ITStrack)
Cluster skipping allowed (fNSkipped data member)
Cuts – fNShared (currently<2), fNSkipped (currently<2), fNShared+fNSipped (currently<2)
dEdx mismatch taken to the account
Cluster signed as used only if not another almost equally probable hypothesis exists
Probability of i_th hypothese being correct is defined as follows:

6. New ITS tracking (2) Differences:
Decision which track from the tree of the hypothesis to accept postponed
Additional information used
dEdx matching coefficient
Usage of the cluster
“Parallel tracking”
weight for each cluster in the track is calculated
where P(i) is the probability that cluster belong to track I
weighted chi2 used

7. Performance - Hijing peripheral 2 eventPt
dPt/Pt[%]
Efficiency [%] no fake cluster
Efficiency[%] one fake cluster accepted
Efficiency[%] two fake clusters accepted
Pt>0.2
0.70
93.7
94.7
95.5
Pt>0.4
0.65
95.2
95.9
96.5
Pt>0.6
0.62
95.1
96.4
96.6

8. Performance – Full HijingPt
dPt/Pt[%]
Efficiency [%] no fake cluster
Efficiency[%] one fake cluster accepted
Efficiency[%] two fake clusters accepted
Pt>0.2
0.755
84.
89.
91.8
Pt>0.4
0.72 88
91.5
93.2
Pt>0.6
0.71
90.5
92.7
93.6
V0 tracking efficiency
Based on one event
K0S – 62% (5/8) -1 Fake
Planned to use production to improve statistic and to tune its tracking

9. Full Hijing event

10. Performance – Full Hijing
Left side – ITS dEdx as function of the momenta
Right side – relative Pt resolution as a function of pt

11. V0 Performance – K0S
K0s event produced to estimate V0 finding efficiency
2000 primary K0s
TPC occupancy – corresponds to hijing peripherial2
Efficiency ~80 %
K0s peak centered at GeV (K0S mass = GeV)

12. Conclusion First version of ITS parallel tracking implemented
Efficiency and pt resolution improved
V0 efficiency
Need to apply algorithm using bigger statistic