1. 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 Summary and Conclusions

2. Motivation  Feasibility study of D 0 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 D 0 reconstruction at 15 and 35 AGeV:  How to generate D 0 with correct parameters (γ, T and σ Y )  What is the signal acceptance for those energies?  How is the pt-Y distribution affected once cuts are applied?

3. Select Candidate Tracks Select Candidate Pairs Apply Final Cuts Calculate S/B, signal efficiency… Apply “soft” pre-selection criteria D 0 π + K - Tools of the simulation Optimised for each geometry and energy using specific algorithm

4. Calculation of S/B: how is it done? 1.Generate signal and background* 2.Apply the final cuts. 3.Fit the background distribution with an exponential function. 4.Fit the signal distribution with a Gaussian function. 5.The background fit function is normalised with respect to detector’s lifetime (~10 11 centr coll). 6.The signal fit function is normalised with respect to detector’s lifetime (~10 11 centr coll) taking into account the cross section. 7.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 (χ 2 primary) Reject vertices with low fit quality (χ 2 secondary) Select vertices within a distance from the initial collision point

6. Au-Au @ 25 AGeV 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) Statistics generated: 225 Millions equivalent central events using Super Event method The two last stations were included in the hits but not in the tracks m πK (MeV/c 2 ) Total thickness : 3.35mm σ = 15.4 ± 0.4 MeV/c 2 Z MC -Z RECO (cm) σ = 84.0 ± 2.8 (μm)

7. Au-Au @ 25 AGeV; Input: Bg=225 Millions, Signal=9000 D0 0.9 S/B 1.2 * 10 -4 2.6% D 0 multiplicityEff Number of D 0 expected after one run (1,2*10 11 centr coll) within the inv. mass range of mean +/- 2σ: 13000 Entries 5 MeV m πK (GeV/c 2 ) Entries / 100 MeV m πK (GeV/c 2 )

8. Geometrical Acceptance for 25AGeV, 9000 D0 4π4π Geometrical Acceptance in the full rapidity range: 34% Pt (GeV/c) Geometrical Acceptance + Cuts Pt (GeV/c) Y Y Y In the 2<Y<3 rapidity range: Reconstruction Efficiency: ~ 5% >> The rapidity region of interest is populated after applying final cuts

9. Au-Au @ 15 AGeV Statistics generated: 249 Millions equivalent central events using Event Mixing method Same Geometry σ = 89.8 ± 3.3 μm σ = 15.8 ± 0.5 MeV m πK (MeV/c 2 ) Z MC -Z RECO (cm)

10. Au-Au @ 15 AGeV: Signal Generation-Multiplicity-Normalisation Generate Signal Pairs : The choice for the parametres follows the choice of parametres for generation of D 0 @ 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 1.4·10 11 centr colisions (For 25AGeV the lifetime is 1.2∙10 11) pBeam = 25 AGeVpBeam = 15 AGeV Gaussian rapidity width = 1 T = 300MeV (Inverse Slope Parameter) 25AGeV15 AGeV The multiplicity was assumed to be 10 -5

11. 1000 Numb D 0 exp 2.4 Eff % 10 -5 0.2 D 0 multiplicity S/B 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 m πK (GeV/c 2 )

12. 4π4π Geometrical Acceptance: 27% Pt (GeV/c) Y Efficiency: Geometrical acceptance for 15AGeV, 8000 D0 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

13. Au-Au @ 35 AGeV Statistics generated: 121 Millions equivalent central events using Event Mixing method Same Geometry Z MC -Z RECO (cm) m πK (MeV/c 2 ) σ InvMass = 14.3 ± 0.4 MeV σ = 86.2 ± 3.3 μm

14. Au-Au @ 35 AGeV: Signal Generation-Multiplicity-Normalisation Generate Signal Pairs : The choice for the parametres follows the choice of parametres for generation of D 0 @ 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 10 11 centr colisions (For 25AGeV the lifetime is 1.2∙10 11) pBeam = 25 AGeVpBeam = 35 AGeV Gaussian rapidity width = 1 T = 300MeV (Inverse Slope Parameter) 25AGeV35 AGeV The multiplicity was assumed to be 10 -3

15. Entries / 5 MeV 2 different selection criteria 113000 77000 Numb D0 exp 3.0 2.1 Efficiency % 10 -3 2.0 10 -3 8 D 0 multiplicity S/B 35 AGeV, Input: Bg=121 Millions, Signal=7000 Entries / 50 MeV S/B=8 Det. Eff = 2.1% m πK (GeV/c 2 )

16. 4π4π Geometrical Acceptance: 37% Pt (GeV/c) Y Efficiency: Geometrical acceptance for 35AGeV, 7000 D0 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

17. Intermediate Summary & Conclusion Next steps and open questions: - Explore other setups that allow D 0 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 more realistic parameters? 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/B 15 = 0.2 ; ~ 1000 D 0 S/B 25 = 0.9 ; ~ 13.000 D 0 S/B 35 = 8; ~ 77.000 D 0

18. Preliminary results of proton-proton collisions 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. Outline: Motivation Event generation Input of the simulation First preliminary results

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

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

21. Preliminary results of proton-proton collisions: UrQMD @ 25 AGeV - 480 409 317 350 UrQMD: (MeV/c) - 401 424 310 322 Experimental data* (MeV/c) 0.040.02K- 1.10.7Pi- 1.71.2Pi+ 1.5 protons 0.090.06K+ Experimental data* /evt UrQMD Model: /evt Particle * Reference: Rossi et al., 1975, Nucl Physics B, page:267 For 100.000 evts: It seems that UrQMD reproduces better than PYTHIA the experimental data.

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. Preliminary results of proton-proton collisions: What is the acceptance? 4 7 10 15 22 37 %of evts with N tracks in acceptance 4308 6729 9934 15165 22495 37447 Num of evts with N tracks in acceptance 2 1 0 5 4 3 Number of tracks in acceptance 0.005 0.03 0.1 0.4 1 2 % of evts with N tracks in acceptance 5 33 104 419 1047 2314 Num of evts with N tracks in acceptance 11 10 9 8 7 6 Nb of tracks in 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. Preliminary results of proton-proton collisions: What is the primary vertex residual? Only 4 or 5 tracks in acceptance; (10% events) Width of the distribution of the order of 80 um Z RECO -Z MC For Primary Vertex

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