Multivariate Analysis for Setting the Limit with the ATLAS Detector

Transcription

1 Multivariate Analysis for Setting the Limit with the ATLAS B s! µ + µ Detector T. Alexopoulos, E. Gazis, S. Leontsinis - NTU Athens A.Cerri CERN International School of Subnuclear Physics 50 th Course Erice, Sicily, 25 th June 2012 Monday, 25 June2012 S. Leontsinis National Technical University of Athens

2 Introduction Indirect probe for new physics Very small SM BR New physics could be as large as 10xSM Plenty of limits set (ATLAS, CDF, CMS, LHCb) Still no horizontal line 2

3 Analysis Strategy First 2.4 fb -1 of 2011 data have been analyzed Blind Analysis (all events ±300 MeV are not accessible until all parts of the analysis is set) 3 mass resolution families (BB, BE, EE) Two options: Cut & count Multivariate Classification of Events Combine N input variables into 1 optimal classifier Train our classifier with signal MC and use it to our data (sidebands) Separate the data in two parts Odd numbered events (used for MVA & optimization) Even numbered events (measurement) 3

4 MVA optimization Many classifiers checked NN, Boosted Decision Trees, 1 Dimensional Likelihood, Linear Discriminant Analyses, etc Boosted Decision Tree the best performance 1. ROC curves 2. C&C performance with s 1+ p B 3. Results and performance tests Optimal number of variables Overtraining Bias from training with signal MC Variables Used: p T, pointing angle calculated in 2D, χ 2 (calculated in 1D & 2D), L xy, d 0 max, d 0 min, DCA, ZCA, ΔR, proper time significance, p long max, p long min 4

5 ROC curves 5

6 BDT performance Efficiency ATLAS Preliminary Simulation s=7 TeV 3 variables 4 variables 5 variables 6 variables 7 variables 8 variables 9 variables 10 variables 11 variables 12 variables 13 variables 14 variables L xy Scalar product in the transverse plane of ( ~x ~p B )/ ~p B T L xy [mm] 6

7 BDT performance Efficiency ATLAS Preliminary Simulation s=7 TeV 3 variables 4 variables 5 variables 6 variables 7 variables 8 variables 9 variables 10 variables 11 variables 12 variables 13 variables 14 variables α 2D Absolute value of the angle in the transverse plance between Δx and p B 2D 7

8 Overtraining KS test (training vs testing sample) Tuesday, 29 May

9 Bias from training? Blind analysis cannot check if the MC is producing any bias in the signal region. Use a shifted mass sample to 6.5 GeV and perform studies with it signal region & GeV sidebands Bonus: Having all the machinery ready we also performed a test-search 1. Train and test using our BDT 2. Obtain the optimal cut using odd events 3. Apply this cut on full background sample, unblinding mass window 9

10 No(!) Bias MC 10

11 No(!) Bias Data Classifier Output ATLAS Preliminary s=7 TeV L dt = 2.4 fb -1 Pol1 fit m µ + µ [MeV] 11

12 BDT optimization and Yield extraction in 3 signal bins 6D problem: 3xq (BDT output) 3xΔm (width of mass window for signal search) Treat the problem as optimization of 6 independent variables Extract optimal q and ΔM cut One more number needed to be calculated: B + yield Binned maximum likelihood fit with 10 MeV binning Events / 10 MeV ATLAS Preliminary s=7 TeV L dt = 2.4 fb -1 Data ( µ max <1) Fit ± ± B K J/ signal Combinatorial Background Partly Reconstructed Decays ± ± B J/ background Reference channel B +! J/ K + ± m J/ K [MeV] 12

13 Result BR(B 0 s! µ + µ ) < at 95% CL Tuesday, 29 May

14 Conclusions Limits are closing the SM predicted BR New result from ATLAS coming soon (still ~3fb -1 of 2011 and 2012 data to be analyzed) Stay tuned! CMS ATLAS LHCb 14

15 Backup slides

16 B-> hh 16

17 Variables 17

18 Isolation efficiency vs pileup 18