Closing in on the Higgs Boson at CDF Zurich, 28 May 2008 Aidan Robson Glasgow University

1/54 Aidan Robson Glasgow University  Limit-setting  Analysis techniques  Analysis stability  Improving sensitivity  Motivation  Higgs  WW  Low mass channels  CDF and Tevatron Combination  Outlook

2/54 Aidan Robson Glasgow University Higgs? Simplest way of breaking electroweak symmetry A complex doublet of scalar Higgs fields that couple to the SU(2)  U(1) vector bosons maintaining gauge invariance and have associated potential V(    ) = (    ) 2 –  2 (    ) with minimum away from   =0 Gauge bosons acquire mass and can write lepton mass terms We are left with one dynamical field single neutral scalar particle

3/54 Aidan Robson Glasgow University Higgs? WW scattering W+W+ W–W– Z/  W+W+ W–W– W+W+ W–W– W+W+ W–W– W+W+ W–W– W+W+ W–W– W+W+ W–W– W+W+ W–W– W+W+ W–W– W+W+ W–W– H H required to cancel high- energy behaviour

4/54 Aidan Robson Glasgow University Direct searches / Indirect limits e+e–e+e– ZHZH Z bbbb m H >114GeV m H <160GeV

5/54 Aidan Robson Glasgow University Tevatron proton–antiproton √s = 1.96TeV

6/54 Aidan Robson Glasgow University CDF proton–antiproton √s = 1.96TeV   = 1.0  = 0.6  = 2.0

7/54 Aidan Robson Glasgow University Decay  Br. Production m H /GeV   / fb m H /GeV gg  H qq  WH qq  qqH qq  ZH bb  H gg,qq  ttH Br q q’ W,Z H ’

8/54 Aidan Robson Glasgow University A physics programme In last year  (pp  ZZ)  3 x  (pp  H) (m H =160)

9/54 Aidan Robson Glasgow University Higgs  WW H0H0 W+W+ W–W– Br( W  l  ) ≈ 0.32 Br( W  jj ) ≈ 0.68

10/54 Aidan Robson Glasgow University Hadronic Ws? WW  l jj Signal + background Background only! W W W W l q q’ l q q’ Leptonic final states for our Higgs search

11/54 Aidan Robson Glasgow University H  WW H0H0 W+W+ l+l+ W–W– l–l– ee, e ,  ; E T Isolation m ll > 16 Z and top suppression Dilepton sample composition Drell-Yan dominated WW dominated Higgs enhanced B ZZ tt WW HHH H signal separation H0H0 W+W+ l+l+ W–W– l–l– W+W+ W–W– q q’ q 90% 10%

12/54 Aidan Robson Glasgow University Limit setting Isolation m ll > 16 or 25 Z and top suppression signal separation Background Higgs signal x 10 events X X = some observable H 1 =SM+Higgs (of mass m H ) H 0 =SM only  Construct test statistic Q = P(data|H 1 )/P(data|H 0 ) –2lnQ =  2 (data|H 1 ) –  2 (data|H 0 ), marginalized over nuisance params except   H  Find 95 th percentile of resulting   H distribution – this is 95% CL upper limit.  When computed with collider data this is the “observed limit”  Repeat for pseudoexperiments drawn from expected distributions to build up expected outcomes  Median of expected outcomes is “expected limit” Expected outcomes 95% CL Limit/SM Median = expected limit  H (pb) 95%  H /  SM 95% rescale PDF

13/54 Aidan Robson Glasgow University Limit setting (2) 95% CL Limit / SM m H / GeV Repeat for different values of m H  build up exclusion plot median 1212 illustrative m H =160

14/54 Aidan Robson Glasgow University What have we found? 95% CL Limit / SM m H / GeV expected limit observed limit illustrative expected limit observed limit illustrative DeficitExcess

15/54 Aidan Robson Glasgow University Analysis overview Spin structure WW vs H->WW lepton   NN score 0 1 var1 var2 var n Cut-based analysis Neural net approachMatrix element likelihood approach d   ∫  ƒ a (x,Q 2 ) ƒ b (x,Q 2 ) |M ab (  4 ;  )| 2 … 1.sequential combination 2.understanding relationship at a deeper level H W–W– W+W+ l –l – l+l+ extend senstivity Looking for some final distribution to which to fit templates and from which to extract limits

16/54 Aidan Robson Glasgow University Matrix element method  Use LO matrix element (MCFM) to compute event probability H  WW  l l WW  l l ZZ  ll W+parton  l +jet W  l +  E T model lepton energy resn pxpypzpxpypz lep1 LO | M | 2 : pxpypzpxpypz lep2 E x, E y parton  lepton fake rate  conversion rate x obs : (with true values y )  Compute likelihood ratio discriminator R = P s P s +  k b i P b i i k b is relative fraction of expected background contrib. P s computed for each m H  Fit templates (separately for high S/B and low S/B dilepton types)

17/54 Aidan Robson Glasgow University Neural network method NN score 0 1 var1 var2 var n Background Higgs  Various versions. Current:  Apply preselection (eg E T to remove Drell-Yan)  Train on {all backgrounds / WW} against Higgs m H =110,120…160…200 { possibly separate ee,e ,  x10  Pass signal/all backgrounds through net  Form templates NN 0 1  Pass templates and data to fitter E T  E T m ll E lep1 E lep2 E T sig Data HWW WW DY Wg WZ ZZ t fakes E T jet1  R leptons  leptons  E T lep or jet E T jet2 N jets Most recent CDF “combined ME/NN” analysis also uses ME LRs as NN input variables

18/54 Aidan Robson Glasgow University What have we found??? Last summer, CDF had 3 analyses: Matrix Element, Neurobayes Neural Net, TMVA Neural Net  expected sensitivities all similar  input distributions: well modeled  observed limits...

19/54 Aidan Robson Glasgow University What have we found??? Last summer, CDF had 3 analyses: Matrix Element, Neurobayes Neural Net, TMVA Neural Net  expected sensitivities all similar  input distributions: well modeled  observed limits... m H / GeV expected limit observed limit illustrative Excess in one of the 3 analyses!

20/54 Aidan Robson Glasgow University Grounding in SM measurements TCE TCE PHX PHX TCE CMUP TCE CMX TCE CMIOCES TCE CMIOPES PHX CMUP PHX CMX PHX CMIOPES PHX CMIOCES Neural net: Matrix element: Different subdetector combinations

21/54 Aidan Robson Glasgow University ME Input Variables leading lepton p x subleading lepton p x ExEx EyEy E T sin(  E T, nearest lep or jet )

22/54 Aidan Robson Glasgow University Stability  convergence Assessing NN stability  rogue variables – had checked data-simulation agreement in as many regions as possible Drell Yan-richWW-rich Met – applicability? training epoch successful trainingunsuccessful training training estimator  control regions…

23/54 Aidan Robson Glasgow University Complementarity Redefine discriminant for WW hypothesis: R’ = P WW P WW +  k b i P b i i  exploit different sensitivities of matrix element / neural net  verify matrix element method: cycle signal – ME is leading order - remove variables that use jet information from neural net for comparison

24/54 Aidan Robson Glasgow University NN same-sign After DY net cut WW NN score WW

25/54 Aidan Robson Glasgow University NN correlations Data WW Higgs  E T :m ll E T subleading lepton :m ll E T :m ll  E T :E T

26/54 Aidan Robson Glasgow University Increased acceptance by adding plug calorimeter (no tracking) and tracks pointing to cracks Lepton coverage electronsmuons

27/54 Aidan Robson Glasgow University Luminosity Inst luminosity (10 30 )  (nb)  recent hardware upgrades  clever prescale strategies – complicate analysis! a trigger consisting of hits in the “CMX” muon system, matched to a track Higher statistics… … but affects trigger rates Year fb –1 3fb –1 2fb –1 Total luminosity (pb –1 ) 4fb –1 delivered CDF: 3.3fb –1 to tape Store number

28/54 Aidan Robson Glasgow University Systematics JES Lepton Energy Scale ISR PDF Fakes Shape uncertainties (neural net): 40 weights stored per event (Higgs and all backgrounds!) (neural net) – and similar for matrix element analysis

29/54 Aidan Robson Glasgow University CDF H  WW expected signal events Current result is NN trained on kinematic + ME likelihood variables

30/54 Aidan Robson Glasgow University CDF limit

31/54 Aidan Robson Glasgow University CDF limit development Oct 05 Jan 07 Mar 07 Aug 07 Expected limits Feb

32/54 Aidan Robson Glasgow University Achieving a deeper understanding  Matrix element likelihood / Neural net Much work into demonstrating relationship between ME and NN approaches First attempt: removed variables that use jet information from neural net for comparison (ME is leading order plus an approximation for p T ) – saw ~equivalence More advanced attempt: swapping between kinematic 4-vector inputs, and ME likelihood inputs – excellent equivalence using all  R leptons E T variable LRHWW using kinematic only  R leptons E T variable H T E 2 lep Njet  E T lep E T jet E T Don’t know exactly what in ME is distinguishing backgrounds Similarly we “guess” at the “right” variables for the NN Closed form calculations could help to bridge the gap

33/54 Aidan Robson Glasgow University Ongoing improvements W+W+ W–W– VBF W+W+ W–W– VH  VWW l l /  Other channels:  2-jet bin  Hadronic taus  Inclusion of more triggers  Improvement of S/B through lepton selection ee m H =130m H =160 WW + Increase WW analysis sensitivity at lower masses factor 1.5 in sensitivity? Sensitivity to low m H from low m ll

34/54 Aidan Robson Glasgow University Low mass channels q q’ W,Z H

35/54 Aidan Robson Glasgow University b-tagging Secondary vertex-finding algorithm Attempt to fit tracks to decay vertex Jet probability Compares track impact parameters to measured resolution functions  probability that there are no long-lived particles in jet cone Neural network filters n tracks in secondary vertex p T fraction carried by those tracks goodness of vertex fit vertex mass transverse decay length & significance …

36/54 Aidan Robson Glasgow University ZH  llbb Double tag One tight or two loose b-tags Z from ee or  ANN 2 is the equivalent of 2.5 x more data compared to a dijet mass peak! Missing transverse energy Projection Dijet Fitter corrects jet energies for the missing energy in the event – improves ZH dijet resolution from 16% to 10%. Single tag q q’ ZHZH Z llbbllbb

37/54 Aidan Robson Glasgow University WH  l bb 25% improvement 10% gain q q’ WHWH W l b double tight tag one tight tag with NN tag 17% gain Three categories: double tight tag one tight tag + one jet probability tag one tight tag with NN tag ANN to improve signal discrimination Add isolated tracks mHmH NN output (120)

38/54 Aidan Robson Glasgow University WH  l bb q q’ WHWH W l b ME technique from single top search

39/54 Aidan Robson Glasgow University VH  E T bb E T > 70GeV One tight or two loose tags veto leptons q q’ W/Z H W/Z (l) b m jj (GeV/c 2 ) 1 tight tag2 loose tags

40/54 Aidan Robson Glasgow University VH  E T bb q q’ W/Z H W/Z (l) b Track-based discriminant for removing QCD background double tight tag one tight tag + one jet probability tag

41/54 Aidan Robson Glasgow University VH  qqbb 4 jets 2 tags  E T trigger S MH=120 /B  5 / data-driven bg Tag rate fraction measured as fn E T, n tracks, m bb,  R jj m bb m qq

42/54 Aidan Robson Glasgow University   lep  had : Br 45% lepton hadronic tau (1&3prong) 2 jets opposite charge 3 ANN: sig vs: Z , tt, QCD combined for fit Alpgen Z+jets M T (lep,E T ) / GeV/c

43/54 Aidan Robson Glasgow University CDF and Tevatron Combinations

44/54 Aidan Robson Glasgow University

45/54 Aidan Robson Glasgow University Current limit

46/54 Aidan Robson Glasgow University Current limit

47/54 Aidan Robson Glasgow University 1 Oct 07 1 Oct fb fb -1 Luminosity Year fb –1 3fb –1 2fb –1 Total luminosity (pb –1 ) 4fb –1 delivered CDF: 3.3fb –1 to tape Store number

48/54 Aidan Robson Glasgow University Achievable Sensitivity Sensitivity factors Minimum = x1.5 Further = x2.25 CDF+D0 combined - curves are sqrt(L) 95% CL

49/54 Aidan Robson Glasgow University Improving sensitivity: low m H Low Mass Higgs (~ 115 GeV) Minimum Achievable Improvements –25% b tagging (improved usage of existing taggers) Into ZH=>llbb and VH=>MET+bb –Implemented in WH=>lnubb Summer’07 –25% trigger acceptance (pre-existing triggers) Into ZH/WH => llbb, lnubb –Completed S vs B studies –20% from advanced analysis techniques studies & better usage of MET Into MET+bb and lnubb –Implemented in ZH=>llbb Summer’07 x1.5 avg. sensitivity improvement for all analysis All improvements validated on analysis/studies with real data/tools %’s are in sensitivity

50/54 Aidan Robson Glasgow University Low mass Higgs( ~115 GeV) Further Achievable Improvements –25% b tagging (NN-based) All channels –Tagger in advanced stage –Efficiency studied –25% trigger acceptance More pre-existing triggers –Based on HTTF studies –10% Tau channels (hadronic)  Id well understood from H  analysis %’s are in sensitivity All improvements validated on analysis/studies with real data/tools Additional x1.5 avg. sensitivity improvement for all analysis Mistag rate Tagging Efficiency Standard secondary vertex b-tagging

51/54 Aidan Robson Glasgow University Achievable Sensitivity Sensitivity factors Minimum = x1.5 Further = x2.25 CDF+D0 combined - curves are sqrt(L) 95% CL 115 GeV

52/54 Aidan Robson Glasgow University Exclusion region grows With 7 fb -1 exclude all masses (except real mass) 3  150: With 5.5 fb -1 tougher: Exclude 140:180 range 3  in one point:

53/54 Aidan Robson Glasgow University Scenario from Feb 2006 LHC 2007: Pilot Run, Z,W calib? 200pb -1 LHC 2008: Physics, 1fb -1 Tev 2007: 4fb -1 : HWW 4x3: at SM limit in the range. TOP and W Mass improved as well, so SM fit limits narrower. Deviations building from expected limit: we focus on this range for ATLAS Perhaps SM fit narrowing on this range. Higgs is OR Perhaps SM Fit excludes upper range. Tev 2009: 3  at 116: ATLAS 2011? for discovery. CDF keeps running!? LHC 2010: 10fb -1 : Discover it for > March 2007 May

54/54 Aidan Robson Glasgow University Jets W/Z Higgs Susy bottom- quark top- quark

55/54 Aidan Robson Glasgow University Backup...

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59/54 Aidan Robson Glasgow University Do we have to get lucky? 115 GeV 7 fb -1 => 70% experiments w/2  30% experiments w/3  160 GeV 7 fb -1 => 95% experiments w/2  75% experiments w/ 3  Solid lines = 2.25 improvement Dash lines = 1.50 improvement Analyzed Lum.

60/54 Aidan Robson Glasgow University CDF H  WW expected signal events

61/54 Aidan Robson Glasgow University Systematics: CDF H  WW %

62/54 Aidan Robson Glasgow University Systematics: D0 H  WW

63/54 Aidan Robson Glasgow University Systematics: WH  WWW

64/54 Aidan Robson Glasgow University Neural network method TMVA DY NN score 0 1 var1 var2 var n WW NN score 0 1 var1 var2 var n DY Higgs WW Higgs  Use TMVA neural nets twice  Train on Drell-Yan and Higgs m H =160 ee,e ,   Train on WW and Higgs m H =110,120…160…200; ee,e ,  DY NN x3 x  Pass signal/data/background through DY–H net  Cut  Pass remaining events through WW–H net WW NN 0 1  Fit templates E T  E T m ll E lep1 E lep2 E T sig Data HWW WW DY Wg WZ ZZ t fakes E T jet1  R leptons  leptons  E T lep or jet E T jet2 N jets

65/54 Aidan Robson Glasgow University Achievable sensitivity (CDF only)

66/54 Aidan Robson Glasgow University NN: low-mass, low m ll Sensitivity to low m H from low m ll

67/54 Aidan Robson Glasgow University Sensitivity at low mass ee ee m H = WW NN score Low mass: W  understanding becomes key WW

68/54 Aidan Robson Glasgow University Improving sensitivity: high m H High mass Higgs (~160 GeV) CDF range of achievable improvements –10-20% from hadronic taus in W decay (including better id) Ongoing studies –25-40% VH  VWW and VBF (jj in final state) Expect good S/B Ongoing studies –10-15% more triggers (existing triggers)+ more leptons %’s are in sensitivity Improvements from x1.5 to x2 in sensitivity All improvements validated on analysis/studies with real data/tools ee m H =130m H =160 WW + Increase WW analysis sensitivity at lower masses

69/54 Aidan Robson Glasgow University Selection B ZZ tt WW HHHH Matrix Element m ll > 25 GeV Metspecial N jets<=1 Matrix element discriminator Neural Net m ll > 16 GeV N jets(E T >15)=0 || N jets(E T <55)=1 || N jets(E T <40)=2 DY–Higgs neural net WW–Higgs neural net Isolation 20GeV/10GeV > 25(ee,  ) > 15(e  ) Cosmic rejection Opposite sign

70/54 Aidan Robson Glasgow University Selection Matrix element Neural net Using extended lepton coverage from WZ observation Using standard leptons Z  ee  (Met,nearest l or j) Met  (Met,nearest l or j) Met  (Met,nearest l or j) H  WW 160 Met WW Metspecial = Met x sin(Met  ) Met (if Met  > 90 0 ) Leptons: “Metspecial”: (Matrix element) Avoid Met pointing along lepton or jet direction

71/54 Aidan Robson Glasgow University Tevatron proton–antiproton √s = 1.96TeV

72/54 Aidan Robson Glasgow University CDF   = 1.0  = 0.6  = 2.0 muon chambers = 2= 2 = 3= m tracker had cal hadronic cal EM cal had cal solenoid pre-radiatorshower max silicon EM cal = 1= 1 Drift chamber to |  |<1 Further tracking from Si Calorimeter to |  |<3 Muon system to |  |<1.5

73/54 Aidan Robson Glasgow University CDF muon patchwork

74/54 Aidan Robson Glasgow University Production Decay m H /GeV Br Br( W  l  ) ≈ 0.32 Br( W  jj ) ≈ 0.68 H0H0 W+W+ W–W–   / fb m H /GeV gg  H qq  WH qq  qqH qq  ZH bb  H gg,qq  ttH

75/54 Aidan Robson Glasgow University Precision EWK fits 1-sided 95%CL upper limit 144 GeV increases to 182 GeV including LEP direct exclusion to 114GeV LEPEWWG, July 2007

76/54 Aidan Robson Glasgow University ME Input Variables leading lepton p x subleading lepton p x ExEx EyEy E T sin(  E T, nearest lep or jet )

77/54 Aidan Robson Glasgow University Higgs yields Matrix element analysis 1/fb Neural net analysis 1/fb Matrix element analysis 2/fb

78/54 Aidan Robson Glasgow University ME same sign

79/54 Aidan Robson Glasgow University ME Likelihood Ratio

80/54 Aidan Robson Glasgow University Systematics JES Lepton Energy Scale ISR PDF Fakes Shape uncertainties (neural net): 40 weights stored per event (Higgs and all backgrounds!) (neural net) – and similar for matrix element analysis

81/54 Aidan Robson Glasgow University Systematics Common uncertainties treated as nuisance parameters: Luminosity Track isolation Higgs:  s, NNLO WW: Jet veto, PDF/Q 2, generator DY: Met modeling, low mass modeling Shape uncertainties:

82/54 Aidan Robson Glasgow University NN: low-mass, low m ll Sensitivity to low m H from low m ll

83/54 Aidan Robson Glasgow University D0 H  WW (e/  ) ee   ee, e  channels 1.1fb –1 ;  channel 1.7fb –1 W+W+ W–W– VBF  includes VBF W+W+ W–W– gg  H

84/54 Aidan Robson Glasgow University D0: Other channels  H  WW  had channel 1fb –1 – select  using neural net – event likelihoods to separate signal (not currently contributing to overall limit)  VH  VWW 1.1fb –1 search for l  l’  +X (like-sign dileptons) 2d likelihood: physics/instrumental backgrounds W+W+ W–W– VH  VWW l l /

85/54 Aidan Robson Glasgow University m H (GeV) Median /  SM Observed /  SM D0 RunIIa+b Preliminary