Hadron production in C+C at 2 A GeV measured by the HADES spectrometer 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 Pavel Tlustý and Vladimír Pospíšil, NPI Řež

Experimental and analysis details November commissioning and physics runs seg. target= 2x 2.5% C+C 2AGeV  200*10 6 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 ) (1st level trigger events) UrQMD simulations - 115M events (gen2) used for parameter production 60 M events (gen3) used for analysis

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 Particle Identification Method

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) STEP I - p.d.f.‘s

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 STEP III - application of Bayes' theorem 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

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 -- ++ C+C, 2AGeV p*q [MeV/c] v/c d p e-e- e+e+ Tracking+TOF Hadron ID

spline tracks matched to META inner mdc segment  2 > -1, spline  2 > -1, SplineAccepted=1 tracks with TOFINO paddle multiplicity =1 Track selection

EXP gen3 SIM SEC 0 SEC 3 protons: momentum_track vs momentum_beta (p track - p  )  vs p  p  = M p *  *  should be filled in QA

Results of hadron ID

Efficiency and purity

Spectrometer acceptance acceptance calculated from SIM data as ratio N rectracks /N primary for p,  +,  - in theta vs momentum

Corrected particle yields

Corrected yields - sector No.0 Momentum distribution Theta distribution

Corrected yields - sector No.3 Momentum distribution Theta distribution

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

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

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

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

Pi distributions in theta_cm

Pi distributions in mom_cm - UrQMD

Pi distributions in mom_cm - EXP

no d in UrQMD0.23 ± 0.02d 1.08 ± 0.11 * ± 0.25p 0.96 ± ± 0.07 –– 0.95 ± ± 0,07 ++ ratio N exp / N sim simulationexperiment (± bias error) Particle yields per event (acceptance corrected)  p+d) exp /p sim

Particle yields per event (acceptance corrected) UrQMD yields to   event (1st level trigger) 0.82  event (no bias) N  = 0.83 ± 0.08   TAPS N  = 0.77 ± 0.07   KAOS

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

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

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

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

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

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

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

NOV02 gen4 protons: mom_track vs mom_beta (p track - p  )  vs p track  p  = M p *  *  SYS 1 SYS 0 KICK SPLINE RK

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  meson and baryon yields extracted to be done: further check of acceptance corrections comparison to kicktrack analysis nov02 gen4 high resolution (runge-kutta) analysis