New Cell Energy Density weights and scale factors parameterization MC09

1. New Cell Energy Density weights and scale factors parameterizationMC09 Sebastian Eckweiler Institut fur Physik Johannes-Gutenberg-Universitaet Belen Salvachua High Energy Physics DivisionArgonne National Laboratory

2. The Goal • Calculate new Global Cell Energy Weights for: • New MC09 simulation • Test weights for AntiKt jets and Cone jets • Calculate new Re-scale factors with Num. Inv. technique for all jet collections on new AODs • Store constants into database and use them as default for MC09

3. Use Validation Samples to calculate the weights Thanks to Iacopo Vivarelli for the production of these samples

4. AntiKt4Topo AntiKt4Tower Extra weight for Topo jets energy PROBLEM: Retrieving EM scale cell energy • AntiKt jets were not in the validation ESDs • need custom reconstruction • Use signal state to retrieve EM scale: • Tower jets seem fine • Topo jets were providing incorrect Cell EM energy

5. Problem and Solution • Problem: • By default make_StandardJetGetter('AntiKt',0.4,'H1Topo').jetAlgorithmHandle() was taken Calibrated Topo Clusters • Solution : Specify input collection  Uncalibrated Topo Clusters • myalg0=make_StandardJetGetter('AntiKt',0.4,'H1Topo').jetAlgorithmHandle() • myalg0.InputCollectionNames=['CaloTopoCluster'] • And commented out: doTopoClusterLocalCalibration = False • Jobs were re-sent to the grid  Correct EM energies !!! Thanks to Pier-Olivier and Pierre-Antoine

6. New H1 weights ! ---- AntiKt4Topo ---- AntiKt4Tower ---- Cone4Topo ---- Cone7Tower Only large differences for AntiKt4Tower in very low statistic region Previous releases we used Cone7Tower weights for ALL collections. Since we are mainly using now AntiKt, We propose to keep AntiKt4Topo weights for ALL MC09 collections.

7. Linearity and resolution without Num. Inversion AntiKt4Topo NO num. inversion

8. Numerical Inversion • We decide to use AntiKt4Topo H1 constants for all collections • However, the Numerical Inversion fits provided by Sebastian show difficulties to find the proper parameterization. • Large fluctuations on the function. • That did NOT happen before (see next 2 slides)

9. OLD H1 (from Cone7Tower): AntiKt4Topo

10. New AntiKt4Topo H1

11. Problem of fluctuations with new constants • OLD constants calculated with Cone7Tower and applied to all Jet Collections • NEW constants calculated with AntiKt4Topo • Difference could be due to: • Cone size • Topo vs Tower • Change on the simulation/reconstruction • We re-did the fits for new constants for Cone7Tower • Similar to OLD MC08 Cone7Tower  NOT due to simulation/reconstruction New H1 From Cone7Tower New H1 From AntiKt4Topo

12. Change Cone size and input New H1 From Cone7Tower New H1 From AntiKt4Topo Difference due to: Input constituents New H1 From AntiKt6Tower New H1 From AntiKt6Topo

13. Between AntiKt 4 or AntiKt 6: Differences are minimal. AntiKt4Topo weights for MC09

14. Numerical inversion in mc09 • Applying the new mc09 cell weights: • Improved response: • Quicker rise and closer to 1 • But: significant changes in shape • increased data - fit discrepancy • Several disadvantages inprocedure with fitted histograms: • no physical motivation • Shape depends on η-ranges: • would need several parameterizations • polynomial up to arbitrarily high orderalso unsatisfactory • difficult to automatize

15. Results - Mc09 • Discrepancies directly propagated to response • Nevertheless, scale within ± 2% : • If physical fluctuations  larger deviations in different sample expected

16. Looking for improvements • Basicall there are 2 classes of possibilities: • Stick to parameterizations: - needs lots of manual effort needed + only trivial changes in software needed • Direct use of TObjects / TGraphs:(Get scale factor with h->GetBinContent() or similar)+ would be easier to automate - eventually sensitive to statistical fluctuations • Possible combination of both methods: smoothed Graphs

17. Looking for improvements • TGraphSmooth offers several methods to smooth Graphs • Easiest possibility: smooth points using a given Kernel-function p(x,y) • Smoothed values: • Example for p(x, xi):Gaussian with mean x • Kernel-function parameters needsome adjustment:e.g. width of the Gaussian • Sensible ‚upgrade‘:p(x,xi) -> p(x,xi) / σito incorporate uncertainties

18. # entries χ2/#points Looking for improvements • TGraphSmooth offers several methods to smooth Graphs • Easiest possibility: smooth points using a given Kernel-function p(x,y) • Smoothed values: • Example for p(x, xi):Gaussian with mean x • Kernel-function parameters needsome adjustment:e.g. width of the Gaussian • Sensible ‚upgrade‘:p(x,xi) -> p(x,xi) / σito incorporate uncertainties • Adjust to give a reasonable ‚version‘of χ2/#points

19. First results • First results look promising • Scale linear within ±1% • Small deviations from ‘perfection‘: ~1% rise at low energies • Nevertheless: seems to be the way to go! 19

20. Summary and Conclusions • New global cell energy density weights for MC09 • Calculated using AntiKt 4 Topo Jets • Numerical Inversion factors will be use to recover JES • Calculated for all Jet Collections with fitting fuctions • Still TO DO: • Check energy density weighs and JES factors with other samples • Store them into database to be use in standard production • Document how the weights and factors have been calculated • Understand origin of fluctuations • Examine different techniques / parameters for smoothing • Find technical way to implement into JetCalibTools • TH2‘s from database already used in local hadron calibration • Could encode TGraphs in TH2‘s to simplify Athena modifications

21. BACK-UP

22. Reconstruction Details: JetSampling calculation

23. OLD weights: Cone7Tower

24. Linearity and resolution without Num. Inversion Cone7Tower NO num. inversion