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TPC parallel tracking and Particle Identification

Learn about the innovative tracking and particle identification methods used in the Alice Time Projection Chamber (TPC). Discover the chamber's design, simulations, tracking history, and cluster finding techniques.

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TPC parallel tracking and Particle Identification

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  1. TPC parallel tracking and Particle Identification Marian Ivanov

  2. Alice TPC • Time Projection chamber – main tracking device of the Alice central barrel • Main tasks • Track finding • Momentum measurements • Particle identification by dE/dx

  3. Alice TPC • Geometrical features: • Drift region: • Cylindrical vessel • Length = 250+250 cm • Rin/Rout ~87/252 cm • Readout chambers • 2×18 sectors (72 chambers) • Sector opening angle 20 degrees • Pad shapes 7.5x4, 10x6 and 15x6 mm ~ 0.5 million pads • Time sampling ~ 445 time bins per pad ~220 million samples per event

  4. TPC simulations • Physical processes: • Relevant GEANT processes • Diffusion • Gas gain fluctuation • ExB effect • Responses in time and pad direction (2D) • noise

  5. TPC tracking • First TPC tracking 1997 • Iouri Belikov, Boris Batyunya, Karel Safarik • Based on Kalman filtering approach • “offline” tracking • Parallel development • Bergen group • Hough transform – global approach • “online” tracking – only “almost” primary particles

  6. New tracking • Maximal Information principle • Use everything what you can ==> You get the best • Why? • You can't use more • Problem – too many degrees of freedom (~220 million 10 bits samples • Compromise – looking for orthogonal parameters • Parallel Kalman Filter tracking approach chosen • To allow to use optimal combination of local and global information about track's and clusters • Global tracking approach (Hough transform) considered only as seeding for track candidates

  7. New cluster finder • Cluster finder looks for local maxima in two dimensional time x pad-row plane • Neighbourhood - matrix 5x5 with maxima at central bin • 5x5 is bigger then typical size of cluster • Standard centre of gravity and RMS used to characterize cluster • Problem • Systematic error due to the threshold effect

  8. New cluster finder • Parameterization of the cluster shape • Depend on the track parameters • Z position – gives the diffusion component • Known during clustering • Θ angle – gives the z angular component • Known during clustering for primary particles • φ angle – depend on the pad row radius and particle momentum • Known only during tracking • Conservative approach – supposing 0 degree – good for high pt tracks

  9. New cluster finder • “RMS” fitting of the cluster • Virtual charge added • signal below threshold replaced by expected value according gauss interpolation • if bigger replaced with amplitude equal to threshold • Signal shape (RMS) used for later error estimation - and as a criteria for cluster unfolding • Gives comparable results with Gaussian fit of the cluster, but is much faster

  10. RMS versus fitting • left side: reconstructed RMS to fitted sigma ratio • right side: ratio as function of the expected cluster RMS

  11. Cluster unfolding • If one of the RMS's – in time or pad direction is bigger then critical RMS - unfolding • Fast spline method for unfolding • Charge conservation • Small systematic effect • Supposing the same signal shape – equivalent to the same track angles – if not fulfilled – tracks diverge very rapidly

  12. Spline unfolding • Amplitude in bin 4 corresponding to cluster on left side • Amplitude in 5 and derivation in five 0 • Amplitude in 2 and 3 taken • C1_4 calculated • Right side • symmetric C2_4 calculated • C1_4 = C1_4*C4/(C1_4+C2_4)

  13. Spline unfolding (standalone simulator) • Dependence of the reconstructed cluster position as function of the distance to the next cluster • RMS of clusters – 0.75

  14. Cluster characteristic • fY,fZ • centre of gravity • fSigmaY, fSigmaZ • shape of the cluster • in case of overlapped clusters – characterize cluster background • fMax, fQ • Signal at the maximum – respectively total charge in cluster • fCType • Cluster type - characterize overlap factor

  15. Cluster error estimation • Errors estimated only during tracking • Using • cluster shape information • cluster amplitude • type of the cluster – is gold-plated or overlapped • track angles and position • is shared info (not yet implemented) • Error parameterization • Different for different pad geometries

  16. Cluster error estimation • Previous parameterization used for “gold-plated” clusters • Overlapped clusters • Additional correction as function of the distortion from expected size • Edge clusters taken separately • Error parameterization principle • Make Gaussian pulls with unite sigma

  17. Seeding with vertex constrain • Seeding 2 times • 1 seeding - 90 % of tracks are found • 2 seeding - 6.7 % additional found • Problems • N2 problem (2 minutes of CPU) • Vertex constrain suppress secondaries • Solution ? • Seeding using polynomial fit without any assumption on vertex position

  18. Seeding without vertex constrain • Simple track follower • Algorithm • Seeding between pad-row i1 and i2 – start in the middle pad-row • Take cluster at middle pad-row • Find 2 nearest up and down – make linear fit • Find prolongation • Take next 2 nearest - update fit - prolongation .... • After 7 cluster - make polynomial fit ... • continue

  19. Tracking • 2 seedings with constrain + few seedings without at different radii (necessary for kinks) • Tracking - parallel • Find for each track the prolongation to the next pad-row • Estimate the errors • Update track according current cluster parameters • Track several track hypothesis in parallel • Allow cluster sharing between different tracks

  20. Removing track hypothesis • Remove-Overlap – called 3 times • After seeding (threshold =50 %) • After tracking outer sectors (threshold =50 %) • After tracking inner sector (threshold =50 %) • Effect (full event) • New tracker - 3 fakes • Old tracker - 7 fakes

  21. dEdx • Truncated mean – 60 % • Currently signals at cluster maximum • Shared clusters not used at all • Correction function for cluster shape • Function of ratio of measured cluster shape to expected cluster shape

  22. Comparison of new tracking and old tracking • Full event dN/dy compared – Hijing parameterization • efficiency comparison for primaries • dEdx comparison for primaries • efficiency comparison for primaries +secondary crossing full TPC • dEdx comparison for primaries +secondary crossing full TPC

  23. Particle identification using TPC dE/dx • dE/dx measurement in TPC (in combination with TRD and ITS) can be used for PID • Next slides • First systematic study of using new TPC tracking and dE/dx information for PID determination (low-momentum region) (Boris Batiounia) • dE/dx as function of momentum • dE/dx spectra for fixed momentum • PID efficiency and contamination (old and new tracking

  24. dEdx as function of the momenta

  25. TPC PID separation (primaries)

  26. Efficiency as function of pt

  27. Efficiency as function of pt

  28. Efficiency as function of pt

  29. Efficiency as function of pt

  30. Pt resolution as function of pt

  31. Pt resolution as function of pt

  32. Pt resolution as function of pt

  33. Pt resolution as function of pt

  34. Efficiency (for kinks) • Left – primaries decaying at radius r • Right - secondary created at radius r

  35. Kink and secondary vertex finder • Track candidates - seeded in several positions within chamber • 'easy' to implement using current new tracking • Algorithm • Combinatorial search – closest point between two tracks investigated • Cluster density criteria before and after kink respectively (V0 used to determine the criteria for hypothesis removal) • Status • First attempts – systematic study of efficiency and contamination still to be done

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