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Combined tracking based on MIP. Kink and V0 finder.

Develop a comprehensive tracking algorithm based on MIP, Kink, and V0 finder for high-flux particle environments. Incorporate iterative approaches for algorithm refinement and comparison while adhering to memory and CPU constraints.

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Combined tracking based on MIP. Kink and V0 finder.

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  1. Combined tracking based on MIP. Kink and V0 finder. Marian Ivanov

  2. Combined tracking based on MIP. Kink and V0 finder. Assumptions. • Current algorithms and supporting data structures for combined tracking don’t allow to apply MIP • Suitable only for primary tracks • Do we need algorithm based on MIP? • of course, we want the best • In the high flux environment we have to be – iteration number 0 is not sufficient • but, we are also realists • iterative approach preferable, framework has to work in each step of algorithm development • possibility to compare different algorithms • don’t bother other programmers • memory and CPU time restrictions • Keep reasonable number of iterations • and - very important • If algorithm based on MIP gives worse results, the problem is in the algorithm not in MIP.

  3. Outlook • Possible sources of information • Access • when and how is the information available? • Kink and V0 finder • what can be used - examples • general strategy -Algorithms • Reliability • Probabilistic interpretation • Usage - on which probability level to discard some information -hypothesis • Preliminary results - Kinks

  4. TOF TRD TPC ITS Kinks – schematic view

  5. TOF TRD TPC ITS V0s – schematic view

  6. Sources of the information • spatial characteristic of a track and sets of tracks • px,py,pz,y,z parameters and covariance • chi2 • number of points on the track • number of shared clusters on the track • overlaps between tracks • DCA for V0s, Kinks and Cascades • … • dEdx • mean, sigma, number of points, number of shared points… reliability • TOF of a track and sets of tracks • derived variables • Mass • Probability that particle “ really exists” in some space interval (used for causality cuts) • Based on clusters occurrence, and chi2 before – after vertex • Invariant mass • Pointing angle of neutral mother particle • …

  7. Access to the information • AliESD event keeps the information about tracks, kink and V0 candidates • Accessible to all tracker • Identified using UniqueID • ESDKink and ESDV0s • Spatial characteristic – AliExternalParameters • Quality information – TPC, ITS, TRD, TOF • Reference to the corresponding tracks • ESDTracks • Spatial characteristic – AliExternalParamters • Quality information – TPC, ITS, TRD, TOF • List of references of possible V0 and Kink candidates • Tracking – iterative process • Tracking inside, - back propagation – refit inward • Updates of the ESD information

  8. DCA and Helix approximation • V0, Kink and Cascade finder • DCA calculation – helix approximation used • Helix approximation not enough precise • multiple scattering • energy losses in the material • non homogenous magnetic field • presence of the fake clusters • high multiplicity events – non correlated tracks • Kink – track associated to daughter particle admixture of the clusters created by mother particle during forward propagation and vice versa for backward propagation • Solution • Iterative process • Find V0, Kink and Cascade vertex using Helix approximation • Refit tracks towards to the vertex • refine DCA

  9. Seeding • To maximize fiducial volume for Kinks, V0s and cascades – seeding (track fragments finding) algorithm has to be implemented in each barrel tracking detector • TPC - fast continuous seeding implemented • ITS - standalone seeding and tracking with vertex constrain implemented • necessary to speed it up • new fast seeding without vertex constrain is currently tested • TRD - extremely slow seeding with vertex constrain, not usable

  10. Combined tracking algorithm. (step 0) • TPC seeding • TPC tracking inward • Kink and V0 finding • DCA calculation • rough cuts • Done • Tracks and V0s arrays defined • tracks - with references to all possible V0 - Done • V0 - with references to the tracks – to be Done

  11. Combined tracking algorithm (step 1) • ITS tracking inward with vertex constrain - Done • ITS seeding with vertex constrain – to be done • ITS tracking without vertex constrain -Done • ITS seeding without vertex constrain - to be done • Parallel tracking • Kink and V0 finding part • refined cuts for V0 candidates found in the TPC applied – to be done • new DCA calculation in the <ITS, inner TPC> fiducial volume - DONE • rough cuts -Done • defined probability level for the “signal” • tracks refits toward to the vertex obtained in the first approximation –Done • refined cuts - DONE

  12. Combined tracking algorithm (step 2) • TPC tracking backward • ++ for mother particle of hypothetical kinks • track fit towards the vertex (ITS information already included) - Done • Refined cuts applied - Done

  13. Combined tracking algorithm Proposal (step 3) • TRD tracking backward - done • TRD seeding – to be done • Kink and V0 finding part • refined cuts for V0 candidates – to be done • new DCA calculation in the <outer TPC, TRD> fiducial volume – to be done • rough cuts – to be done

  14. Combined tracking algorithm Proposal (step 4) • TOF matching • building of the tree of hypothesis • “Parallel tracking” – as in the ITS • For secondary tracks (from kink and V0 decays) postpone the decision until V0 and Kink refit – the list of possible TOF clusters and spatial chi2 stored in the track • Kink and V0 finding part • refined cuts for V0 candidates

  15. Combined tracking algorithm Proposal (step 5) • TRD inward tracking • refined cuts for V0s – to be done • TPC inward tracking • Update of the Kink and V0 information - DONE • refined cuts for kink and V0s -DONE • ITS inward tracking • refined cuts for V0s – to be done • Apply previously stored TOF information for kink and V0 PID • Cleaning up ESD – final cuts

  16. Time schedule • TPC inward tracking – done • kink and V0 finder in TPC fiducial volume • Done • ITS tracking - Done • fast ITS seeding • implemented • to be tuned • kink and V0 finder in <ITS, TPC> fiducial volume • plan to finish it before September (CHEP conference 2004, next ALICE week) • Done

  17. Kink finder – results (0) • Algorithm described before • Current sensitive volume – only TPC • Kink finder efficiency • 60 % for central event (both pions and Kaons), 25 % combinatorial background admixture • ~85 % low multiplicity events • Time consumption ~ 10 s for central event

  18. Kink finder efficiency • Efficiency for Kaons as a function of decay radius • Left side – low multiplicity – 2000 Kaons • Right side – same events merged with central event

  19. Kink Qt resolution • Qt resolution for Kaon as a function of decay radius • Left side – low multiplicity – 2000 Kaons • Right side – same events merged with central event

  20. Kink position resolution • Kink position resolution as the function of angle • Left side – low multiplicity – 2000 Kaons • Right side – same events merged with central event

  21. Kink angular resolution • Kink angular resolution • Left side – low multiplicity – 2000 Kaons • Right side – same events merged with central event

  22. Conclusion • strategy for combined tracking based on MIP defined • Partially implemented • overall time scheduled • to be defined according experiences obtained from first stage

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