Status Report on hbb Analysis Jyothsna Rani for the hbb group Andy, Avto, Marine, Tim, Boris All D0...

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Status Status Report Report on hbb Analysis on hbb Analysis Jyothsna Rani Jyothsna Rani for the hbb group for the hbb group Andy, Avto, Marine, Tim, Boris Andy, Avto, Marine, Tim, Boris All D0 Meeting All D0 Meeting 28 28 th th January 2005 January 2005
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Transcript of Status Report on hbb Analysis Jyothsna Rani for the hbb group Andy, Avto, Marine, Tim, Boris All D0...

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 2

OutlineOutline

Introduction Higgs production Signal kinematics Analysis overview Limits and Exclusion plots Summary & Outlook

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 3

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 4

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 5

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 6

Neutral MSSM Higgs Branching RatioNeutral MSSM Higgs Branching Ratio

CP odd

Higgs

CP even

Higgs

tan = 5

tan = 5

tan = 40

tan = 40

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 7

Masses and WidthsMasses and Widths

Using M. Spira’s HDECAY 3.101, tan = 30

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 8

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 9

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 10

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 11

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.

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 12

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.

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 13

Event Selection - OfflineEvent Selection - OfflineLoose initial analysis cuts

Optimized analysis cuts for each signal mass

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 14

Jet pT and distributions

distributionJES corrected pT

In Data after the Kinematical cuts.

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 15

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).

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 16

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 17

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 18

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)

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 19

Efficiency and Tag Rate functionsEfficiency and Tag Rate functions

SVT Loose

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 20

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 21

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.

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 22

Triple b-tag background fitTriple b-tag background fit

At the 95 % exclusion For tan = 100

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 23

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 %

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 24

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 25

Exclusion PlotExclusion PlotTree Level Assumption

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 26

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

ADM , 28th Jan 2005 K. Jyothsna Rani -- TIFR 27

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