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TPC V0 finder

TPC V0 finder. Kink, V0 and double found. old tracking artificial criteria – accept track if more then 0.4 pad-rows and 0.5 findable maybe good for primaries no chance for kinks and secondaries new-new tracking defined region where cluster track density bigger then a threshold

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TPC V0 finder

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  1. TPC V0 finder

  2. Kink, V0 and double found • old tracking • artificial criteria – accept track if more then 0.4 pad-rows and 0.5 findable • maybe good for primaries • no chance for kinks and secondaries • new-new tracking • defined region where cluster track density bigger then a threshold • find Kinks, V0 and double find candidates – estimate range • redo fit for short tracks – with already known range

  3. Efficiency vs. vertex position • Left – primaries decaying at radius r • Right - secondary created at radius r

  4. First iteration • combinatorial search • minimal DCA – cut 6 sigma • fiducial volume – given by efficiency • kink 150-220 cm • V0 -100 – 250 cm • double ? • first iteration cut based only on density before and after kink respectively V0

  5. Special event • event with 1000 K • forced to decay between 120 – 220 cm • part of them before decay region • in fiducial volume 150-220 ~ 646 Kinks and V0

  6. Density definition • ratio - number of fond clusters to findable clusters on given region • for kinks taken safety space before and after kinks • Current cuts for dN/dy =1000 (blindly thinking about 8000) • kinks • min before <0.5 • max before >0.3 • min after <0.5 • max after >0.5 • effect 229 good kinks – 12 fake kinks

  7. Density (for Kinks)

  8. Kink fiducial volume • volume given by seed and tracking efficiency for “short” track

  9. Kink vertex resolution • systematic shift towards smaller R • at the beginning secondary take also clusters from primary

  10. Reconstructed px in vertex +pt resolution – in %

  11. perpendicular momentum of daughter particle • left – reconstructed P used • right – P taken from simulation

  12. Conclusion • kink candidates for next refitting found • need to speedup combinatorial search • time for 1000 primaries ~ 5 sec • plan • put it to the tracking code • do refitting of track • during back propagation save state vector in each vertex candidate • choose the best one if fulfill a cut criteria • perspective –use TRD for improving of kink pt resolution

  13. Appendix • in order to do study - new data members and function added to the AliTPCtrack • fClusterIndex – replacement of old fClusters • GetD() – distance to the vertex • fFirstPoint, fLastPoint • Density(row0, row1) – cluster density between row0 and row1 • … and • fSdEdx

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