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D 0 reconstruction: 15 AGeV – 25 AGeV – 35 AGeV

D 0 reconstruction: 15 AGeV – 25 AGeV – 35 AGeV. M.Deveaux, C.Dritsa , F.Rami IPHC Strasbourg / GSI Darmstadt. Outline Motivation Simulation Tools Results for 25AGeV Results for 15AGeV Results for 35AGeV Intermediate Conclusions Proton-Proton collisions: first attempt

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D 0 reconstruction: 15 AGeV – 25 AGeV – 35 AGeV

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  1. D0reconstruction: 15 AGeV – 25 AGeV – 35 AGeV M.Deveaux, C.Dritsa, F.Rami IPHC Strasbourg / GSI Darmstadt • Outline • Motivation • Simulation Tools • Results for 25AGeV • Results for 15AGeV • Results for 35AGeV • Intermediate Conclusions • Proton-Proton collisions: first attempt • Summary and Conclusions

  2. Motivation • Feasibility study of D0 reconstruction for beam energy of 25AGeV is ongoing: • Simulations with relatively high statistics are needed to improve precision of results. • What are the S/B and the tagging efficiency results for this beam energy and for a specific geometry? • How are the above results affected for different beam energy (35AGeV, 15 AGeV) but same geometry? • Can we measure open charm at 15AGeV? Questions to address for studying D0 reconstruction at 15 and 35 AGeV: • How to generate D0 with correct parameters (γ, T and σY) • What is the signal acceptance for those energies? • How is the pt-Y distribution affected once cuts are applied?

  3. D0π+K- Tools of the simulation Apply“soft” pre-selection criteria Select Candidate Tracks Select Candidate Pairs Optimised for each geometry and energy using specific algorithm Apply Final Cuts Calculate S/B, signal efficiency…

  4. Calculation of S/B: how is it done? Generate signal and background* Apply the final cuts. Fit the background distribution with an exponential function. Fit the signal distribution with a Gaussian function. The background fit function is normalised with respect to detector’s lifetime (~1011 centr coll). The signal fit function is normalised with respect to detector’s lifetime (~1011 centr coll) taking into account the cross section. Integrate the functions in a region of 2σ around the mean value of signal. *Part of the background is generated with the Super Event method: Mixing all particles of all events together.

  5. Optimisation of selection criteria (cuts) The procedure The cut optimisation procedure is based on an iterative algorithm searching for a maximum on a multidimensional surface (developed by M.Deveaux). Advantages: • It takes into account correlations between different cuts. • It is fast (not more than few hours) Disadvantages: • May converge at local maxima. • Most cuts are implemented but not all yet. (Ex. impact parameter not yet implemented) The most important cuts • Rejection of particles intersecting the primary vertex (χ2 primary) • Reject vertices with low fit quality (χ2 secondary) • Select vertices within a distance from the initial collision point

  6. Au-Au @ 25 AGeV ZMC-ZRECO (cm) Geometry used: 3 MAPS 200 μm, 5μm spatial resolution ( 10-15-20cm) 1 HYBRID 750 μm, 50μm pixel size ( 30cm) 5 STRIPS 400 μm ( 50, 62.5, 75, 87.5, 100cm) Total thickness : 3.35mm σ = 84.0 ± 2.8 (μm) σ = 15.4 ± 0.4 MeV/c2 mπK (MeV/c2) The two last stations were included in the hits but not in the tracks Statistics generated: 225 Millions equivalent central events using Super Event method

  7. Entries / 100 MeV mπK (GeV/c2) S/B Eff D0 multiplicity 0.9 2.6% 1.2*10 -4 Au-Au @ 25 AGeV; Input: Bg=225 Millions, Signal=9000 D0 Entries 5 MeV mπK (GeV/c2) Number of D0 expected after one run (1,2*1011centr coll) within the inv. mass range of mean +/- 2σ: 13000 mπK (GeV/c2)

  8. Geometrical Acceptance for 25AGeV, 9000 D0 Geometrical Acceptance in the full rapidity range: 34% 4π Pt (GeV/c) Pt (GeV/c) Y Y Geometrical Acceptance + Cuts In the 2<Y<3 rapidity range: Reconstruction Efficiency: ~ 5% >> The rapidity region of interest is populated after applying final cuts Pt (GeV/c) Y

  9. Au-Au @ 15 AGeV • Same Geometry Statistics generated: 249 Millions equivalent central events using Event Mixing method σ = 15.8 ± 0.5 MeV σ = 89.8 ± 3.3 μm mπK (MeV/c2) ZMC-ZRECO (cm)

  10. 15 AGeV 25AGeV pBeam = 15 AGeV pBeam = 25 AGeV T = 300MeV (Inverse Slope Parameter) Gaussian rapidity width = 1 Au-Au @ 15 AGeV: Signal Generation-Multiplicity-Normalisation Generate Signal Pairs : The choice for the parametres follows the choice of parametres for generation of D0 @ 25AGeV: Because of lack of information for determining a Temperature the value of T is not changed. Finally, the normalisation is done with respect to the detector’s lifetime which was estimated to be 1.4·1011 centr colisions (For 25AGeV the lifetime is 1.2∙1011) The multiplicity was assumed to be 10-5

  11. S/B Eff % Numb D0 exp D0 multiplicity 0.2 2.4 1000 10-5 15 AGeV, Input: Bg=249 Millions, Signal=8000 Background and signal distributions after cuts – before normalisation. The fits are shown. Entries / 5 MeV Entries / 50 MeV mπK (GeV/c2) mπK (GeV/c2) mπK (GeV/c2)

  12. Efficiency: Geometrical acceptance for 15AGeV, 8000 D0 Geometrical Acceptance: 27% 4π Pt (GeV/c) Pt (GeV/c) Y Y In the 2<Y<3 rapidity range: Reconstructed/Generated : ~ 5.6% >> The rapidity region of interest is populated after applying final cuts Pt (GeV/c) Y

  13. mπK (MeV/c2) ZMC-ZRECO (cm) Au-Au @ 35 AGeV • Same Geometry Statistics generated: 121 Millions equivalent central events using Event Mixing method σ = 86.2 ± 3.3 μm σInvMass = 14.3 ± 0.4 MeV

  14. 35 AGeV 25AGeV pBeam = 35 AGeV pBeam = 25 AGeV T = 300MeV (Inverse Slope Parameter) Gaussian rapidity width = 1 Au-Au @ 35 AGeV: Signal Generation-Multiplicity-Normalisation Generate Signal Pairs : The choice for the parametres follows the choice of parametres for generation of D0 @ 25AGeV: Because of lack of information for determining a Temperature the value of T is not changed. Finally, the normalisation is done with respect to the detector’s lifetime which was estimated to be 1011 centr colisions (For 25AGeV the lifetime is 1.2∙1011) The multiplicity was assumed to be 10-3

  15. S/B Efficiency % Numb D0 exp D0 multiplicity 8 2.1 77000 10 -3 2 different selection criteria 2.0 3.0 113000 10 -3 35 AGeV, Input: Bg=121 Millions, Signal=7000 Entries / 50 MeV Entries / 5 MeV mπK (GeV/c2) mπK (GeV/c2) S/B=8 Det. Eff = 2.1% mπK (GeV/c2)

  16. Efficiency: Geometrical acceptance for 35AGeV, 7000 D0 Geometrical Acceptance: 37% 4π Pt (GeV/c) Pt (GeV/c) Y Y In the 2<Y<3 rapidity range: Reconstruction Efficiency: 4.5% >> The rapidity region of interest is populated after applying final cuts Pt (GeV/c) Y

  17. Intermediate Summary & Conclusion A comparison study between 25 , 15 and 35 AGeV was done: • The IM resolution and secondary vertex resolution remain almost unchanged. • The over-all reconstruction efficiency was not significantly different: 2% • The S/B as much as the number of reconstructed D0 scale (roughly) with the multiplicity. • S/B15 = 0.2 ; ~ 1000 D0 • S/B25 = 0.9 ; ~ 13.000 D0 • S/B35 = 8; ~ 77.000 D0 Next steps and open questions: - Explore other setups that allow D0 measurements with better results. - What is the physics we can do with the above results? - Make an error estimation on S/B - Update cut finding procedure (expect improved results) - How to produce signal pairs with morerealistic parameters?

  18. Preliminary results of proton-proton collisions Outline: • Motivation • Event generation • Input of the simulation • First preliminary results Motivation: >Nucleon-nucleon reaction data provide a reference for the interpretation of nucleus-nucleus collisions. >The measurement of open charm in proton-proton collisions is itself interesting as there are no data available at threshold energies.

  19. Preliminary results of proton-proton collisions: PYTHIA vs UrQMD @ 25AGeV Models already tried for event generation: >PYTHIA >UrQMD PYTHIA is not adapted for such low energies; Both models were checked in terms of charged particle multiplicity and only UrQMD in terms of average transverse momentum for charged particles.

  20. Particle <multiplicity>/event PYTHIA <multiplicity>/event Experimental data* Pi+ and Pi- 4 3 protons 3.2 1.5 K+ and K- 2 0.1 Preliminary results of proton-proton collisions: PYTHIA @ 25AGeV Models for event generation: >PYTHIA @ 25AGeV PYTHIA gives a factor of 2 more protons and a factor of 20 more kaons But UrQMD gives rather satisfactory results as they arecloser to experimental data... *Rossi et al. , 1975, Nucl Physics B, page:267

  21. Particle UrQMD Model: <multipl>/evt Experimental data* <multipl>/evt UrQMD: <pt> (MeV/c) Experimental data* <pt> (MeV/c) Pi+ 1.2 1.7 350 322 Pi- 0.7 1.1 317 310 K+ 0.06 0.09 409 424 K- 0.02 0.04 480 401 protons 1.5 1.5 - - Preliminary results of proton-proton collisions: UrQMD @ 25 AGeV For 100.000 evts: It seems that UrQMD reproduces better than PYTHIA the experimental data. * Reference: Rossi et al. , 1975, Nucl Physics B, page:267

  22. Preliminary results of proton-proton collisions: Input of the simulation • CBMROOT FEB07 • STS geometry • 2 MAPS ( 150 μm; 10,20cm) • 6 STRIPS ( 400 μm; 30, 40, 50, 60, 75, 80, 100cm) • NO signal • NO TARGET material used for a first approach • 100.000 collisions

  23. Number of tracks in acceptance %of evts with N tracks in acceptance Num of evts with N tracks in acceptance Nb of tracks in acceptance % of evts with N tracks in acceptance Num of evts with N tracks in acceptance 0 37 37447 6 2 2314 1 22 22495 7 1 1047 2 15 15165 8 0.4 419 3 10 9934 9 0.1 104 4 7 6729 10 0.03 33 11 0.005 5 5 4 4308 Preliminary results of proton-proton collisions: What is the acceptance? Summarizing: • 75% of events have from 0 to 2 tracks in acceptance  Primary vertex reconstruction either impossible or very difficult! • 20% of events have from 3 to 5 tracks in acceptance • The rest 5% have more than 6 tracks inside acceptance

  24. ZRECO -ZMC Preliminary results of proton-proton collisions: What is the primary vertex residual? For Primary Vertex Only 4 or 5 tracks in acceptance; (10% events) Width of the distribution of the order of 80 um

  25. Preliminary results of proton-proton collisions: Summary - Open questions • The particle multiplicity for proton-proton is very low; for 75% of the events it is almost impossible to reconstruct the collision point. • For 10% of the events (4-5 tracks in acceptance) the width of the distribution primary vertex residual is of the order of 80um • Study other models for event generation (DPMJET, others?) • More realistic simulation: Implement a target material • The target geometry from HADES is “waiting” to be implemented. • Is there a better candidate? • Is there a modification in the tracking algorithm for primary vertex finding needed? • Explore other setups? • Study other systems: ex: p+C

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