1 / 27

Search For B  Decay With SemiExclusive Reconstruction

Search For B  Decay With SemiExclusive Reconstruction C.Cartaro, G. De Nardo, F. Fabozzi, L. Lista University & INFN Napoli. Outline. Analysis progress (BAD #389 v.3) New m ES fit Data/Background comparison Sideband sample Continuum MC (preliminary)

romeob
Download Presentation

Search For B  Decay With SemiExclusive Reconstruction

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Search For B Decay With SemiExclusive Reconstruction C.Cartaro, G. De Nardo, F. Fabozzi, L. Lista University & INFN Napoli

  2. Outline • Analysis progress (BAD #389 v.3) • New mES fit • Data/Background comparison • Sideband sample • Continuum MC (preliminary) • Systematics & upper limit extraction • Further studies • Neutral energy (preliminary) • Conclusions

  3. sideband peak GeV/c2 Data Sample & B Counting • 2002 data processed and included in the analysis • mES distribution fitted as Argus (sideband) Xball (peak) • 3rd polynomial no longer used (impact on B counting < 0.6%) • B sample counting: • NBB = (1.660.09)105 • Using a Gaussian to fit the peak: • NBB reduced by 4.2%

  4. Data - MC Comparison Data • After preselection (request on # of GTL tracks and 0) • Better data/MC agreement using data sideband • Continuum MC study started • not in BAD #389 v.3 1GTL & 00 1GTL & 00

  5. Data (peak) B+B(MC,peak) Data (cont.+comb.) Data Sideband VS MC 3-prong – 3 invariant mass 1-prong – neutral energy

  6. Data/Bkg Estimate Comparison • Two procedures: • Bin-by-bin approach: • Each variable range is binned • In each bin: • fit the mES distribution • estimate the peaking and non-peaking background components • Single-bin approach • The mES fit is performed on the full variable range

  7. 0 preselection Bin5 Bin1 mES(GeV) mES(GeV) Bin25 Bin15 GeV mES(GeV) mES(GeV) Data (peak) B+B(MC) Data (cont.+comb.) Bin-by-Bin Approach Example: EMC Energy Sideband / peak argus area ratio Data

  8. Data (peak) B+B(MC) Data (cont.+comb.) Signal (MC) Bin-by-bin VS Single-bin 0 Neutral energy e missing momentum  2 invariant mass GeV

  9. Systematic Errors • Continuum and combinatorial background • Extracted from data sideband : 5.21< mES < 5.26 GeV/c2 • Scaled to peak area: mES> 5.27 GeV/c2 after preselection using single-bin approach • Systematic correction: • The ratio between number of events passing each cut in the bin-by-bin / single-bin is taken as correction • Assumed the correction can be independently applied on each variable, the total correction is the product of the corrections • On such correction we assume 100% of systematic error • Other systematic errors: • NBB reduced by 4.2% using Gaussian instead of a Xball to fit the mES peak • Efficiencies: not yet included but most of the code is available

  10. Background B    B0    NEW! B   

  11. Upper Limit Extraction -2 Log Q B ( B    ) < 2.98  10  4 B ( B    ) < 3.91  10  4 Dashed curve Solid curve We are blind on B but we can assume that the number of observed candidates is equal to the nearest integer to the expected background prediction We also include statistical and systematic uncertainty on the background estimate Notice: More B+B MC may reduce the statistical uncertaintyon the background and improve the upper limit! Assuming gaussian instead of Xball peak, # of B+B reduces by 4.2% B ( B    ) < 4.08  10  4

  12. Upper Limit Distribution • Generating data samples according to a Poisson distribution with the background prediction we get a distribution for the upper limit we can expect • Mean value : 3.6104 • Most probable value: 2.5104 BR (104)

  13. bin-by-bin data s.b. single-bin data s.b. Data (peak) B+B(MC) Data (cont.+comb.) Signal (MC) Monte Carlo Further Studies on EMC Energy • The worst data/MC disagreement is the neutral energy in  • Bin-by-bin approach doesn’t help in this case

  14. Improving Neutrals Quality • Remove fake photons using Vub recipe to get a better Data/MC agreement: • EMC acceptance • 0.41 < EMC < 2.54 • splitoff removal • 0.05 < LAT < 0.5 • 9/25> 0.9 See for details: http://www.slac.stanford.edu/BFROOT/ www/Organization/CollabMtgs/2002/detSep2002/Tues4d/ric.pdf

  15. Data (peak) B+B(MC) Data (cont.+comb.) Signal (MC) Neutral Energy Improvement • Applied cuts: • EMC • splitoff rm • Applied cuts: • LAT • 9/25 • Applied cuts: • LAT • 9/25 • EMC • splitoff rm  The variable is now less discriminating

  16. Impact on U.L. Extraction • Neutral energy with cuts on LAT and 9/25 (the most efficient for the 3-prong mode) modifies the expected background • Increasing the expected background, the upper limit will increase too: • U.L. = +0.35  10  4-absolute- Not in BAD v.3

  17. Conclusions • The analysis is at good stage • 2002 data processed and inserted in the analysis • Few bugs fixed (background double-counting, ...) • Good agreement Data/MC • Tested using wrong sign sample (total event charge  0) • Studied neutral energy quality • Studied systematic effects • Background estimate • B counting • Efficiencies not yet included completed • Channel 0 0  doesn't improve the u.l.

  18. Backup Slides Some more details….

  19. b B Decay • Allows the measurement of fB|Vub| • Determination of fB given |Vub| (from independent measures) • Standard Model: Br O(10-5-10-4 ) for the  channel • Helicity suppression in the  and e channels • Br O(10-7) for muons and O(10-12) for electrons • The best upper limits: • L3 : Br( B  tn) < 5.7  10-4 • P.L. B396 327 (1997) • CLEO: Br( B  tn) < 8.4  10-4(@ Y(4S) resonance) • P.R.L. 86 2950 (2001) l+ u B++  n

  20. SemiExclusive Reconstruction Starting idea: • To reconstruct as much as possible B mesons decays in the final states: BD0(*) n1pn2K n3 Ksn4p0 where n1=1...5,n2=0...2,n3 =0,1,n4= 0,1 • The reconstruction ignores the intermediate resonances, for example BD+Ds (Ds Fp) is reconstructed as BD+KKp • All remaining tracks and neutral objects are associate to the recoiling B on which we look for B Pro: strong continuum and combinatorial background suppression Cons: sensible reduction of the sample (breco0.2%)

  21. sideband peak GeV/c2 Data Sample & B Counting Fit: Argus(continuum)  Crystal ball (peak) 3rd polynomial no longer applied with the Argus fit • Monte Carlo (SP4, Run-1 and Run-2 conditions) • Signal43000 events B   B  D0(*) 74000 events B   B  D0(*)a1/ • Background72 × 106 generic B+B 67 × 106 generic B0B0 107×106 continuum • Continuum and combinatorial background also evaluated from data sideband • Data • 82 fb-1from Run-1 and Run-2  mES =(E*beam-p*2B)-1/2 • mES peak area is our B sample and is used as denominator for limit extraction • it is not necessary to estimate the semiexclusive reconstruction efficiency

  22. Event Preselection • Channels under study: • e Br = 18.0% •  Br = 17.5% •  Br = 11.1% • 0 Br = 25.2% •  Br = 8.95% • 0 0  Br = 8.95% • Maintained but not included Put new values!!!!! PDG 2002 Data • Preselections • 1 GTL & 0 0 • 1 GTL & 1 0 or 2 0 • 3 GTL & 0 0 • Fit to the mES distributions • Crystal Ball + Argus • Fits after the preselection used for • data-MC comparison • expected background prediction 1GTL & 00 Generic BB 1GTL & 00

  23. Cut Optimization • An automated procedure based on a scanning of the possible cut values has been developed • determine the selection that minimizes the expected upper limit • For each variable in a given selection • Choose a possible cut • Redo the selection and the toy MC for the u.l. extraction • Take the cut value giving the lowest expected upper limit

  24. 0 events decay proceeds via an intermediate  1 track and 10 pmissing> 1.4 GeV/c Neutral Energy < 100MeV 0.55 GeV < m(0) < 1 GeV SemiExcl purity mode > 50% 1-prong events 1 track, 0 0 No KS Neutral Energy <110MeV Neutral Bumps < 1 Pmissing > 1.2 GeV/c Kaon veto Particle ID  only Lepton veto pc.m.s. > 1.2 GeV/c SemiExcl purity mode > 50% Event Selection •  events • decay proceeds via two intermediate resonances, an a1 and a  • 3tracks and00 • Pmissing > 1.2 GeV/c • Neutral Energy < 100 MeV • Neutral Bumps < 1.5 • 600 MeV < m(0) < 950 MeV • 1.1 GeV < m(+) < 1.6 GeV • |p1 + p2 +p3 |> 1.6 GeV/c (c.m.s.) • Lepton & kaon veto • SemiExcl purity mode > 40%

  25. The total event charge is left as last cut in order to define two samples: Q = 0: signal sample Q  0: control sample B  + Total efficiency on signal (MC): 13.0 % Data (peak) B+B(MC) Data (cont+comb) Signal (MC) Samples and Efficiency Total Charge – 3 prong events channel selected

  26. Background • Continuum and combinatorial background • Extracted from data sideband : 5.21< mES < 5.26 GeV/c2 • Scaled to peak area: mES> 5.27 GeV/c2 after preselection • Peaking background: • Estimated from generic B+B Monte Carlo • To avoid double counting of the combinatorial background the mES sideband in generic B+B MC is scaled and subtracted to the component in the signal region • Scaled to the data peak area  e  0 

  27. Statistical Technique • Using LEP technique to combine more channels • Likelihood ratio estimator: • This definition can be extended to include PDFs A Toy MC determines the log(Q) distribution for different expected values of s to be compared with the value obtained from Q data sample The BF estimate is obtained in corrispondence of the minimum in the log(Q)distribution if exists

More Related