1 / 47

October 30, 2006

Search for Higgs Boson Production in Association with W-Boson at CDF. Yoshiaki Kusakabe (Waseda) for CDF Collaboration. October 30, 2006. DPF and JPS meeting 2006 Sheratorn, Wikiki, Honorulu ,Hawaii. Outline. Introduction Overview of the Experiment TEVATRON CDF Detector

timmyl
Télécharger la présentation

October 30, 2006

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 Higgs Boson Production in Association with W-Boson at CDF Yoshiaki Kusakabe (Waseda) for CDF Collaboration October 30, 2006 DPF and JPS meeting 2006 Sheratorn, Wikiki, Honorulu ,Hawaii

  2. Outline • Introduction • Overview of the Experiment • TEVATRON • CDF Detector • Search for Higgs Boson Production • Higgs Boson Production at TEVATRON • Constraints on Higgs Boson from Past Experiments • b-tagging • Background • Signal • Limit 4. Conclusions Yoshiaki Kusakabe

  3. Introduction • Motivation • What are matters composed of? • How do forces propagate? • How does particle have mass? The Standard Model? Gauge Bosons of “Strong”, “Weak” “ElectroMagnetic” forces Fundamental particles Higgs Boson? No evidence yet… Yoshiaki Kusakabe

  4. TEVATRON Integrated Luminosity C.M. Energy: 1.96 TeV CDF DØ p p Main Injector TEVATRON TEVATRON: p,pbar:  980GeV ∫Ldt~1.5fb-1 (acquired by CDF) 1fb-1 (good quality w/ Si) (∫Ldt~110pb-1 (RUN 1)) Yoshiaki Kusakabe

  5. CDF Detector Solenoid (1.4T) -bend charged particles h -ln[tan(q/2)] =1.0 (40.4) Silicon Vertex Detector - Detect tracks for Secondary vertex reconstruction  =2.0 (15.4) Central Outer Tracker - Measure charged particle tracks  = 3.0 (5.7) EMand HAD Calorimeters -Measure energy of EM and HAD particles Muon Detector -Observe muon q z Yoshiaki Kusakabe

  6. Higgs Boson Production at TEVATRON Higgs Boson Production Higgs Production Cross Section Higgs Boson Branching ratio mH<135GeV/c2 : Hbb mH>135GeV/c2 : HW+W- Large cross section huge QCD bkg for Hbb Smaller cross section smaller bkg Yoshiaki Kusakabe

  7. Constraints from Past Experiments LEP2 Direct search Constraint on Higgs Boson mass from Electroweak Global Fit mH>114.4 GeV/c2 at 95% C.L. 199 114.4 < mH < 199 (GeV/c2) at 95% C.L. Focus on WHlvbb Yoshiaki Kusakabe

  8. Results from RUN2 experiment Limit on Higgs Boson Production DØ: 378pb-1 ·Br < 2 ~ 3 pb Single and double b-tagging CDF: 320pb-1 ·Br < 10 ~ 2.8 pb at least one b-tagging Improve and update the analysis Yoshiaki Kusakabe

  9. Secondary Vertex b-tagging Jet axis Secondary vertex Primary vertex Identification of bottom quark originated jets Reduce W+light flavor bkg displaced SECondary VerTeX b-tagging (SECVTX b-tagging) • b-quark has fairly long life time (1.510-12s ) • B-meson travels significantly long distance • then decays into lighter hadrons • Produce a displaced SECVTX • But… • SECVTX tagged jets are contaminated by • l-jets : finite resolution of • (light-jet) SECVTX reconstruction • c-jets : tagged frequently due to • (charm-jet) long life time of D-meson Yoshiaki Kusakabe

  10. Neural Network b-tagging SECVTX b-tagged jets are still much contaminated(~50%) by l- and c-jets Neural Network b-tagging (NN b-tagging) • Utilize SECVTX and its independent variables as input parameters • Two Networks to separate b-c and b-l in SECVTX tagged jets 8 SECVTX variables + 8 SECVTX independent variables = 16 variables Yoshiaki Kusakabe

  11. Neural Network b-tagging b-l Network b-c Network l-like b-like b-like c-like Neural Network outputs Keeping 90% of true b-jets, 65% of l-jets and 50% of c-jets are removed! Yoshiaki Kusakabe

  12. Event Selection Parton Process Detected as jets 2 jets b-tagging Observable Lepton(e/µ) Not detected by CDF detector  Missing ET Yoshiaki Kusakabe

  13. Background • Monte Carlo background: (MC based estimation) Single top, Diboson(WW, WZ, ZZ), Background Categories • Non-W QCD: (Data based estimation) • QCD jet fakes as lepton • Mistag : (Data based estimation) • Falsely tagged events by SECVTX and NN • W+Heavy Flavor: (Data and MC based estimation) Yoshiaki Kusakabe

  14. Background Estimate (single tag) ~65% mistag rejection ~50% c-jet rejection w/ NN w/o NN w/ NN ~35% total bkg rejection Data is consistent with bkg estimate for w/ and w/o NN tagging Exactly 1 b-tagging (single tag) Yoshiaki Kusakabe

  15. Background Estimate (double tag) Data and Bkg are consistent each other At least 2 b-tagging (double tag) * NN b-tagging is NOT applied, because double tagged events are pure enough Yoshiaki Kusakabe

  16. Signal Systematic Uncertainty Signal Acceptance Expected Signal Events NN tag keeps 90% signal Use of b-tagging results in ~50% signal loss ~95% background removal Yoshiaki Kusakabe

  17. Sensitivity ~20% improvement by separating single and double tagging Significance: *S, B are number of events in 3 window in dijet mass distribution Significance combination: ~10% improvement by NN tagging Focus on =1 SECVTX tag w/ NN tag 2 SECVTX tag *Find the most sensitive b-tagging option a priori Yoshiaki Kusakabe

  18. Dijet Mass Distributions =1 SECVTX w/ NN tagging ≥2 SECVTX Data and Bkg are consistent each other! Yoshiaki Kusakabe

  19. Limit on Higgs Boson Production Convolute all systematics Poisson likeliood Binned Likelihood Poisson dist. for i-th bin Dijet mass distributions of data and bkg are consistent each other Fit dijet mass dist. to extract upper limit Yoshiaki Kusakabe

  20. Observed Limit Factor of >10 away from the SM Combine other channel! Talk by W. Yao (Nov. 1st) Yoshiaki Kusakabe

  21. Conclusions We performed a search for WHlbb with 1fb-1 at CDF -NN taggingimproved ~10% of sensitivity -Combined use of single and double taggingimproved ~20 % of sensitivity -Data and SM bkg are consistent each other -Set upper limit: Br < 3.9~1.3 pb @95% C.L. for mH=110~150GeV/c2 Yoshiaki Kusakabe

  22. Backup Slides Yoshiaki Kusakabe

  23. CDF Detector (3D view) Yoshiaki Kusakabe

  24. CDF Detector (3D) Yoshiaki Kusakabe

  25. CDF Detector (2D) Yoshiaki Kusakabe

  26. CDF Detector (SVX) Silicon VerteX Detector (SVX) Silicon microstrip chamber R=2.1 ~ 17.3cm || < 2.0 Yoshiaki Kusakabe

  27. CDF Detector (SVX) Yoshiaki Kusakabe

  28. CDF Detector (COT) COT Outside of silicon tracker, r=40~137 cm Covers |eta| <= 1.0 Open-cell drift chamber with argon-ethane gas in 50/50 mixture Yoshiaki Kusakabe

  29. CDF Detector (COT) Yoshiaki Kusakabe

  30. CDF Detector (Event Display) Yoshiaki Kusakabe

  31. Neural Network b-tagging Input variables for NN b-tagging Yoshiaki Kusakabe

  32. Neural Network b-tagging Good variables for b-l separation b pT(SECVTX)/pT(Jet) l Lxy significance c Good variables for b-c separation Vertex mass Pseudo-c Yoshiaki Kusakabe

  33. Neural Network b-tagging SECVTX variables for NN input Yoshiaki Kusakabe

  34. Neural Network b-tagging SECVTX independent variables for NN input Yoshiaki Kusakabe

  35. Neural Network b-tagging NN b-tagging Validation b-l Network b- enriched sample l- enriched sample Yoshiaki Kusakabe

  36. Event Selection Integrated Luminosity in Each Detector Component Miniskirt and Keystone were not operated well before Aug. 2005 Refere 95557 (pb-1) for luminosity Yoshiaki Kusakabe

  37. Mistag Positive(true) and negative(false) tagging by SECVTX Positive tag Negative tag Lxy significance: Lxy/(Lxy) - Lxy significance > 7.5 : positive tag(true) - Lxy significance < -7.5: negative tag(false) Yoshiaki Kusakabe

  38. Mistag  dependence ET dependence Tag rate Mistag rate Yoshiaki Kusakabe

  39. W+Heavy Flavor Heavy flavor fraction Tagging efficiency Yoshiaki Kusakabe

  40. Kinematics (=1 SECVTX w/ NN tagging)  ET  Leading jet 2nd leading jet Yoshiaki Kusakabe

  41. Kinematics (= 1SECVTX w/ NN tagging)  ET  Lepton HT Missing ET MET  Yoshiaki Kusakabe

  42. Kinematics (=1 SECVTX w/ NN tagging) Jet-Jet distance ∆  (1st jet-MET) W-transverse mass Yoshiaki Kusakabe

  43. Kinematics (≥2 SECVTX tagging)  ET  Leading jet 2nd leading jet Yoshiaki Kusakabe

  44. Kinematics (≥ 2SECVTX tagging)  ET  Lepton HT Missing ET MET  Yoshiaki Kusakabe

  45. Kinematics (≥2 SECVTX tagging) Jet-Jet distance ∆  (1st jet-MET) W-transverse mass Yoshiaki Kusakabe

  46. Pseudo-Experiment and Observed Limit Yoshiaki Kusakabe

  47. NNLO Cross Section and Branching Ratio Yoshiaki Kusakabe

More Related