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Combined tracking based on MIP. Kink and V0 finder.

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

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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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