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Lighting up the Higgs sector with photons at CDF

Lighting up the Higgs sector with photons at CDF. Baylor HEP Seminar. Karen Bland November 12, 2011. Outline. Introduction Tevatron and CDF Detector Photon ID and Efficiency SM H  γγ Search Fermiophobic h  γγ Search Summary and Conclusions. Outline. Introduction

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Lighting up the Higgs sector with photons at CDF

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  1. Lighting up the Higgs sector with photons at CDF Baylor HEP Seminar Karen Bland November 12, 2011

  2. Outline • Introduction • Tevatron and CDF Detector • Photon ID and Efficiency • SM Hγγ Search • Fermiophobic hγγ Search • Summary and Conclusions

  3. Outline • Introduction • Theoretical Overview • Motivation • Tevatron and CDF Detector • Photon ID and Efficiency • SM Hγγ Search • Fermiophobic hγγ Search • Summary and Conclusions

  4. The Standard Model • Higgs boson is only SM particle that hasn’t been observed! • Through the Higgs mechanism: (1) Electroweak symmetry is broken (2) Other SM particles acquire mass • But mass of Higgs boson a free parameter… • Has to be determined experimentally if exists • What we know so far: • Lower mass SM Higgs boson mass preferred by electroweak constraints • Search focus: mH =114 – 145 GeV • SM Higgs boson not to be discovered at Tevatron • However through spring 2012 Tevatron, more sensitive in much of search region than LHC experiments

  5. The Standard Model • Higgs boson is only SM particle that hasn’t been observed! • Through the Higgs mechanism: (1) Electroweak symmetry is broken (2) Other SM particles acquire mass • But mass of Higgs boson a free parameter… • Has to be determined experimentally if exists • What we know so far: • Lower mass SM Higgs boson mass preferred by electroweak constraints • Search focus: mH =114 – 145 GeV • SM Higgs boson not to be discovered at Tevatron • However through spring 2012 Tevatron, more sensitive in much of search region than LHC experiments Tevatron experiments are still working very hard to improve analysis techniques and add final datasets to fill in as much of the remaining gaps as possible. The Hγγ analysis contributes to this effort Hγγ contributes sensitivity here

  6. SM Higgs Production at the Tevatron Gluon Fusion • ggH is largest cross section • Excluded from channels where Higgs decays to quarks due to multijet backgrounds (like Hbb) Associated Production Vector Boson Fusion

  7. SM Higgs Production at the Tevatron Gluon Fusion~ 1000 fb @ 120 GeV • ggH is largest cross section • Excluded from channels where Higgs decays to quarks due to multijet backgrounds (like Hbb) • Hγγ gains by using all three production methods (~1300 fb) • Produced only rarely: • One out of every 1012 collisions • That’s about 2 Higgs bosons produced each week Associated Production~ 225 fb @ 120 GeV Vector Boson Fusion ~ 70 fb @ 120 GeV

  8. SM Hγγ Decay • Dominant low mass decay mode is Hbb • Hγγ Br < 0.25% • Signal expectation @ 120 GeV:N = σ×L × Br = 1300fb × 7.0fb-1 × 0.002 ~ 18 Hγγ events produced (~ 6 reconstructed)

  9. Is a Hγγ search interesting at the Tevatron? • Many beyond SM scenarios include a larger Br(Hγγ) • New results for one such scenario shown later in the talk • Small Br, however contributes sensitivity to Tevatron search in difficult region ~125 GeV:

  10. Is a Hγγ search interesting at the Tevatron? • Clean signature compared to Hbb • Photons (or electrons from photon conversions) easier to identify/reconstruct than b-jets • Larger fraction of Hγγ events accepted in comparison • Total acceptance: • ~35% accepted for ggH • ~30% accepted for VH and VBF • Largest efficiency losses from fiducial requirements and ID efficiency • Also improves reconstructed mass resolution…

  11. Is a Hγγ search interesting at the Tevatron? • Great mass resolution: • Mass resolution limited only by electromagnetic (EM) calorimeter and ability to select correct vertex of event (natural width negligible) • 1σ width ~3 GeV or less(Mjj width is ~16 GeV) • Resolution ~5x betterthan best jet algorithms for Hbb • Great background discrimination using Mγγ alone • Search for narrow resonance onsmoothly falling background • Fits to non-signal region of mass spectrum can be used to estimate background

  12. Outline • Introduction • Tevatron and CDF Detector • Photon ID and Efficiency • SM Hγγ Search • Fermiophobic hγγ Search • Summary and Conclusions

  13. Tevatron • pp collisions at √s = 1.96 TeV • Peak luminosity 414×1032 cm-2s-1 • Shut down on Sept. 30th, 2011 • 11.9 fb-1 delivered • 9.9 fb-1 stored on tape at CDF Results shown here use 7.0 fb-1

  14. CDF Detector and Particle Identification e’s and γ’s interact in calorimetry via electromagnetic cascades (i.e. ionization andbremmstrahlung for e’s and photoelectric effect, Compton scattering, and pair production for γ’s) Hadrons interact in calorimetry via cascades of nuclear interactions (much more complex than EM cascades) “Jets” come from quarks or gluons fragmenting Muons long-lived and won’t interact incalorimetry;leave trackin trackingdetector andmuonchambers Charged particles leave a “track”in the tracking chambers Want a detector that can differentiate between different types of final state particles

  15. Electromagnetic Calorimeter p Muon Chambers Central Tracker Silicon Vertex Detector CDF Detector Hadronic Calorimeter p Solenoid

  16. Outline • Introduction • Tevatron and CDF Detector • Photon ID and Efficiency • Introduction • Central Photons • Forward Photons • Conversion Photons • SM Hγγ Search • Fermiophobic hγγ Search • Summary and Conclusions

  17. Photon Identification • “Central” • |η|<1.1 • “Plug” • 1.2<|η|<2.8 • Tracking efficiency lower than in central region • Easier to miss a track and reconstruct fake object as a photon • Higher backgrounds then for plug photons Central Plug Cross sectional view of one detector quadrant

  18. Photon Identification • Basic Photon Signature: • Compact EM cluster • Isolated • No high momentum track associated with cluster • Profile (lateral shower shape) consistent with that of a prompt photon • Unlike that of π0/ηγγ decays (the largest background for prompt photons) • Hard to do this with calorimeters alone Signal Inside jets Background

  19. Photon Identification • ΕΜ calorimeter segmentation: • Δη×Δϕ ~ 0.1×15° (|η|<1) • Not fine enough to fully reject π0/η jets Hadronic Calorimeter • Shower max detector • ~6 radiation lengths into EM calorimeter • Finely segmented • Gives resolution to better reject π0/ηγγ • Αlso refines EM cluster position measurement to better match associated tracks Electromagnetic Calorimeter Shower maximum detector Signal Background

  20. Central Photon Identification • Three level selection • (1) Loose requirements • Fiducial in shower max detector • Ratio of hadronic to electromagnetic transverse energy (Had/EM) < 12.5% • Calorimeter isolation • . • Cut slides with • Track isolation < 5 GeV • (2) Track veto • Number tracks ≤ 1 • If 1, then pTtrk1 < 1 GeV • (3) Cut on NN Output • More details on next slides

  21. Central Electron Identification • Three level selection • (1) Loose requirements • Fiducial in shower max detector • Ratio of hadronic to electromagnetic transverse energy (Had/EM) < 12.5% • Calorimeter isolation • . • Cut slides with • Track isolation – pTtrk1 < 5 GeV • (2) Track veto • Number tracks ≤ 2 • If 2, then pTtrk2 < 1 GeV • (3) Cut on NN Output • More details on next slides • No pure high statistics data sample of photons to validate ID efficiency • Selection chosen so can be modified for electrons • Then use Ze+e– decays (more detail later)

  22. Central Photon Identification • Relative to standard photon selection, increases signal efficiency by 5% and background rejection by 12% NN discriminant constructed from seven well understood variables: • Ratio of hadronic to EM transverse energy • Shape in shower max compared to expectation • Calorimeter Isolation • Track isolation • Ratio of energy at shower max to total EM energy • Lateral sharing of energy between towers compared to expectation Trained using inclusive photon MC and jet MC (with ISR photons removed and energy reweighting) s/sqrt(b) for Hγγ vs NN cut gives optimum cut of 0.74

  23. Central Photon ID Efficiency • ID efficiency checked in data and MC from Ze+e– decays • Z mass constraint applied to get a pure sample of electrons to probe • Effect of pile-up seen through Nvtx dependence • Net efficiencies obtained by folding εvtx into Nvtx distribution of diphoton data and signal MC (a weighted average) • Net photon ID efficiency: Data: 83.2% MC: 87.8% • MC scale factor of 94.8% applied • Total systematic uncertainty of 2% applied from: • Differences between electron vs photon response (checked in MC) • Data taking period dependence • Fits made to Z mass distribution • Small uncertainties using this method!

  24. Plug Photon ID and Efficiency Standard CDF Cut-Based ID Same Efficiency Technique as for Central Photons Net photon ID efficiency: Data: 73.2% MC: 80.6% MC scale factor of 90.7% applied Total systematic uncertainty of 4.5% • Fiducial in shower max detector • Ratio of hadronic to EM transverse energy* < 5% • Calorimeter isolation* < 2 GeV • Track isolation* < 2 GeV • Shape in shower max compared to expectation * Slides with EM energy or ET

  25. Photon Conversions • γe+e– • Colinear tracks moving in approximately same direction • Occurs in presence of detector material • More material, higher the probability of converting port cards, cables COT inner cylinder ISL outer screen L00, L0-L4 L6 L7

  26. PhotonConversions • Conversion probability at CMS substantially higher*… • ~70% of Hγγ events have at least one photon that converts!! • Similarly for ATLAS • Much more important at LHC experiments! * J. Nysten, Nuclear Instruments and Methods in Physics Research A 534 (2004) 194-198 p ≈ 15% for central γ • Use central only • Then for two photons, % of events lost from a single central photon converting is: • 26% for CC channel • 15% for CP channel • CDF had only one Run I measurement using converted photons: γ cross section  PRD,70, 074008 (2004) • Hγγ is the second analysis to use it for Run II

  27. Conversion ID • Base selection: • |η|<1.1 • Oppositely signed high quality tracks • Proximity: r-f sep and Δcotθ • e + (γe+e–) “trident” vetophoton radiated via bremmstrahlung • Other tighter selection on calorimeter and tracking variables applied to further reduce backgrounds • 7% uncertainty in conversion ID taken as systematic from Ze+trident studies cut ~94% efficient r-ϕ separation (cm) cut ~95% efficient cotθ= pz/pT Example trident Δcotθ

  28. Outline • Introduction • Photon ID and Efficiency • SM Hγγ Search • Event Selection • Background Modeling • Results • Tevatron Combination • Fermiophobic hγγ Search • Summary and Conclusions

  29. Event Selection • Inclusive photon trigger • Single photon ET > 25 GeV • Trigger efficiency after offline selection obtained from trigger simulation assuming zvtx = 0 and trigger tower clustering • Use photon ID as previously described • Photon pT > 15 GeV • Four orthogonal diphoton categories: • Central-central photons (CC) • Central-plug photons (CP) • Central-central conversion photons (CC Conv) where one converts • Central-plug conversion photons (CP Conv) where central converts

  30. Primary Background Composition • Real SM photons • Irreducible background • Via QCD processes from hard interaction • Fake backgrounds • Reducible backgrounds • Electrons from Z/γ*e+e– • Jets fragmenting to neutral mesons (π0/ηγγ) which then decay to pairs of colinear photons and are reconstructed as a single photons

  31. Data-Driven Background Model • Fit to non-signal (“sideband”) regions of Mγγ distribution • We use a 6 parameter polynomial times exponential to model smooth portion of the data • Fit is then interpolated into the 12 GeV signal region to estimate background expectation for Higgs mass hypothesis • Example shown here for a test mass at 115 GeV for CC channel

  32. Data-Driven Background Model • Channels with a plug photon have a non-neglible contaminated from Z background • Breit-Wigner function added to smooth distribution to model this, where mean and width are bounded in fit • Example shown here for a test mass at 115 GeV for CP channel

  33. Background Model • Vertical red lines show window excluded from fit for Higgs mass hypothesis being tested • Interpolated fit used to obtain data-fit residuals • Used to inspect for signs of a resonance for each mass and channel • No significant resonance observed CC Channel CC Conversion Channel

  34. Background Model • Vertical red lines show window excluded from fit for Higgs mass hypothesis being tested • Interpolated fit used to obtain data-fit residuals • Used to inspect for signs of a resonance for each mass and channel • No significant resonance observed CP Channel CP Conversion Channel

  35. Background Rate Uncertainty • Parameters of fit function varied within uncertainties to obtain a new test fit • Integral in 12 GeV signal region calculated for test fit • Repeated many times • Largest upper and lower differences from standard fit stored • Then symmetrized to obtain rate uncertainty for each test mass and channel • Model dependence checked by testing alternate fit functions • Variation in normalization as compared to standard found to be within uncertainties already obtained

  36. CC Channel Discriminant and some Math Checks • We show these distributions so you can check our results with some simple math… • Use 12 GeV signal region, so as an example, here it would be 120 +- 6 GeV • Real limits are much more complicated, but this is a rough check that results are in the right ball park • N Signal (S) = 2.2, N Bkg (B) = 271 +- 16.5 • 16.5 1σ statistical error on the background expectation  68% of the time • For S = 2.2, obviously we’re not sensitive to a SM Higgs observation • How many signal events would we be sensitive to at say a 95% C.L? • 33  2σ  about 95% data fluctuates between 271 +- 33 • The number 33 is simple 95% upper C.L. limit on the amount of reconstructed signal we’re sensitive to based on bkg expectation alone • We can excludes models with predict > 33 γγ decays at this mass @ 95% C.L. • How much is this relative to the SM prediction?: 33/2.2 = 15  This is a simple 95% C.L. upper limit relative to the SM prediction • Including systematic uncertainties degrades limits by about 10-15%

  37. Final Discriminants Limits add as ~ 1/Li2 similar to a || resister, where Li is limit for an individual channel

  38. SM Limits arXiv:1109.4427To be published in PRL • Shown are 95% upper C.L. limits on σ×Br relative to SM prediction using a Bayesian method • Most sensitive expected limit is for 120 GeV where limit is ~13.0×SM • An improvement of ~33% on last result presented! • Improvements from better central photon ID, including forward photons, and reconstructing photon conversions • Observed limit at ~28×SM above 2σ but we didn’t consider “look-elsewhere effect” • With this affect considered, has less than 2σ significance • This result included in Tevatron Higgs combination • First SM Higgs result from CDF Run II to be published • Has also been combined with D0 results to give a Tevatron SM Hγγ result…

  39. DØ’s SM H→γγ Search • Uses a boosted decision tree as final Hγγ discriminant • Βased on five kinematic inputs: Mγγ, pTγγ, ET1, ET2, Δφγγ • Example output shown for mass of 115 GeV • From March 2011 • Using 8.2fb-1 • Observed @ 115: 12.5xSM • Expected @ 115: 15.8xSM • PRL 107, 151801 (2011)

  40. Tevatron 95% C.L. Limits on Hγγ arXiv:1107.4960

  41. Tevatron vs LHC • Due to higher jet backgrounds, the LHC is betting on the Hγγ channel rather thanHbb for a low mass Higgs discovery… • Also gain from higher cross sections, calorimeter resolution, and mass resolution • Limited by Br (as at Tevatron) and multiple interactions (pileup) • As of Sept, 2011 both CMS (CMS-PAS-HIG-11-021) and ATLAS (arXiv:1108.5895) have results in Hγγ of about 3-4xSM expectation: • Tevatron is clearly not competitive with LHC in this channel… how about in BSM models?

  42. Outline • Introduction • Tevatron and CDF Detector • Photon ID and Efficiency • SM Hγγ Search • Fermiophobic hγγ Search • Theory Motivation • Differences in search from SM • Results • Tevatron Combination • Summary and Conclusions

  43. Fermiophobic Higgs (hf) • It’s likely nature doesn’t follow the SM Higgs mechanism… • We also consider a “benchmark” fermiophobic model • A two-Higgs doublet model extension to the SM • Spontaneous symmetry breaking mechanism different for fermions and bosons  5 Higgs • We search for one in which: • No Higgs coupling to fermions • SM Higgs coupling to bosons • SM production cross sections assumed

  44. Fermiophobic Higgs (hf) Production Gluon Fusion~ 1000 fb @ 120 GeV • No gghf • σ ~ 300 fb @ 120 GeV Associated Production~ 225 fb @ 120 GeV Vector Boson Fusion ~ 70 fb @ 120 GeV

  45. Fermiophobic Higgs (hf) Decay • hbb no longer dominant Suppressed by m2b/m2W • Dominant low mass decay mode is now hγγ • Signal expectation @ 120 GeV:N = σ×L × Br • = 300fb × 7.0fb-1× 0.03 ~63 (22) hfγγ events produced (reconstructed) ~4x higher than SM expectation Br ~ 13x higher than SM @ 120 GeV

  46. Fermiophobic Higgs (hf) Decay • hbb no longer dominant Suppressed by m2b/m2W • Dominant low mass decay mode is now hγγ • Signal expectation @ 100 GeV:N = σ×L × Br = 560fb ×7.0fb-1× 0.18 ~700 (245) hfγγ events produced (reconstructed) ~30x higher than SM expectation Br ~ 120x higher than SM @ 100 GeV

  47. Event Selection • Inclusive photon trigger • Single photon ET > 25 GeV • Trigger efficiency after offline selection obtained from trigger simulation assuming zvtx = 0 and trigger tower clustering • Use photon ID as previously described • Photon pT > 15 GeV • Four orthogonal diphoton categories: • Central-central photons (CC) • Central-plug photons (CP) • Central-central conversion photons (CC conv) where one converts • Central-plug conversion photons (CP conv) where central converts • gghf suppressed • Optimize for VH/VBF • Split into three diphoton pt bins: • High: pT> 75 GeV • Medium: 35 < pT < 75 GeV • Low: pT < 35 GeV • 4 diphoton categories x 3 pT bins = 12 total channels Greatest sensitivity! Same as SM Search Different for hf search

  48. Background ModelExample fits for CC for each pTγγ bin Same approach for background model as done for SM High pTγγ Bin At 120 GeV: N signal = 2.9 s/sqrt(b) = 0.66 Medium pTγγ Bin At 120 GeV: N signal = 2.5 s/sqrt(b) = 0.37 Low pTγγ Bin At 120 GeV: N signal = 1.3 s/sqrt(b) = 0.09

  49. Results arXiv:1109.4427To be published in PRL • At 95% C.L., observed (expected) on B(hfγγ) exclude a fermiophobic Higgs boson with a mass < 114 GeV (111 GeV) • A limit of 114 GeV is currently the world’s best limit on a hf Higgs from a single experiment • To be published in PRL with SM result • Has also been combined with D0 results to give a Tevatron hfγγ result…

  50. DØ’s fermiophobic hf→γγ Search • Same as SM, but no ggH and higher Br • Uses a boosted decision tree as final hfγγ discriminant • Βased on five kinematic inputs: Mγγ, pTγγ, ET1, ET2, Δφγγ • Example output shown for mass of 115 GeV • From March 2011 • Using 8.2fb-1 • Observed @ 115: 12.5xSM • Expected @ 115: 15.8xSM • PRL 107, 151801 (2011)

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