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) 17 Feb 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 17 Feb 2k+10 Vanya Belyaev 3

4. E prs E prs > E 0 : small background + large statistics - fast convergency to “wrong” constants E prs < E 1 : large background + small statistics - slower convergency to “correct” constants - slower convergency to “correct” constantsCombine! Few iterations with E prs > E 0 Few iterations with E prs > E 0 Then switch to E prs < E 1 Then switch to E prs < E 1 (Intermediate scenario ? ) (Intermediate scenario ? )  1 : E prs < E 1  1 : E prs < E 1  2 : E prs > E 0  2 : E prs > E 0 17 Feb 2k+10 Vanya Belyaev 4

5. Kali -  0 Data Flow for Kali -  0 (I) 17 Feb 2k+10 Vanya Belyaev 5 Kali -  0 Job ROOT NTuple/TTree DST or DAQ fmDST

6. Kali -  0 Data Flow for Kali -  0 (II) 17 Feb 2k+10 Vanya Belyaev 6 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) 2k+(4-5): 3-5 iterations are OK 2k+(4-5): 3-5 iterations are OK 2k+9 : 2-3 iterations are OK 2k+9 : 2-3 iterations are OK

7. Kali -  0 Data Flow for Kali -  0 (III) The secondary iterations The secondary iterations 17 Feb 2k+10 Vanya Belyaev 7 Kali -  0 Job ROOT NTuple/TTree fmDST CondDB (?) (optional) Set of Calibration constrants

8. Current status/Progress report Samples has been studied: Samples has been studied: Monte Carlo, min-bias : 1000M Monte Carlo, min-bias : 1000M Study cuts & fit stability Study cuts & fit stability 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 : Reco06, Reco07 2k+9 collisions : Reco06, Reco07 Apply simplified version to data Apply simplified version to data Combine cells into groups Combine cells into groups 17 Feb 2k+10 Vanya Belyaev 8 Many thanks to Albert for kind help!

9. MC: bad fits & small statistics 17 Feb 2k+10 Vanya Belyaev 9 Small Statistics <100   /cell Bad Fit

10. MC: bad fits & small statistics 17 Feb 2k+10 Vanya Belyaev 10

11. Fit quality Some smartness has been introduced: Some smartness has been introduced: For small statistics the background shape is fixed from “per-area” histogram For small statistics the background shape is fixed from “per-area” histogram For “crazy mass, width”, skip the cell from *current iteration*, … For “crazy mass, width”, skip the cell from *current iteration*, … Still a lot of room for improvement Still a lot of room for improvement 17 Feb 2k+10 Vanya Belyaev 11 5953 from 6016

12. How to improve fit quality? Better background parameterization, less degrees of freedom: Better background parameterization, less degrees of freedom: e.g. sqrt(m)*p2(  m) instead of p3(m) e.g. sqrt(m)*p2(  m) instead of p3(m) Better initial setting from “per-area” histos Better initial setting from “per-area” histos Some constraints against “crazy” values Some constraints against “crazy” values Skip per iteration with hope to recover for next iteration Skip per iteration with hope to recover for next iteration Release ePrs cuts for “problematic” cells Release ePrs cuts for “problematic” cells Smart: Use Albert’s trick with fake  0 to fix the background shape Smart: Use Albert’s trick with fake  0 to fix the background shape 17 Feb 2k+10 Vanya Belyaev 12 Puzzle: 200M miscalibrated events: more stable fits… Pure technical, but important…

13. 200M miscalibrated events No more news today. No more news today. Calibration coefficients are obtained for the first iteration Calibration coefficients are obtained for the first iteration For improvement & stability, the local reprocessing of fmDST is required (“secondary iteration”) For improvement & stability, the local reprocessing of fmDST is required (“secondary iteration”) (Can also send the uncorrected coefficients to Olivier) (Can also send the uncorrected coefficients to Olivier) Some pure technical problem with reprocessing of fmDST Some pure technical problem with reprocessing of fmDST It looks like something has been changed recently in global DaVinci configuration (packing/unpacking/…) It looks like something has been changed recently in global DaVinci configuration (packing/unpacking/…) Should be easy to fix ~1-2 days… Should be easy to fix ~1-2 days… 17 Feb 2k+10 Vanya Belyaev 13

14. 2k+9 Collision Data: Kali with 12 zones 5799&5800 5799&5800 4.2M events 4.2M events Mass: 139.3±0.3 →135.0±0.3 Mass: 139.3±0.3 →135.0±0.3 Sigma: 13.5 ±0.5 →11.2± 0.4 Sigma: 13.5 ±0.5 →11.2± 0.4 5842 & 5845 5842 & 5845 0.5M events 0.5M events Mass: 139.0±0.2 →135.1±0.2 Mass: 139.0±0.2 →135.1±0.2 Sigma: 13.1± 0.3 →10.9± 0.3 Sigma: 13.1± 0.3 →10.9± 0.3 17 Feb 2k+10 Vanya Belyaev 14

15. Note:  0 mass by Victor 17 Feb 2k+10 Vanya Belyaev 15

16. Calibration 17 Feb 2k+10 Vanya Belyaev 16 Reco06Reco07

17. Instead of summary Kali works with MC and real data Kali works with MC and real data Final check with mis-calibrated MC samples Final check with mis-calibrated MC samples (fix the reprocessing) (fix the reprocessing) Kali for Reco06 and Reco07 are in agreement Kali for Reco06 and Reco07 are in agreement Some puzzle with P T (  0 ) -dependence Some puzzle with P T (  0 ) -dependence Stability of fits need to be improved Stability of fits need to be improved It must work in automated regime! It must work in automated regime! A lot of ideas how to do it A lot of ideas how to do it Selection cuts *must* be reoptimized Selection cuts *must* be reoptimized There is some confidence that Kali is does the job: Ecal calibration with  0 17 Feb 2k+10 Vanya Belyaev 17

18. BACKUP 17 Feb 2k+10 Vanya Belyaev 18

19. 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... 6 Oct 2k+9 Vanya Belyaev (Nikhef & ITEP ) 19

20. Kali -  0 Kali -  0 : Few plots 17 Feb 2k+10 Vanya Belyaev 20 all min(E prs ) > 10 MeV min(E prs )< 10 MeV, max(E prs )> 10 MeV max(E prs ) < 10 MeV

21. 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 17 Feb 2k+10 Vanya Belyaev 21

22. 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 17 Feb 2k+10 Vanya Belyaev 22 “Natural” input for Kali Job Easy to (mis)Calibrate!

23. 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 17 Feb 2k+10 Vanya Belyaev 23

24. Calibration 17 Feb 2k+10 Vanya Belyaev 24