Status Status ReportReport on hbb Analysis on hbb Analysis
Jyothsna Rani Jyothsna Rani
for the hbb groupfor the hbb group
Andy, Avto, Marine, Tim, BorisAndy, Avto, Marine, Tim, Boris
All D0 MeetingAll D0 Meeting
2828thth January 2005 January 2005
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OutlineOutline
Introduction Higgs production Signal kinematics Analysis overview Limits and Exclusion plots Summary & Outlook
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IntroductionIntroduction• MSSM has pairs of Higgs
doublet fields– Hu couples to up-type quarks
and leptons and Hd to down-type
– The ratio of their VEV ’s is defined as:
tan = <Hu>/<Hd>
– 5 Higgs particles after EWSB: h0, H0, A0, H+, H-
– h0 is ‘guaranteed’ to be light:
m h0 < ~ 130 GeV (MSSM)
TOP 5FNS
BOTTOM 4FNS
h0, H0, A0 production at Tevatron
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bh vs bbhbh vs bbh
Cro
ss-s
ecti
on (
fb)
Cro
ss-s
ecti
on (
fb)
Inclusion of closed top loop diagrams Solid 4FNS
Dashed 5FNS
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Higgs Production and Large tan Higgs Production and Large tan Large tan → enhanced bbh/H/A
At tree level , cross section rises like tan2 A and h/H are produced simultaneously.
CP odd
Higgs
CP even Higgs
h H
A
Total cross section
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Neutral MSSM Higgs Branching RatioNeutral MSSM Higgs Branching Ratio
CP odd
Higgs
CP even
Higgs
tan = 5
tan = 5
tan = 40
tan = 40
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Masses and WidthsMasses and Widths
Using M. Spira’s HDECAY 3.101, tan = 30
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QCD Calculations at NLOQCD Calculations at NLO• Significant progress
has been recently made by theorists– Have d/dpT, d /d at
NLO– Uncertainties from
renormalization/ factorization scales
variation– PDF errors evaluated
following CTEQ prescription For generator cuts:
|| < 2.5 and pT > 15 GeV
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MSSM FrameworkMSSM FrameworkSUSYOnly recently did thorough
investigations.Have tan enhancement factors in 5
MSSM scenarios at one-loop level.Significantly different from tree level
assumption of tan2Along the lines of the Tevatron SHWG
studiesMore importantly, have ×BR enhancement
factors in terms of
taneffective vs. tan
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MSSM scenariosMSSM scenarios
])1(9[
9
)1(
tan2 22
2
bbSMSUSY
BR
tanb
bb h
h
Function of various
SM/SUSY parameters:
Xt=At-cot, , Mg, Mq, etc.
• Loop level corrections to cross section and BR
with
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Pre-selection and LuminosityPre-selection and Luminosity Pre-selection cuts:
“at least three offline jets with lead jet pt > 20 GeV and two jets with pT >15 GeV (uncorrected) and || < 2.6”
Data collected during Nov 2002 – June 2004 with v9 – v12 Trigger List versions.
87.5M events corresponding to Integrated Luminosity 260 pb-1
Exclude Jet/MET badLBNs for v9 – v12 Trigger List.
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Event Selection - TriggeringEvent Selection - Triggering
NewOldLevel
3 * 5GeV4 * 5GeVL1: tower ET
2 * 25GeV1 * 15GeV
3 * 15GeVL3: jet ET
3 * 8GeV50GeV
L2: jet ET
(ET>5GeV)
Three levels of triggering (old and new trigger version).
Efficiencies relative to offline selection of 68-80%,
depending on Higgs Mass.
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Event Selection - OfflineEvent Selection - OfflineLoose initial analysis cuts
Optimized analysis cuts for each signal mass
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Jet pT and distributions
distributionJES corrected pT
In Data after the Kinematical cuts.
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MC Samples (Signal and Background)MC Samples (Signal and Background)
• Signal Samples:– h(bb)b, 3b final state with parton pT > 15GeV
mh=90, 100, 110,120, 130 and150 GeV each 100K events using PYTHIA generator.
• Background Samples:– Heavy Flavor Multijet process “bbjj” (ALPGEN).– QCD Irreducible process “bbbb” (ALPGEN).– Fake Jets, “jjjj” (obtained from DATA).– ttbar (PYTHIA).– Z(bb)+X (PYTHIA).– Z(bb)+b (PYTHIA).
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Analysis OverviewAnalysis Overview
Signal hb(120)
Signal: at least 3 b-tagged jets Invariant mass of leading jets at mh
Backgrounds: “QCD heavy flavor” : bbjj, ccjj, cccc, bbcc, bbbb “QCD fakes” : jjjj “Other” : Z(bb,cc), tt
Kinematic cuts Cut on ET of leading jets Optimize for each Higgs mass
Look at the di-jet invariant mass of the leading ET jet combination. Search for an excess of events consistent with a Higgs signal shape.
Fitted Background
Data
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Background EstimationBackground Estimation
Full multi-jetdata sample
Double b-tagged
data sample
Calculate TRF
(ET & 3 Regions of )
Apply TRF
Triple b-tagged
background shape
Fit outside to real triple
b-tagged distribution
Tag Rate Function
Probability to b-tag a jet
Cross-check of backgroundestimation methods
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HF Correction to Tag Rate FunctionHF Correction to Tag Rate Function
Full multi-jetdata sample
Double b-tagged
data sample
Calculate TRF
(ET & 3 Regions of )
Fit with sum of the
backgrounds
Assuming only light jets, no HF
HF Normalizatio
n
Calculate HF corrected
TRF(ET & 3 Regions of
)
After the HF correction.
bbjj, bbbb, ttbar, zb,
jjjj (fakes from DATA)
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Efficiency and Tag Rate functionsEfficiency and Tag Rate functions
SVT Loose
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Double b-tagDouble b-tag Cross-check performed on double b-tagged events
ALPGEN bbj+bbjj samples Tests:
TRF parameterization and background estimation methods Trigger modelling b-tagging efficiency and kinematic bias Jet reconstruction efficiency and kinematic bias
Before HF correction
After HF correction
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Double b-tag Mbb and Triple b-tagDouble b-tag Mbb and Triple b-tag
The 3-b background is also estimated using the MC and compared as a cross-check.
Requiring the two jets to be b-tagged (Mbb) in double b-tagged events.
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Triple b-tag background fitTriple b-tag background fit
At the 95 % exclusion For tan = 100
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SystematicsSystematics Acceptance uncertainties:
Higgs pT spectra (theory) Trigger efficiency Di-jet mass resolution Jet reco/ID efficiency Jet energy scale uncertainty b-tagging efficiency
Background uncertainties:(roughly independent of mass)
Quality of the Tag Rate Function parameterizations (χ2) Statistics of the 3 b-tagged data outside the signal region
Totals:
Signal ~ 20% Background ~ 3%
IN Parcentage %
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Exclusion Plot with 260pbExclusion Plot with 260pb-1 -1 datadata Exclusion limits are calculated using ROOT’s TLimit CL S = CL S+B / CL B
Sweep through tan, given mAMeasured rate
Tree Level Assumption
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Future ProjectionsFuture Projections Using the expected (mA, tan) limit, future projections with
higher Int. Lumi are made.
Sensitivity to tan
down to ~40 for
mA=100GeV with
4fb-1 data and
with the current
assumptions and
performances.
Int Lumi in pb-1
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Summary and OutlookSummary and Outlook Improvements since Moriond 2004 result:
Better theoretical understanding.Twice the amount of data.Better b-tagging.
We believe we have the (almost) state of the art phenomenological interpretation of our measurements.
Future:b-tag combinationNeural Network