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Bayesian Approach for Particle Identification in Panda(Root)

This overview discusses the requirements, probability density functions, and Bayesian theorem for particle identification in Panda(Root), along with the current implementation and future improvements.

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Bayesian Approach for Particle Identification in Panda(Root)

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  1. Bayesian Approach for Particle Identification in Panda(Root) Stefano Spataro ISTITUTO NAZIONALE DI FISICA NUCLEARE Sezione di Torino

  2. Overview • Requirements for Particle Identification • Probability Density Functions • TheBayesian Theorem • Current Implementation in PandaRoot • Many things to do

  3. Requirements for Particle Identification different detectors for PID covering different momentum/angle ranges MVD, TPC/STT, Cherenkov, EMC, MDT… • handling of different PID signals (dE/dx, C, EMC shower, …) • combining several PID detectors to improve identification • if one detector does not contribute to PID, it should not decrease the identification perfomances

  4. Requirements for Particle Identification (II) • handling of different PID signals (dE/dx, C, EMC shower, …) • combining several PID detectors to improve identification • if one detector does not contribute to PID, it should not decrease the identification perfomances • PID procedure should be as much as possible automatic • PID depends also on analysis • Detector response (i.e. resolution) • Event/track selection (analysis) we need to separate

  5. Probability Density Function a function that describes the relative likelihood for a random variable to occur at a given point in the observation space (Wikipedia) probability to find a variable in a given range [a,b] i.e. Gaussian distribution Probability Density Function

  6. Probability Density Function - II HADES gaussian distributions • For each particle hypothesis • calculation of (normalized) pdf • from simulation • from experimental data x – signal (p, dE/dx, C…) h – particle hyp (e, , , K, p) depends on detector response

  7. The Bayes Theorem If many detectors/algorythms constributing to PID Global Likelihood k = MVD dE/dx, DRC C… Probability that a given track with given params x corresponds to particle type h

  8. The Bayes Theorem - II apriori probability to find the particle kind h in the detector pdf P(h) depends only on track/event selection counts

  9. Current Implementation in PandaRoot PndPidCandidate PidChargedCand PndPidProbability PidAlgoIdealCharged Ralf Kliemt • Momentum • Time-of-flight • EMC energy • EMC shower shape • Cherenkov angle • MVD dE/dx • # Muon Layers • … PndPidProbability PidAlgoDrc Stefano Spataro PndPidProbability PidAlgoMvd Laura Zotti TPidSelector Analysis The first two PID algorythms (apart from MC)

  10. DIRC: PndPidDrcAssociatorTask (myself) 2000 e, , , K, p p [0.2, 2.0] GeV/c  [20°, 120°]  [0°, 360°] p K   e pdf: Gaus center cos C = 1/n sigma parametrization (e) = () = () = 0.006 (K) = (p) = 0.005

  11. PidAlgoDrc GetElectronProb() GetMuonProb() GetPionProb() GetKaonProb() GetProtonProb()

  12. Particle Identification with DIRC PndPidProbability::GetXxxProb() PndPidProbability::GetXxxPdf() Xxx Electron, Muon, Pion, Kaon, Proton P(p)>0.2 P(K)>0.2 P()>0.2 P()>0.2 P(e)>0.2 p K   e

  13. MVD: PndPidMvdAssociatorTask (Laura Zotti) pdf: Landau p MPV(p) K e,, p = 1 GeV/c (p) MVD dE/dx Landau

  14. GetElectronProb() GetMuonProb() PidAlgoMvd GetPionProb() GetKaonProb() GetProtonProb()

  15. Particle Identification with MVD P(p)>0.2 P(K)>0.2 P()>0.2 P()>0.2 P(e)>0.2 p K   e furter details in MVD session, Laura Zotti talk

  16. Combined Particle Identification macro/pid/pid_check.C PndPidProbability *drc = (PndPidProbability *)drc_array->At(ii); PndPidProbability *mvd = (PndPidProbability *)mvd_array->At(ii); PndPidProbability *combo = (*drc) * (*mvd); combining different algorythms simple multiplication

  17. Particle Flux P(h) depends only on track/event selection PndPidProbability::GetXxxProb(PndPidProbability* flux) PndPidProbability::GetXxxProb()  default PndPidProbability* flux = new PndPidProbability(1,1,1,1,1))

  18. Particle Flux - DPM 2000 events @ 6 GeV/c no elastic particle yields primary + secondary PndPidProbability *flux = new PndPidProbability(239+237,114+101,2282+2375,35+42,517+1052)

  19. Things to do • More PID algorythms are needed (call for manpower) • TPC, STT, MDT, EMC, … • Automatic calculation of efficiency/purity (myself) • Charged and Neutral candidates should be unified • Smarter way to handle PidProbability (maps?) • Probability for positive and negative particles? • Flux (momentum/theta-wise?)

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