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Status of GlueX Particle Identification

Status of GlueX Particle Identification. Ryan Mitchell September 10, 2004. Outline. Overview of Particle ID Components. Physics Examples. Look at each subdetector: Central Drift Chamber (CDC) Barrel Calorimeter (BCAL) Cerenkov (CKOV) Forward Time of Flight (TOF)

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Status of GlueX Particle Identification

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  1. Status of GlueXParticle Identification Ryan Mitchell September 10, 2004

  2. Outline • Overview of Particle ID Components. • Physics Examples. • Look at each subdetector: • Central Drift Chamber (CDC) • Barrel Calorimeter (BCAL) • Cerenkov (CKOV) • Forward Time of Flight (TOF) • Likelihoods for Particle Hypotheses. • Angular Efficiencies.

  3. Particle ID with the • GlueX Detector: • dE/dx from the CDC • 2. Time from the BCAL • 3. Photo-electrons from • Cerenkov • 4. Time from the TOF

  4. Starting Momentum Spectra • Starting momenta • for four reactions: • p  +-p • 2. p  K+K-p • 3. p  K*K*p • K+-K-+p • p  K1K-p • K+K-p • K++-K-p Black = proton, Blue = Kaons, Red = Pions

  5. PID Cases (p  K*K*p) • 0. No PID Info • CDC • CDC, BCAL • CKOV • CDC, CKOV • CDC,BCAL,CKOV • CKOV,TOF • CDC,CKOV,TOF • CDC,BCAL,CKOV,TOF Central Forward Black = proton, Blue = Kaons, Red = Pions

  6. An Example From GEANT (pK*K*p)

  7. dE/dx from the CDC Calculated with Argon Gas Good K/p separation below ~2 GeV/c. Black = Proton Blue = Kaon Red = Pion Green = Electron

  8. Measured dE/dx in the CDC Measurements for pK*K*p. Calculating Likelihoods: Given a track with momentum p hitting the CDC, CDCLi = 1/i(2)1/2exp(-(x-xi)2/ 2i) i = particle hypothesis xi = predicted measurement i = predicted error (10%) x = actual measurement A dE/dx resolution of 10% is assumed. (Case 2 tracks) Black = proton, Blue = Kaons, Red = Pions

  9. Time of Flight from BCAL With 200 ps resolution, resolve: -- /K up to ~1 GeV/c -- K/p up to ~2 GeV/c Flight distance of 1.0 meters. Black = Proton Blue = Kaon Red = Pion Green = Electron

  10. Measured Time from BCAL Calculating Likelihoods: Given a track with momentum p and pathlength L hitting the BCAL, BCALLi = 1/i(2)1/2exp(-(t-ti)2/ 2i) i = particle hypothesis ti = predicted measurement i = predicted error (200ps time resolution, 1% momentum res., 1% length res.) t = actual measurement Measurements for pK*K*p. Time resolution of 200ps (Case 2 tracks) Black = proton, Blue = Kaons, Red = Pions

  11. CKOV Photo-Electrons The CDR design: Index of Refraction = 1.0015 Length = 1.0 meters Efficiency = 90 cm-1. Black = Proton Blue = Kaon Red = Pion Green = Electron

  12. Measured CKOV NPE Calculating Likelihoods: Measurements for pK*K*p. Given a particle of momentum p, calculate the expected number of photoelectrons under different particle hypotheses:  = N0sin2c. (Case 7 tracks) If cerenkov “fires”: CKOVLi = (1-e-) + a – a(1-e-) If cerenkov is quiet: CKOVLi = e-(1-a) N0 = cerenkov efficiency  = length of cerenkov  = cone of radiation angle a = accidental rate Black = proton, Blue = Kaons, Red = Pions

  13. Time from the TOF Wall With 100 ps resolution, resolve: -- /K up to ~2.5 GeV/c -- K/p up to ~4.0 GeV/c Black = Proton Blue = Kaon Red = Pion Green = Electron

  14. Measured TOF from the TOF Wall Calculating Likelihoods: Given a track with momentum p and pathlength L hitting the TOF, TOFLi = 1/i(2)1/2exp(-(t-ti)2/ 2i) i = particle hypothesis ti = predicted measurement i = predicted error (100ps time resolution, 1% momentum resolution, 1% length resolution) t = actual measurement Measurements for pK*K*p. Time resolution of 100ps (Case 7 tracks) Black = proton, Blue = Kaons, Red = Pions

  15. Likelihoods • Combine the likelihoods from all the detectors into a single likelihood, e.g., LK = LKCDCLKBCALLKCKOVLKTOF • Make decisions based on likelihood ratios. For now, use: /K separation = 2ln(L/LK) K/ separation = 2ln(LK/L) p/K separation = 2ln(Lp/LK)

  16. /K Separation /K separation = 2ln(L/LK) for pK*K*p Look at the ratio for each detector individually. (case 2) (case 2) (case 7) (case 7) Blue = Kaons, Red = Pions

  17. /K Separation Combine likelihoods into an overall likelihood. Now look at the /K separation. Blue = Kaons, Red = Pions

  18. K/p Separation K/p separation = 2ln(LK/Lp) for pK*K*p Look at the ratio for each detector individually. (case 2) (case 2) (case 7) (case 7) Black = proton, Blue = Kaons

  19. K/p Separation Combine likelihoods into an overall likelihood. Now look at the K/p separation. Black = proton, Blue = Kaons

  20. Particle ID Cuts • To get an idea of efficiencies, make the following simplifications… • A Pion is considered identified if: 2ln(L/LK) > 2.0 • A Kaon is considered identified if: 2ln(LK/L) > 2.0 • A Proton is considered identified if: 2ln(Lp/LK) > 2.0

  21. Momentum Efficiencies For pK*K*p, there is definite structure in momentum efficiencies… Inefficiencies due to… -- high momentum central tracks. -- forward tracks from 2 to 3 GeV/c Black = proton, Blue = Kaons, Red = Pions

  22. Angular Efficiencies for K*K* Starting After PID Efficiencies in Gottfried-Jackson angles. Preference for forward- backward decays.

  23. Angular Efficiencies for K+K-

  24. Angular Efficiencies for K1K

  25. Conclusions • There are no overwhelming problems in GlueX particle ID. • In the present setup, inefficiencies occur due to: • High momentum central tracks • Forward tracks between 2 and 3 GeV/c • These inefficiencies lead to sculpted momentum spectra, but only slight variations in angular efficiencies.

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