Recent Results in Susy Higgs Searches at DØ Jonathan Hays On behalf of the DØ Collaboration Fermilab Joint Experimental-Theoretical Seminar Friday, 12

Recent Results in Susy Higgs

Searches at DØJonathan Hays

On behalf of the DØ Collaboration

Fermilab Joint Experimental-Theoretical Seminar

Friday, 12th November 2010

Wine and Cheese Seminar 2

Outline

Introduction

Searches for Higgs + b-jets

tau final states (b)

b-jet final states (bbb)

Conclusions and Outlook


Tevatron

5.2 fb-1

4.3 fb-1

Tevatron and the detectors continue to perform very well

~16 fb-1 expected by Oct 2014Over 9.6 fb-1 delivered


D-Zero


Standard Model

Highly successful theory but:

No dark matter candidateNo gravityHierarchy and naturalness

problemsNo unification


Supersymmetry

Solves naturalness problemLSP = dark matter candidate ?SupergravityGUT unification possible

hep-ph/9709356

Introduce new space-time symmetry between fermions and bosons


MSSM Higgs Sector2 Higgs doublets

5 physical scalars: 3 neutral: h, H, A

2 charged: H±

A

hH

Totaltan()=30

MSSM

A. Djouadi, hep-ph:0810-2439

tree level two parameters: mA and tanβ

σMSSM ~ 2×Br×tan2β×σSM

Chance of discovery before SM sensitivity!

Radiative corrections large brings in dependence on other model parameters


MSSM Higgs

→ tt

bf → 3b/b tt

bbf→ 4b/bbtt

Enhancement to “down-type” fermions

BR(φ→bb) ~ 90%BR(φ→) ~ 10%

φ→clean signatures but low BR

bφ→breduced backgrounds added sensitivity at low mA

bφ→bbblarge backgroundhigh BR

φ = (h,H or A)


MSSM Higgs

→ tt

bf → 3b/b tt

bbf→ 4b/bbtt

Enhancement to “down-type” fermions

BR(φ→bb) ~ 90%BR(φ→) ~ 10%

φ→clean signatures but low BR

bφ→breduced backgrounds added sensitivity at low mA

bφ→bbblarge backgroundhigh BR


Inclusive Searches

→ tt

φ→

φ→


Inclusive Searches

http://arxiv.org/abs/1003.3363v3

φ→

Tevatron combination

http://arxiv.org/abs/1003.3363v3


Exclusive SearchesPublished results from DØ

bφ→bbφ→bbb

Phys. Rev. Lett. 104, 151801 (2010) Phys. Rev. Lett. 101, 221802 (2008)


Search Strategy

Optimise analysis based on expected limits with full systematics

In absence of significant discrepancy between data and background:Set limits in (almost) model independent waySet limits in benchmark SUSY scenarios

Combine results across channels for particular model choices


Signal Modelling

Reweighted in pt and eta of spectator b-jet based on MCFM calculation

Important differences in kinematics when moving from LO to NLO

Use 5 flavour number scheme:

Generate gb→bh at LO in PYTHIAAcceptance cuts on the spectator b-jet


Signal Modelling

Large enhancements to the couplings give large widths

Simulate widths using “narrow” samples and convoluting with Breit-Wigner

Radiative corrections have significant effect

Larger effect for bbb channelsLess significant for bττ


b-jet identification

Several mature algorithms used:3 main categories:

- Soft-lepton tagging- Impact Parameter based- Secondary Vertex reconstruction


b-jet identificationMeasure b & c efficiencies on b-jet enriched sample

Fake rate measured on multijet sample

Composition estimated from secondary vertex mass templates

MC and data differences

Data MC

b-tagged samples

Direct taggingReweightwith TRFs

Tag rate functions (TRF) parameterise efficiencies and fake rates versus pt and eta

NIM A 620, 490 (2010)


-lepton identification

TRK CAL

Type 1

o

no TRK, but EM sub-cluster

TRK CAL

Type 2

³ 1 TRK

wide CAL cluster

Type 3

Hadronic decays categorised by decay mode

Leptonic decays – single isolated leptons

Neural network (NN) trained for each type to discriminate against jets

Efficiencies measured in clean Z sample


Searches in tau final states4.3 fb-1 integrated luminosityCollected with single muon trigger

Dominant backgrounds:Z→ + jetstop pairsmulti-jet (QCD + W+jets)

Event selection: Single isolated muon Opposite sign had

1 loose b-tagged jet ( ε ~ 71%)

PreselectionNo b-tag

Complementary to φ→ and bφ→bbb

bφ→bτµτhad


Searches in tau final statesTrain NNs to discriminate against top and multi-jet backgrounds

Final discriminant = geometric mean of 3 NN outputs

NN b-tagger suppresses Z+jets background

Combine all NNs into single discriminant


bφ→bμhad limits

Tree level limit

Most stringent limit at low MA

4.3 fb-1 preliminary results

Limits set using “CLs” method


Searches with b-quarks

5x more dataExtended mass range: 90-300 GeVLarger MC samples

New result with 5.2fb-1 data

Submitted to Phys. Lett. B arxiv.org:1011.1931

Expanded and improved treatment of systematics

- e.g. b-taggingRe-analyzed old 1fb-1 data set

Major improvements since previous 1fb-1 publication


Searches with b-quarks

Very large multi-jet background

Challenging to model → data driven method

Multijet cross sections not well predicted → float normalisation

bf → 3b/b tt

3 or 4 jets, 3 must be b-tagged

5.2fb-1 collected with jet triggers – making use of lifetime information

Kinematic likelihood (D) used to select best jet pairing, + cut to suppress background


Background Modelling

MC correctionfactor

2 b-tagdata

3 b-tagbackground

Predict background shape from 2-tagged data with correction from MC

Add plot here...

2D correction: likelihood vs invariant mass


Background Modelling:Sample composition

In 3-tag samplebbb ~ 47% bbj ~ 32%bbc ~ 17%ccj ~ 2%

Needed for MC correction factor

Estimated using MC fit to data over several b-tag operating points


Background Modelling

Validate modelling in a signal poor region

“wrong” jet pair looks like background

Pick lowest likelihood pairing and select D < 0.12

Excellent agreement seen between model and data


Kinematic Likelihood

Trained on jet-pairings

Two likelihoods: low mass MA < 140 GeV high mass MA ≥ 140 GeV

In each event select pairing with highest LH value

Cut on LH optimised considering expected limits with full systematics

LH > 0.65 for all mass points


Kinematic Likelihood

Cut Cut

Projection of 2D distributions onto likelihood axis


Mass distributionsDi-jet invariant mass distribution used as input for the limit setting

D > 0.65, background normalised to data


Systematics

Background : normalisation included as nuisance parameter

Only consider variations in shape

Signal: dominated by b-tagging (15%-20%) and jet energy scale (2-14%) includes both rate and shape systematics


Systematics: Fake-rate

An area of major improvement since 1fb-1 result

remeasured on hbb specific samples

Detailed approach to systematics

b-tagging SF SVT Template fit

Sample composition

Fake rate determinationFa

ke ra

teFa

ke ra

te


Results


Results

Small excess ~ 2.5σAfter trials factor ~ 2.0 σ


SUSY Benchmark ScenariosFive additional parameters due to radiative correction

MSUSY (parameterizes squark, gaugino masses)

Xt (related to the trilinear coupling At → stop mixing)

M2 (gaugino mass term)

(Higgs mass parameter)Mgluino (comes in via loops)

Two common benchmarksMax-mixing - Higgs boson mass

mh close to max possible value

for a given tanNo-mixing - vanishing mixing in

stop sector → small mass for h


MSSM Scenario Limits

μ>0 suppressed production x BR – only set limits for μ<0


OutlookStill large potential for improvements:

More data: 5 → 7+ fb-1

Improved b-tagging → 30% (bbb) yield

Improved analysis techniques e.g. Event based discriminants → 15-30% sensitivity


Outlook: CombinationsCombine within channels – D0 + CDF – can be done in roughly model independent way

Combine across channels – generally requires picking a model

Aim for new D0 combination by Moriond with up to ~7fb-1

Preparations for Tevatron combination also underway

φ→

(φ→) + (bφ→b) + (bφ→bbb)


Outlook: SM Contributions?

eg P. Draper et al. arXiv:0905.4721v2

Interpret SM limits within MSSM

Real potential to probe large region of MSSM Higgs parameter space


ConclusionsInteresting time to be doing Higgs searches at the Tevatron!

Large data sets + sensitive analyses = discovery potential!


Backup slides


Mass distribution

Background normalised to data-signal

(S+B = D)

3-jet channels


?


Limit Setting

Use modified frequentist method “CLs”

Test statistic: negative poisson log likelihood ratio

Pseudo-experiments to extract likelihood distribution for B and S+B hypotheses

iiii

i

idi

bsbp

d

ppL

i

or

,!

)exp(

Systematics incorporated as Gaussian smearing in pseudo-experiments


LLR Distributions

Background likeSignal like

CLb CLs+b

Documents

Recent Results in Susy Higgs Searches at DØ Jonathan Hays On behalf of the DØ Collaboration Fermilab Joint Experimental-Theoretical Seminar Friday, 12