Search for SM Higgs in the WHlvbb
Channel using ~2fb-1
Tatsuya Masubuchi
for CDF Collaboration
University of Tsukuba
XLIIIrd Rencontres de Moriond
Electroweak Interactions and Unified Theories
March 4 2008
La Thuile, ItalyIntroduction to WH Search
Higgs Boson : the only particles not discovered in theStandard Model It is possible to search Higgs boson directly atTevatron!!

Constraint on Higgs Mass
114.4 GeV (LEPII) < mH < 182 GeV
Most Probable Value (EW global fitting)mH= 76+33-24 GeV
Standard Model prefers low
mass Higgs Boson !!
WH channel (σppWH ~ 0.2 pb) : One of promisingchannels in low mass Higgs search at Tevatron
Tatsuya Masubuchi

XLIIIrd Recontres de Moriond, La Thuile2Event Selection andb Jet Identification
W+2jets signature
Event Selection

 High pT lepton
W boson High missing ET selection
 Exactly two high ET jets

W + 2 jets
selection

 b jets identification from Higgs is crucial in this analysis

 Extract Higgs signal from huge W+jets backgrounds
 Use various b jets identification algorithms
b Jets Selection
 High S/B
1. Double b-tagged events (tight+tight)
2. Double b-tagged events (tight+loose)  Low statistics
3. Single b-tagged events
Tatsuya Masubuchi

 High statistics
Low S/B

XLIIIrd Recontres de Moriond, La Thuile3Event Yield
Double btagged events

tight+tighttight+loose

Signal (mH =115 GeV)

1.11±0.14

0.94±0.11

Expected
Background80.62±18.75

Observed data 83

86.99±17.99

90

Dominant Backgrounds
W+bb, W+cc (~48%)
tt (~21%)

Single b-taggedevents
Signal (mH = 115GeV)

2.35±0.15

Expected
Background809.61±159.38

Observed data805Dominant Backgrounds
 W+bb,W+cc (~47%)
QCD fake (~23%)
 fake b-tag (~13%)

Forward electron contribution is added in final result(Signal acceptance ~10% gain)
Tatsuya Masubuchi

XLIIIrd Recontres de Moriond, La Thuile

4

WH Search Strategies
 Check the excess of observed data with Neural

Network discriminant
Neural Network Output

Neural Network Inputs
Optimizediscrimination
betweensignal andbackgrounds

BackgroundRegion

Signal Region

Signal Region

Other input variables•pT of W + 2 jet system• ∆R(lepton-ν)• pT imbalance• ΣET (loose jet)
• Minimum Mlνj
Tatsuya Masubuchi

Search sensitivity improves by ~60%,
compared to previous analysis, which
includes NN discriminant, b-tag
optimization and forward electron
 No Significant excess in signal region

XLIIIrd Recontres de Moriond, La Thuile595% C.L. Upper Limit on WH channel
 Set 95% C.L. upper limit using a binned maximum

likelihood technique Final result combines three b-tagging categories

Tatsuya Masubuchi

Result Observed(Expected) upper limit
is 8.2(7.3) times higher than SMin mH ~115 GeV Analysis is limited statistically
Future Prospect increase acceptance (isolatedtracks)
 Improve b-tag, jet energy
resolution and more advanced
multivariate technique Aim to gain another 50% andtriple the dataset by 2010

BACK UP

Tatsuya Masubuchi

XLIIIrd Recontres de Moriond, La Thuile7Tevatron and CDF• pp collider : c.m. energy 1.96 TeV• Direct Higgs search is capable inTevatron only
CDF
p Tevatron

D0

CDF Detector
Calorimeter (EM)Muon chamber

Calorimeter (HAD)

ppp
Silicon vertex detector
Central outer tracker (COT)

Use 1.9fb-1
in this analysis

Tracker

EM cal

Had calMuon detector

8b Flavor Tagging Algorithm
1.

Secondary Vertex (SECVTX) b-tagging
2.

Jet Probability b-tagging
3.

Identify b-jets using the long lifetimeof b hadrontagging eff : ~40%, fake rate : ~ 1%
Identify b-jets using impact parameterof track in jetstagging eff : ~50%, fake rate : ~5%

Neural Network (NN) b-tagging
Use 16 jet parameters (Lxy, Ntrk, Mvtx …)
Optimize NNs to separate b-jet from c-jetand light-jet
Keep 90% b-jet, reject 65% light-jet,50% c-jet (for SECVTX tagged jets)9NN b-tagging performance• Two neural network output selection are required
NNoutbl > 0.182
NNoutbc > 0.242

Keep 90% b jets after SECVTX b-tagging
Reject 65% light jets, 50% c jets
2008/2/15

博士論文本審査10

Background estimation
Single b-tagged
eventsDouble b-tagged
events (tight + tight)

Double b-tagged
events (tight + loose)

Signal Region (W + 2 jets evens)• 1, 3 and 4 jets events are verified as a control region

Tatsuya Masubuchi

XLIIIrd Recontres de Moriond, La Thuile

11Neural Network Input variables
Double b-tagged events (tight + tight)

Tatsuya Masubuchi

XLIIIrd Recontres de Moriond, La Thuile

12Neural Network output
Double b-tagged events(tight + loose)

Tatsuya Masubuchi

Single b-tagged eventsXLIIIrd Recontres de Moriond, La Thuile13