Hadron production in C+C at 2 A GeV measured by the HADES spectrometer

1. Hadron production in C+C at 2 A GeV measured by the HADES spectrometer Pavel Tlustý and Vladimír Pospíšil, NPI Řež • Nov02 gen3 analysis and results for spline tracks (shown in Dubna) • changes - removing bug in acceptances, theta_cm distributions • Nov02 gen3 and gen4 QA • outlook

2. Experimental and analysis details November 2002 - commissioning and physics runs seg. target= 2x 2.5% C+C 2AGeV200*106 events: 56% LVL1 trigger + 44% LVL2 trigger 4 outer MDCIII-IV only 2 sectors with 4 chambers + spline tracking used in the analysis 71M events (gen2) used for parameter production 33 M events (gen3) used for analysis (days 345-350) (1st level trigger events) UrQMD simulations - 115M events (gen2) used for parameter production 60 M events (gen3) used for analysis

3. Particle Identification Method Principle: for each track a probability that it is of a particle type h is calculated, for all possible particle types Bayes theorem implemented cut on the resulted probability set to decide on PID Input: for each track (track candidate) with a given momentum we have a set of independent measured variables in HADES: velocity, energy loss, RICH response, MDC hit, SHOWER response Output:- a probability, that a given track corresponds to the particle type h- efficiency and purity for a selected cut

4. STEP I - p.d.f.‘s • Normalized probability density distributions of each measured variable determined for each particle type • from exp data when possible (good separation of particles) • interpolation and extrapolation of „difficult“ regions“ with overlap from different particles • from simulations if necessary (e.g. RICH response)

5. STEP III - application of Bayes' theorem If probabilities of occurences of individual hypotheses == relative incident rates for each particle type in the PID case P(i), are known (or can be estimated from both experimental data and simulations), then where is probability that a track with measured is of a type h. There is clearly a need to take this into an account, as it changes the decision on the hypothesis test, compare Fig.1 with Fig.2

6. C+C, 2AGeV e- e+ v/c + - p d p*q [MeV/c] Tracking+TOF Hadron ID • Hadrons are identified using velocity and momentum measurements. • pdf‘s - distributions of velocity for given particle type (in given theta and momentum bin) for each sector separately • yields - number of tracks of a given particle type • p/ separation for p <1000 MeV/c

7. Track selection • spline tracks matched to META • inner mdc segment c2 > -1, spline c2 > -1, SplineAccepted=1 • tracks with TOFINO paddle multiplicity =1

8. protons: momentum_track vs momentum_beta EXP gen3 SIM SEC 0 (ptrack - pb )vs pb pb = Mp * b * g SEC 0 SEC 3 SEC 3 should be filled in QA

9. Results of hadron ID

10. Efficiency and purity

11. Spectrometer acceptance acceptance calculated from SIM data as ratio Nrectracks/Nprimary for p, p+,p- in theta vs momentum

12. Corrected particle yields

13. Corrected yields - sector No.0 Momentum distribution Theta distribution

14. Corrected yields - sector No.3 Momentum distribution Theta distribution

15. Yields ratios sec0/sec3 vs momentum exp/sim vs momentum

16. theta vs phi distribution of particles with mom>600 MeV/c exp sim exp/sim Sector No.3 p+ p - p

17. Corrected yields - sector No.3 - selected part Momentum distribution Theta distribution

18. Particle distributions in c.m. mom_cm > 200 MeV/c

19. Pi distributions in theta_cm

20. Pi distributions in mom_cm - UrQMD

21. Pi distributions in mom_cm - EXP

22. experiment (±bias error) simulation ratioNexp / Nsim p+ 0.70 ±0,07 0.74 0.95 ± 0.10 p– 0.72 ±0.07 0.75 0.96 ± 0.10 p 2.44 ± 0.25 2.47 1.08 ± 0.11 * d 0.23 ± 0.02 no d in UrQMD Particle yields per event (acceptance corrected) * (p+d)exp/psim

23. Particle yields per event (acceptance corrected) • UrQMD yields to 4p - 1.15 p / event (1st level trigger) • 0.82 p / event (no bias) • Np = 0.83 ± 0.08 p0 TAPS • Np = 0.77 ± 0.07 p+ KAOS

24. NOV02 gen3 and gen4 QA • tracks yields per sector, theta and phi distributions of negative tracks (test of PID) • momentum determination - protons, pi- ??

25. NOV02 gen3 - negative tracks vs phi large differences between sectors, for spline 15% difference between 2 sectors, kick even worse p yields copy distribution of negative tracks should be the same in electron distributions???? EXP SIM

26. NOV01 - negative tracks vs phi sec0 not used for analysis much better than Nov02 gen3 EXP SIM

27. NOV02 gen4 - negative tracks vs phi SYS 0 much better than Nov02 gen3 SYS 1

28. NOV02 gen3 - negative tracks vs theta Sec0 - Inefficiency in theta<30 and theta ~ 65 EXP SEC0 EXP SEC3 SIM

29. NOV01 - negative tracks vs theta much better than Nov02! EXP SIM

30. NOV02 gen4 - negative tracks vs theta differences between sectors SEC 0 SEC 3

31. NOV02 gen4 protons: mom_track vs mom_beta (ptrack - pb )vs ptrackpb = Mp * b * g SYS 1 SYS 0 SYS 1 SYS 0 KICK SPLINE RK

32. Summary and outlook • hadron PID analysis (beta vs momentum) performed using spline tracks for Nov02 experiment • problems with momentum determination and acceptance (track reconstruction efficiency for particles with low energy loss) observed • p meson and baryon yields extracted • to be done: • further check of acceptance corrections • comparison to kicktrack analysis • nov02 gen4 high resolution (runge-kutta) analysis