1. 20/12/2011Christina Anna Dritsa1 The model: Input Charge generation The charge of the cluster is taken by random sampling of the experimental distribution for 25 pixels 0 degrees incident angle Charge sharing The charge distribution among the pixels in the cluster is based on a 2D Lorentz distribution (derived from the 1D) 0 degrees incident angle Landau: Accumulated charge on 25 pixels The simulation of inclined particles is derived by this initial parameterization. 1: Charge generation 2: Charge sharing among pixels

2. 20/12/2011Christina Anna Dritsa2 The model: charge distribution Charge provided by Landau (25 pixels) Charge provided by Landau (25 pixels) –if needed: scale to the trajectory length The trajectory is divided in segments The trajectory is divided in segments A Lorentz function corresponds to each segment A Lorentz function corresponds to each segment Illustration for inclined track

3. 20/12/2011Christina Anna Dritsa3 The model L ( x k,i,y k,i ) Charge on pixel i Sum over segments (k) x,y-coordinates of pixel i Pixel pitch Lorentz Amplitude Lorentz width x,y-coordinates of segment k

4. 20/12/2011Christina Anna Dritsa4 Evaluation Accumulated charge plot

5. 20/12/2011Christina Anna Dritsa5 Evaluation Average pixel multiplicity Good agreement between simulation and experimental data

6. 20/12/2011Christina Anna Dritsa6 Evaluation (qualitative): shape Simulation Experimental data Asymmetry is not sensor feature. Reflections in readout cable => Not simulated.

7. Intermediate summary CBM aims to explore the QCD phase diagram with rare probes Requirement for high intensities and performing vertex detector (MAPS sensors) Collision pile up and delta electrons increase substantially the hit density Precise simulation of detector response is crucial Detector response model developed and successfully tested with experimental data   Perform D 0 → K - π + measurement feasibility study

8. Open questions Evaluate open charm performance accounting for realistic sensor response and delta electrons. Is pile up tolerable? What is the impact of particle identification in open charm performance? Approach: simulation – –test different assumptions on pile up – –test different assumptions on particle identification

9. Monte Carlo Transport Code (Geant3/Geant4) Detector response models Event Generation Thermal model UrQMD beam particles Simulation chain Generate D 0, T=300 MeV, σ Y =1 nuclear coll. Au+Au, 25 AGeV Delta electron generation due to passage through target Simulate interaction of particles through matter, MF etc Simulate realistic detector response

10. Simulation chain (reconstruction level) Hit Finding Track Finding (Cellular Automaton) Track fitting (Kalman filter) Primary and secondary vertex fitting Analysis code Reconstruction of particle impact point Association of hits belonging to the same particle trajectory Fit the particle track and provide charge sign and momentum Provides coordinates and uncertainties of vertex position Reject the maximum of background while keeping the maximum of signal

11. Definition of pile up What is a pile up of N collisions? – –Several collisions occur within a readout cycle of the detector In simulation: Accumulate hits from N collisions – –1 central Au+Au at 25 AGeV – –N-1 minimum bias – –100xN beam ions (delta electrons) Pile up occurs only in the MVD layers

12. Generation of high statistics

13. 1 D 0 →π + K - per 10 6 collisions: requires highly efficient background rejection (better than 10 -9 ) Need high statistics to test background rejection Use event-mixing like technique: – –During the last step (analysis): – –Combine opposite charge particles originating from different nuclear collisions. – –Gain CPU time ( no need for GEANT simulation, track reconstruction) Increase statistics by factor N (= number of collisions simulated) Statistics reached: ~10 8 collisions (from ~10 4 ) time consuming

14. Background rejection High statistics background is generated. How to efficiently eliminate it?

15. 20/12/2011Christina Anna Dritsa15 Cut optimisation Criterion to define optimum cut value: – –Maximise significance S/sqrt(S+B) Method: Use multidimensional analysis in which the significance is maximised using simultaneously all cuts + Fast and user friendly - may converge to local maxima => careful usage

16. 20/12/2011Christina Anna Dritsa16 The CBM – MVD MVD STS RICH TRD TOF ECAL PSD Detector integration: IKF, Frankfurt StationZ (cm)R inner [mm]R outer [mm] Mat. Budget 155.5250.3% X 0 2105.5500.5% X 0

17. Results: cluster merging Merged cluster (M C ) MVD station Unambiguous clusters (U C ) What is the fraction of unambiguous clusters as a function of the hit density (collision pile up) ? f=U C /all

18. 20/12/2011Christina Anna Dritsa18 Invariant mass distributions open cuts Background Signal final cuts

19. 20/12/2011Christina Anna Dritsa19 S and B calculation

20. 20/12/2011Christina Anna Dritsa20 Results S/B efficiency

21. 20/12/2011Christina Anna Dritsa21 Summary Feasibility of open charm measurements was investigated based on newly developed detector response model for MAPS Feasibility of open charm measurements was investigated based on newly developed detector response model for MAPS Delta electrons were accounted for Delta electrons were accounted for Different assumptions on event pile up and PID capabilities were made Different assumptions on event pile up and PID capabilities were made

22. Conclusion The digitiser reproduces the response of MAPS sensors within 10% in terms of… For an event pile up above 5 substantial cluster merging is observed Additional counting statistics for a moderate pile up is cancelled out by reduced sensitivity. CBM remains sensitive to open charm with S/B between on ~0.1 and ~3 depending on pile up and particle identification assumptions. Expect better results for collisions at 35 AGeV

23. Outlook Improve cluster finding algorithms Adding a 3 rd MVD station might improve sensitivity of CBM (master thesis C.Trageser, IKF) Digitiser describes also partially depleted and irradiated MAPS (master thesis M.Domachowski, IKF) Expect improvement on sensor development: new CMOS processes allow approaching ~µm

24. 20/12/2011Christina Anna Dritsa24 Additional slides

25. Simulation setup CBMROOT simulation framework – –root based framework Detectors: – –MVD for vertex reconstruction – –STS for track and momentum reconstruction – –Thickness of silicon detectors : 300 µm – –TOF is modelled with ideal proton identification. Tracking-vertexing – –Cellular automaton and Kalman filter Realistic detector response (MVD, STS)

26. 20/12/2011Christina Anna Dritsa26 Strategy of D0 reconstruction Reconstruct the invariant mass by combination of all opposite charge pairs Apply selection cuts – –Define cuts – –Optimise Evaluate performance (S/B, significance…) – –Estimate Signal, Background – –Use scaling factors where needed

27. 20/12/2011Christina Anna Dritsa27 MVD detector geometry Thickness of sensors – Geometry Thickness of sensors – Geometry –1 st MAPS at 5 cm is 300 µ m thick –2 nd MAPS at 10 cm is 500 µ m thick StationZ (cm)R inner [mm] R outer [mm] Mat. Budget 155.5250.3% X 0 2105.5500.5% X 0

28. 20/12/2011Christina Anna Dritsa28 Assumptions on collision rate CBM year: 5 · 10 6 s ≈ 2 months CBM year: 5 · 10 6 s ≈ 2 months Assumed sensor time resolution: t int = 30 µ s Assumed sensor time resolution: t int = 30 µ s Collision rate (interactions/s)Collisions/year(mbias) No pile up 3 · 10 4 1.5 · 10 11 Pile up N N x 3 · 10 4 N x 1.5 · 10 11 Pile up 5 1.5 · 10 5 7.5 · 10 11

29. Event generators Nuclear collisions: UrQMD used for the final state phase space distributions of hadrons for Au+Au collisions at 25 AGeV. Delta electrons are generated with GEANT by the passage of beam particles through the target. D0 signal generated with thermal model and embedded in Au+Au collisions. Production multiplicity taken from: – –HSD : 2 x 10 -4 – –SHM : 3.7 x 10 -5 Due to low production multiplicities, event mixing-like technique is used to generate high statistics for background.

30. Data Processing and Data Levels Event generator UrQMD, HSD, user defined,... Transport (VMC) GEANT3, GEANT4, FLUKA,... Detector Response Reconstruction Analysis CBMROOT Simulation (MC) GEN MC RAW ESD Data Level Experiment DAQ CBM Software Workshop, Ebernburg, 8 November 2011 30 Volker Friese

31. 20/12/2011 Christina Anna Dritsa 31 Background rejection π+π+ K-K- D0D0 PV SV Cuts: e.g. impact parameter, vertex position, quality of vertex…

32. Cut effect

33. Explain pile up ٭ ٭ ٭ ٭ ٭ ٭ ٭ ٭ ٭ ٭

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35. Detector geometry MVD for vertex reconstruction STS for track and momentum reconstruction PID assumptions…