1. G. BrunoOffline week - February 20051 Comparison between test- beam data and the SPD simulations in Aliroot G. Bruno, R. Santoro Outline:  strategy of the MC simulation  comparison with real data  conclusions

2. G. BrunoOffline week - February 20052 Minibus 2 Minibus 3 Test chip 425 m 50 m 256 rows 32 columns Beam test in 2003  Test detector: 300  m sensor  Tracking precision:  (x) =  (y)  10  m  Full scan of threshold and tilt-angle y x

3. G. BrunoOffline week - February 20053 Minibus 0 Minibus 1 Test chip 425 m 50 m 256 rows 32 columns Beam test in 2002  Test detector: 200  m sensor  Tracking precision:  (y)  6  m  threshold and tilt-angle scan y x

4. G. BrunoOffline week - February 20054 Kinematics: –  -, p= 120/350 GeV, gaussian beam profile (  x =  y =0.2 cm) –Beam focusing tuned to reproduce the real data –1 track per event ; 50K events for each setup (threshold, tilt-angle, etc) Geometry: –Starting point: AliITSvSPD02 (setup for 2002 by J. Conrad e B. Nielsen) –Our developments (actually a minor work): setup for 2003 geometry with test-plane tilted (for both 2002 and 2003) thin sensor (200  m) with thick chips (750  m) for 2002 setup SPD response-function & simulation: –AliITSresponseSPD, AliITSsimulationSPD (i.e. the Ba/Sa model without diffusion) Strategy of the MC simulation Ba/Sa

5. G. BrunoOffline week - February 20055 Kinematics Geometry SPD response-function & simulation (cont.) –AliITSresponseSPD, AliITSsimulationSPDdubna (i.e. the Dubna model with the diffusion) Clustering, tracking, efficiency and precision studies are done with the same codes used for test-beam real data (see talk by D. Elia) –Immediate comparison –No bias from different algorithm Strategy of the MC simulation Dubna

6. G. BrunoOffline week - February 20056 Tracking precison: setup 2003  track (x)   track (y)  8  m For real data:  track = 10  m

7. G. BrunoOffline week - February 20057 Tracking precison: setup 2002  track (y)  5  m For real data:  track (y)=6  m

8. G. BrunoOffline week - February 20058 Comparison Ba/Sa MC vs. data Setup 2003 –P c =P r =0. (no coupling) P c =P r =0.1 (suggested coupling) Real MC P=0.1 MC P=0 Coupling has to be introduced !

9. G. BrunoOffline week - February 20059 Comparison Ba/Sa MC vs. data Ba/Sa MC Real data One might play with P r and P c (let’s say P r =0.2 P c =0.03 ) P c =P r =0.1... but this would mask the real physics ongoin in the detector !

10. G. BrunoOffline week - February 200510 Comparison Ba/Sa MC vs. data Setup 2003P c =P r =0.1 (in ALICE notes) Ba/Sa MCReal data 3 2 1

11. G. BrunoOffline week - February 200511 Comparison Ba/Sa MC vs. data Setup 2003P c =P r =0.1 (in ALICE notes) Ba/Sa MCReal data 3 1 Even if cluster type distribution can be reproduced, it will not be related with track impact on the pixels

12. G. BrunoOffline week - February 200512 Comparison dubna MC vs. data Setup 2003 –P c =P r =0. (no coupling) –standard conditions for diffusion –E th = 3220 elec/holes Real MC Coupling can help with the fine details

13. G. BrunoOffline week - February 200513 Comparison dubna MC vs. data dubna MC Real data

14. G. BrunoOffline week - February 200514 Comparison dubna MC vs. data dubna MC Real data In log scale

15. G. BrunoOffline week - February 200515 Comparison dubna MC vs. data Setup 2003 dubna MCReal data 3 2 1

16. G. BrunoOffline week - February 200516 Comparison dubna MC vs. data Setup 2003 dubna MCReal data MC distribution is narrower than real data:  not enough diffusion in the model ! 3 1

17. G. BrunoOffline week - February 200517 Comparison dubna MC vs. data dubna MCReal data Efficiency versus threshold parameters Is there a relation between DAC and MC th ?

18. G. BrunoOffline week - February 200518 Comparison dubna MC vs. data dubna MCReal data Efficiency versus threshold parameters gaussian fit no linearity Real data: threshold linear over the full range (see talk by Domenico) ! MC: at very hard threshold linearity is lost ! gaussian fit

19. G. BrunoOffline week - February 200519 Comparison dubna MC vs. data dubna MCReal data Efficiency versus threshold parameters

20. G. BrunoOffline week - February 200520 dubna MC This naive method can give a good estimate ! Comparison dubna MC vs. data no MC linearity

21. G. BrunoOffline week - February 200521 Comparison dubna MC vs. data dubna MCReal data Precision of the tracking is a bit better in the MC –it is better to compare the intrinsic resolutions

22. G. BrunoOffline week - February 200522 Comparison dubna MC vs. data dubna MCReal data 200  m steeper than 300  m both in data and in MC MC@200  m: there is a maximum as observed in real data MC@300  m: the minimum cannot be reached: one has to introduce more diffusions in the model !!!! nominal precision

23. G. BrunoOffline week - February 200523 Comparison dubna MC vs. data dubna MCReal data 300m Threshold (e - ) With more diffusion in the model the cl2 curve is expected to go up !

24. G. BrunoOffline week - February 200524 Comparison dubna MC vs. data dubna MCReal data 200m Threshold (e - ) Again, with more diffusion the cl2 curve should go up (but less than at 300 m)

25. G. BrunoOffline week - February 200525 Definition of cluster types 13245 7 8 VTH = 200

26. G. BrunoOffline week - February 200526 tilted angle 0° 300  m Real: @190 DAC @3220 e-h 300  m MC Comparison dubna MC vs. data data (DAC 190) MC (3220 e - ) * For a given threshold DAC one can already get a good matching by playing only with E th

27. G. BrunoOffline week - February 200527 tilted angle 0° 300  m Real: @190 DAC @3220 e-h 300  m MC Comparison dubna MC vs. data 0O0O

28. G. BrunoOffline week - February 200528 300  m Real: @190 DAC @3220 e-h 300  m MC tilted angle 10° Comparison dubna MC vs. data

29. G. BrunoOffline week - February 200529 300  m Real: @190 DAC @3220 e-h 300  m MC tilted angle 20° Comparison dubna MC vs. data

30. G. BrunoOffline week - February 200530 cpu consumptions in the two models hitssdigitshitssdigitshitssdigits Ba/Sa cp time 31.0 17.4 986.5 466.0 22493 12905 real time 0:00:380:00:180:17:440:08:037:03:373:46:20 Dubna cp time 30.8 240.2 987.5 2753 22529 22899 real time 0:00:38 0:04:020:17:430:46:156:55:496:37:31 1K events 10K events 50K events The Ba/Sa code is much faster for small size file (the model is simpler) but both become slow when managing large files

31. G. BrunoOffline week - February 200531 Conclusions As it is, the Ba/Sa model is not suited for studies such as charm and beauty production (displacement of the secondary vertices) The dubna model reproduces the test beam details much better In term of cpu, dubna slower than ba/sa Test beam data suggest that more diffusion has to be introduced in the model

32. G. BrunoOffline week - February 200532 What next Fine-tuning Optimization of the algorithm in term of cpu

33. G. BrunoOffline week - February 200533 A reminder of the Ba/Sa model The energy deposited in the sensitive material during the transport (at the moment GEANT) is distributed among the pixels according to two mechanisms: Charge sharing –Energy in each pixel proportional to the track path in that pixel Capacitive coupling between adiacent pixels –P c (P r ) is the probability to fire an adiacent pixel along the column (row) –If fired, it gets the same energy E of the parent pixel –Default: P c =P r =0.1 (in Aliroot set to 0) not fired Fired, Ecoupl, E

34. G. BrunoOffline week - February 200534 references: R. Caliandro, R. Dinapoli, R. A. Fini, T. Virgili, Simulation of the response of a silicon pixel detector, Nucl. Instrum. Meth. A 482 (2002) 619-628 R. Caliandro, R. Dinapoli, R.A. Fini and T. Virgili, A model for the simulation of the response of pixel detectors, ALICE INT-2000-23 R. Caliandro, R. Dinapoli, R.A. Fini and T. Virgili, Simulation of the response of the ALICE silicon pixel detectors, ALICE INT-2001-05 R. Barbera, R. Caliandro, B.V. Batyunya, A.G. Fedounov, R. A. Fini, B.S. Nilsen, T. Virgili, Status of the simulation for the silicon pixel detector in ALICE, ALICE-INT-2001-48 A reminder of the Ba/Sa model At the initial stage, noise is added to all the pixels according to a gaussian (default: sigma = 280 elec-hole pairs) From Sdigit (analog) to Digit (digital) –A pixel gives a digit if the energy is larger than a threshold E th (default: E th =2000 elec.-hole pairs) Actually the model was thought with a parametrization of the diffusion –never implemented in Aliroot

35. G. BrunoOffline week - February 200535 A reminder of the Dubna model Charge sharing: diffusion –The electrons/holes produced along the track are let to diffuse (T,V,  ). –Slower than Ba/Sa (we will quantify later) –A better physical description: essential to match the observed (real data) improvements in the intrinsic resolution due to cluster 2,3 Capacitive coupling between adiacent pixels –the same as Ba/Sa –By default switched off: P c =P r =0.0 noise: –“electronics” = “baseline” + “noise” (i.e. const + gaussian) –Default: “electronics”=0.+0. After work by B.Nielsen and J. Conrad for merging features of the two models... 3 2