1. Kali Calo progress report Dasha Savrina (ITEP/Moscow), Vanya Belyaev

2. Iterative  0 calibration The “standard” procedure The “standard” procedure HERA-B HERA-B Robust (as soon as  0 peak is vizible) Robust (as soon as  0 peak is vizible) “Millipede-like” algorithms are fragile “Millipede-like” algorithms are fragile Rely only on “standard” reconstruction technique Rely only on “standard” reconstruction technique No “dedicated” reconstruction No “dedicated” reconstruction Can be done “track-independent” Can be done “track-independent” Requires only limited information Requires only limited information Can be rather fast ( “on-line” mode) Can be rather fast ( “on-line” mode) 22 April 2k+10 Vanya Belyaev 2 Irina

3. Rely on “multiplicative” calibration Rely on “multiplicative” calibration 0 ~ E prs <<E ecal, 0 ~ E prs <<E ecal, the best contribution from E seed ~ E ecal the best contribution from E seed ~ E ecal simultaneous Ecal/Prs calibration is difficult simultaneous Ecal/Prs calibration is difficult Needed? Sensitivity to Prs is not large Needed? Sensitivity to Prs is not large For physics: E prs > E 0, for calibration E prs E 0, for calibration E prs < E 1 Contradiction? Contradiction? E prs > E 0 : small background + large statistics E prs > E 0 : small background + large statistics E prs < E 1 : large background + small statistics E prs < E 1 : large background + small statistics 22 April 2k+10 Vanya Belyaev 3

4. E prs E prs > E 0 : small background + very large statistics - fast convergency - constants are ”biased” without the special care E prs < E 1 : large background + small statistics - slower convergency - slower convergency -”unbiased” constants Combine both, fit three histograms: - max (prs 1,prs 2 ) < 10 MeV - prs 1 10 MeV - prs 1 10 MeV - min (prs 1,prs 2 ) > 10 MeV - min (prs 1,prs 2 ) > 10 MeV 22 April 2k+10 Vanya Belyaev 4

5. Three histograms (Inner Zone) 22 April 2k+10 Vanya Belyaev 5

6. Three histograms (Middle Zone) 22 April 2k+10 Vanya Belyaev 6

7. Three histograms (Outer Zone) 22 April 2k+10 Vanya Belyaev 7

8. Three histograms 22 April 2k+10 Vanya Belyaev 8

9. How to treat additive Prs? Prs is not a multiplicative factor! Prs is not a multiplicative factor! Projection: Projection: Prs ~0 : m *= sqrt ( ) Prs ≠0 : m *= sqrt ( + (1- ) prs/e  ) Fitting Fitting Prs ~0 : =  –   m/m Prs ≠0 : = 1 –   m/m/(1-  ),  = Prs ≠0 : = 1 –   m/m/(1-  ),  = get three coefficients 1, 2, 3 (sometimes less than three) get three coefficients 1, 2, 3 (sometimes less than three) Combine them to get the final Combine them to get the final 22 April 2k+10 Vanya Belyaev 9 Thanks to Marie- Noelle Mean value the given cell

10. Kali -  0 Data Flow for Kali -  0 (I) 22 April 2k+10 Vanya Belyaev 10 Kali -  0 Job ROOT NTuple/TTree DST or DAQ fmDST

11. Kali -  0 Data Flow for Kali -  0 (II) 22 April 2k+10 Vanya Belyaev 11 ROOT NTuple/TTree Make histos using the current estimate for calibration constants Make histos using the current estimate for calibration constants Fit histograms Fit histograms Get corrections for calibration constants Get corrections for calibration constants Iterate up to convergency produce the final set of calibration constants produce the final set of calibration constants Set of Calibration constrants CondDB (?) (optional)

12. Kali -  0 Data Flow for Kali -  0 (III) The secondary iterations The secondary iterations 22 April 2k+10 Vanya Belyaev 12 Kali -  0 Job ROOT NTuple/TTree fmDST CondDB (?) (optional) Set of Calibration constrants

13. Current status/Progress report Samples has been studied: Samples has been studied: Monte Carlo. min-bias, miscalibrated: 200M Monte Carlo. min-bias, miscalibrated: 200M Make the real large scale excersize Make the real large scale excersize 2k+9 collisions : Reco07 2k+9 collisions : Reco07 Apply simplified version to data Apply simplified version to data Combine cells into groups Combine cells into groups 22 April 2k+10 Vanya Belyaev 13 Many thanks to Albert for kind help!

14. 200M minbias MC09 sample Input: 200 M minbias MC09 Input: 200 M minbias MC09 22 April 2k+10 Vanya Belyaev 14 ROOT file DB with 6x6k histograms Fitting job ~60x6 histograms, O(1000) fits Calculate coefficients

15. Numbers Ntuple projections: 28x(20’) ~ 5 hours Ntuple projections: 28x(20’) ~ 5 hours Fitting 8xO(1 ½ h) ~ 12 hours Fitting 8xO(1 ½ h) ~ 12 hours Analysis O(5’) Analysis O(5’) However all time consuming operations can be done in parallel: However all time consuming operations can be done in parallel: CAF (Cern Analysis Farm): 8-core machine dedicated to calibration & alignment CAF (Cern Analysis Farm): 8-core machine dedicated to calibration & alignment 2-4 “interactive” iterations per night… 2-4 “interactive” iterations per night… 22 April 2k+10 Vanya Belyaev 15 Per 1 iteration!

16. Convergency The convergency is fast The convergency is fast After 5 iterations the mean correction 0.02% After 5 iterations the mean correction 0.02% rms 0.7% rms 0.7% 5% of cells has correction in excess of 1% 5% of cells has correction in excess of 1% Tails: 8 cells with 10% corrections… Tails: 8 cells with 10% corrections… The corrections after subsequence reprocessing are large The corrections after subsequence reprocessing are large rms ~ 6%, significantly larger that for any “simplified” setup. Why ??? rms ~ 6%, significantly larger that for any “simplified” setup. Why ??? 22 April 2k+10 Vanya Belyaev 16

17. “Bad Cells” Some cells have low population 90 cells Some cells have low population 90 cells Mainly in Outer Zone Mainly in Outer Zone Some cells have bad fits 9 cells Some cells have bad fits 9 cells Some cells have no convergency ~O(10) Some cells have no convergency ~O(10) Some cells have large corrections Some cells have large corrections 5% of cells have corrections in excess of 1% 5% of cells have corrections in excess of 1% a few cells have corrections up >5% a few cells have corrections up >5% 2-3 cells have maximal allowed corrections 10% 2-3 cells have maximal allowed corrections 10% 22 April 2k+10 Vanya Belyaev 17

18. Results after 2 reprocessings The framework is good, but results are bad….  The framework is good, but results are bad….  22 April 2k+10 Vanya Belyaev 18

19. Discrepancy on the edges 22 April 2k+10 Vanya Belyaev 19

20. (Preliminary) summary for MC09 Kali framework works Kali framework works The results are not encouraging (yet?) The results are not encouraging (yet?) “True” and obtained coefficients are different “True” and obtained coefficients are different Systematics of the method? Systematics of the method? Systematics of corrections/reconstruction? Systematics of corrections/reconstruction? Difference is larger at the edges… Difference is larger at the edges… Some puzzles with e/pt dependency Some puzzles with e/pt dependency Compare with “non-miscalibrated” data? Compare with “non-miscalibrated” data? Compare with Albert’s method? Compare with Albert’s method? Next steps ? Next steps ? 22 April 2k+10 Vanya Belyaev 20

21. BACKUP 22 April 2k+10 Vanya Belyaev 21

22. Cuts Standard Recontruction Standard Recontruction No Spd hit (???) No Spd hit (???) E T (  ) > 300 MeV E T (  ) > 300 MeV E PRS < 10 MeV E PRS < 10 MeV P T (  0 ) > 800 MeV the most discussed cut... P T (  0 ) > 800 MeV the most discussed cut... 22 April 2k+10 Vanya Belyaev 22

23. Kali Kali -  0 Job Regular Gaudi -based job Regular Gaudi -based job Actually “stripped-down” version of DaVinci Actually “stripped-down” version of DaVinci (optionally) apply constants to Ecal digits (optionally) apply constants to Ecal digits Calibrate/re-calibrate/mis-calibrate Calibrate/re-calibrate/mis-calibrate (re-recontruct) Calorimeter objects (re-recontruct) Calorimeter objects Clusters, Hypos, Neutral ProtoParticles, Photons LoKi -based algorithm that acts on LHCb::Particles LoKi -based algorithm that acts on LHCb::Particles StdLooseAllPhotons StdLooseAllPhotons Find good  0 →  candidates with loose cuts Find good  0 →  candidates with loose cuts Fill n-tuple Fill n-tuple (optionally) Destroy TES ! (optionally) Destroy TES ! Write femto-DST Write femto-DST 22 April 2k+10 Vanya Belyaev 23

24. Kali -  0 : fmDST Write only Spd/Prs/Ecal/Hcal digits that make contributions into “good” photons from “good”  0 - candidates Write only Spd/Prs/Ecal/Hcal digits that make contributions into “good” photons from “good”  0 - candidates Write in TES-format: Write in TES-format:Raw/Ecal/DigitsRaw/Spd/DigitsRaw/Prs/DigitsRaw/Hcal/Digits 500k minimum bias MC09 events on input: 500k minimum bias MC09 events on input: 380k evens with “good”  0 : 150MB of fmDST 380k evens with “good”  0 : 150MB of fmDST ~ 330 bytes/event, mainly due to Gaudi overhead ~ 330 bytes/event, mainly due to Gaudi overhead ~ 300GB for 10 9 available MC09 statistics ~ 300GB for 10 9 available MC09 statistics 22 April 2k+10 Vanya Belyaev 24 “Natural” input for Kali Job Easy to (mis)Calibrate!

25. Kali -  0 Kali -  0 : Summary (Some) progress in Kali( -  0 ) framework (Some) progress in Kali( -  0 ) framework Resurrect 2k+(4/5) code Resurrect 2k+(4/5) code “Ready” for full-scale test with 10 9 events “Ready” for full-scale test with 10 9 events Few tiny (pure technical) aspects to be solved Few tiny (pure technical) aspects to be solved GRID is essential GRID is essential fmDST are very useful fmDST are very useful My dream: on-line Kali -  0 22 April 2k+10 Vanya Belyaev 25

26. Calibration 22 April 2k+10 Vanya Belyaev 26

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29. Calibration 22 April 2k+10 Vanya Belyaev 29 Reco06Reco07