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Grégory Schott Institute for Experimental Nuclear Physics

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Presentation on theme: "Grégory Schott Institute for Experimental Nuclear Physics"— Presentation transcript:

1 Results of combination Higgs toy combination, within and across experiments, with RooStats
Grégory Schott Institute for Experimental Nuclear Physics of the Karlsruhe Institute of Technology on behalf of the ATLAS-CMS Higgs groups and statistics forum ATLAS-CMS statistics meeting, 5th July 2010

2 Introduction I will present the results of ATLAS, CMS and the combination summary of the results obtained by Kyle Cranmer and myself thanks to Kyle for the FC, PL and some Hybrid results in case of PL we both get results that of course agree very well This is on-going work; while a large part of the results are available, not everything has been done yet and not everything has been validated yet Toy study (made with mockup numbers), H→WW at 160 GeV with 1fb-1 don't draw physics conclusion of the results Significance and upper limit computed with RooStats (for a given data) different approaches tested as recommanded by the statistics forums Validation of RooStats (ROOT , June 2009) Conclusion and discussions Grégory Schott - ATLAS-CMS statistics meeting

3 The combined model Combination of H→WW→ee, mm, em analyses
Parameter of interest: ratio of cross-sections, r = m = s / sSM CMS analysis: 3 observables, 37 nuisance parameters Lognormal distribution of nuisance parameters Uncertainty on control region measurement in the systematics ATLAS analysis: 9 observables, 12 nuisance parameters Truncated gaussian distribution of nuisance parameters Control regions measurements included in the analysis Combined analysis: common parameter interest (thus, 100% correlated) no other correlation (yet) across the experiments Concretely, all the information for the combination is shared via 2 ROOT files (for ATLAS & CMS) containing RooFit/RooStats workspaces graphical representation of the objects in the combined model Grégory Schott - ATLAS-CMS statistics meeting

4 Toy-data Data used for significance:
generated from the model in the hypothesis that signal is present Data used for exclusion: generated from the model in the background-only hypothesis Check the backups if you're interested by the number of observed events assumed (or the likelihood functions) Grégory Schott - ATLAS-CMS statistics meeting

5 Profiled likelihood results
significance: reasonably good estimate if large enough 95% CL UL taken for -log l(r) = 1.921 assumption of Wilks's asymptotia that's not always valid! with systematics r signif. ATLAS 1.13 2.70 CMS 0.98 4.89 COMBI 1.02 5.58 ^ with systematics r UL ATLAS 0.79 CMS 0.28 COMBI 0.25 ^ Grégory Schott - ATLAS-CMS statistics meeting

6 Hybrid Frequentist-Bayesian
B-toys CMS, 477k toys SB-toys QLEP QTEV l(m) data test statistics significance (no syst.) significance (with syst.) ATLAS QLEP QTEV l(m) 3.78 - 3.07 ± 0.01 2.8 ± 0.1 CMS 6.22 ± 0.02 4.77 ± 0.02 > 4.6 4.3 ± 0.1 COMBI > 3.5 run for the 3 test statistics mentionned in the previous talk need to study the advantage and inconvenient of the 3 approaches Grégory Schott - ATLAS-CMS statistics meeting

7 Hybrid test statistics distributions
computing the p-value for significance in this approach is challenging: speed improvements would be useful or use importance sampling techniques CMS distribution (and results previous slide) made with a RooFit-independent tool QLEP QLEP CMS model with 'LandS' Grégory Schott - ATLAS-CMS statistics meeting

8 Summary of upper limits
with BAT [ Caldwell, Kollar, Kroeninger, Comp.Phys.Com. 180, 2197 (2009) ] CMS ATLAS 95% CL UL posterior (r | data) r 95% CL upper limits: results with systematics (except if indicated otherwise) technique test stat rule sampling UL ATLAS UL CMS UL COMBI Feldman-Cousins (no syst.) l(m) CLS+B toys 0.69 ± 0.05 - Profile LR (Wilks) asymptotic 0.79 0.28 0.25 Feldman-Cousins++ 0.78 ± 0.05 0.26 ± 0.02 0.23 ± 0.02 Hybrid QLEP CLS ~ 0.68 0.29 ± 0.03 (LandS) ~ 0.61 Bayesian n/a, flat prior on r MCMC* 0.72 0.31 Grégory Schott - ATLAS-CMS statistics meeting

9 Validations Some validation of the analysis performed with independent software (LandS, M. Chen, CMS) Since 'LandS' was developped specifically for this analysis, it is faster than RooStats which is a general tool showing that RooStats still need to improve table of compared results: we still need to work on the missing parts PL and toy-MC results using an earlier version of the ATLAS model were validated with an independent code (Hao Liu, et. al.) further on-going work on validating Bayesian limits: S. Schmitz (CMS) currently using BAT, RooStats providing another MCMCCalculator Grégory Schott - ATLAS-CMS statistics meeting

10 Conclusion and outlook
A good start, work is on-going on Lessons learned: we achieved in a short time scale to combine the Atlas and CMS analyses -> proof of principle that we can do that proof of principle we can compare different statistical methods to one another still some weaknesses identified that RooStats need to improve we also need to continue the validations of RooStats still need to understand those results discussion has started on statistical methods we want to use in common proper treatment of correlations across experiments will require planning and thoughtful parametrization of systematics activity will continue (other masses, other channels, more thorough correlation studies, ...) Grégory Schott - ATLAS-CMS statistics meeting

11 End of talk − Backup slides
Grégory Schott - ATLAS-CMS statistics meeting

12 Toy-data Data used for significance:
generated from the model in the hypothesis that signal is present Data used for exclusion: generated from the model in the background-only hypothesis Observables: 1) nobs_bin1 = 15 2) nobs_bin2 = 7 3) nobs_bin3 = 13 4) obs_s_em_0j = 36 5) obs_ww_em_0j = 52 6) obs_tt_em_0j = 3650 7) obs_wj_em_0j = 143 8) obs_s_ee_0j = 9 9) obs_ww_ee_0j = 5 10) obs_wj_ee_0j = 49 11) obs_s_mm_0j = 18 12) obs_ww_mm_0j = 28 Observables: 1) nobs_bin1 = 4 2) nobs_bin2 = 2 3) nobs_bin3 = 3 4) obs_s_em_0j = 19 5) obs_ww_em_0j = 63 6) obs_tt_em_0j = 3814 7) obs_wj_em_0j = 123 8) obs_s_ee_0j = 6 9) obs_ww_ee_0j = 14 10) obs_wj_ee_0j = 53 11) obs_s_mm_0j = 16 12) obs_ww_mm_0j = 26 Grégory Schott - ATLAS-CMS statistics meeting

13 ATLAS likelihood model
slide from K. Cranmer, Grégory Schott - ATLAS-CMS statistics meeting

14 CMS likelihood model slide from A. Korytov, 24.06.2010
Grégory Schott - ATLAS-CMS statistics meeting

15 Profile likelihood (zoom)
Grégory Schott - ATLAS-CMS statistics meeting

16 Bayesian upper limits posterior (r | data) ATLAS posterior (r | data)
COMBI r r Grégory Schott - ATLAS-CMS statistics meeting


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