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

Closing in on the Higgs Boson at CDF

Zurich, 28 May 2008

Aidan RobsonGlasgow University

Aidan Robson Glasgow University

Limit-setting Analysis techniques Analysis stability Improving sensitivity

Motivation

Higgs WW

Low mass channels

CDF and Tevatron Combination

Outlook

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 massand can write lepton mass terms

We are left with one dynamical field single neutral scalar particle

Higgs?WW scattering

required to cancel high-energy behaviour

Direct searches / Indirect limits

bmH>114GeV mH<160GeV

Tevatron

proton–antiproton

√s = 1.96TeV

proton–antiproton

√s = 1.96TeV

= 1.0= 1.0

= 0.6= 0.6

= 2.0= 2.0

Production

mH/GeV

qqZHbbH

gg,qqttH

A physics programme

In last year

(ppZZ) 3 x(ppH)(mH=160)

Higgs WW

Hadronic Ws?

WW ljj

Signal + background

Background only!

Leptonic final statesfor our Higgs search

W–l–

ee, e, ; ET

Isolation

mll > 16

Z and topsuppression

Dilepton sample composition

tt ttWW WW WW WW

H H HH

signalseparation

W–l–

Limit settingIsolation

mll > 16 or 25Z and topsuppression

signalseparation

Background

Higgs signal x 10

X = some observable

H1=SM+Higgs (of mass mH)H0=SM only

Construct test statistic Q = P(data|H1)/P(data|H0) –2lnQ = 2(data|H1) – 2(data|H0) , marginalized over nuisance params except H

Find 95th 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”E

95% CL Limit/SM

Median = expected limit

H (pb)

0 20 1 2

rescalePD

Limit setting (2)

mH / GeVRepeat for different values of mH build up exclusion plot

median 12

illustrative

mH=160

What have we found?95

mH / GeV mH / GeV

expected limitobserved limit

illustrative

Deficit Excess

Analysis overview

Spin structure WW vs H->WW lepton

NNscore

var2var n

Cut-based analysis

Neural net approach Matrix element likelihood approach

d ∫ ƒa(x,Q2) ƒb(x,Q2) |Mab(4;)|2 …

1. sequential combination2. understanding relationship at a deeper level

W+l –

ityextend senstivity

Looking for some final distribution to which to fit templates and from which to extract limits

Matrix element method Use LO matrix element (MCFM) to compute event probability

HWWllWWllZZllW+partonl+jetWl+

ET modellepton energy resn

LO |M|2 :px

Ex , Eyparton lepton fake rate conversion rate

(with true values y)

Compute likelihood ratio discriminator

Ps + kbiPb

kb is relative fraction of expected background contrib.Ps computed for each mH

Fit templates (separately for high S/B and low S/B dilepton types)

Neural network method

NNscore

var2var n

Background Higgs

Various versions. Current: Apply preselection (eg ET to remove Drell-Yan) Train on {all backgrounds / WW} against Higgs mH=110,120…160…200 { possibly separate ee,e,

Pass signal/all backgrounds through net Form templates

Pass templates and data to fitter

ETsigData

HWWWWDYWgWZZZt t

ETjet1

Rleptons

leptons

ET lep or jet

ETjet2

Most recent CDF“combined ME/NN” analysis also uses ME LRs as NN input variables

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...

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...

mH / GeV

illustrative

Excess in one of the 3 analyses!

Grounding in SM measurements

Neural net: Matrix element:

Different subdetector combinations

ME Input Variables

leading lepton px subleading lepton px

ET sin( ET ,nearest lep or jet)

Stability

convergence

Assessing NN stability

rogue variables – had checked data-simulation agreement in as many regions as possible

Drell Yan-rich WW-rich

MetMet– applicability?

training epoch training epoch

successful training unsuccessful training

control regions…

Complementarity

Redefine discriminant for WW hypothesis:

R’ =PWW

PWW + kbiPb

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

NN same-sign

After DYnet cut

WW NN score WW NN scoreWW NN score

NN correlations

WWHiggs

ET :mll

ETsubleading lepton

ET :mll

ET :ET

Increased acceptance by adding plug calorimeter (no tracking) and tracks pointing to cracks

Lepton coverage

electrons muons

Luminosity

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Inst luminosity (1030)

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

Year2002 2003 2004 2005 2006 2007 2008

4fb–1

3fb–1

2fb–1

4fb–1 delivered

CDF: 3.3fb–1 to tape

Store number

Systematics

JESLepton Energy ScaleISRPDFFakes

Shape uncertainties(neural net):

40 weights stored per event(Higgs and all backgrounds!)

(neural net)

– and similar for matrix element analysis

CDF H WW

expected signalevents

Current result is NN trained on kinematic + ME likelihood variables

CDF limit

CDF limit development

Oct 05

Jan 07Mar 07

Aug 07

Expected limits

Feb 08

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 pT) – saw ~equivalence

More advanced attempt:swapping between kinematic 4-vector inputs, and ME likelihood inputs– excellent equivalence

using all Rleptons

ET variableLRHWW

using kinematic only Rleptons

ET variableHT

NjetET

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

Ongoing improvements

VH VWWl

l / Other channels:

2-jet bin Hadronic taus Inclusion of more triggers Improvement of S/B through lepton selection

ee mH=130 mH=160

+ Increase WW analysissensitivity at lower masses

factor 1.5in sensitivity?

Sensitivity to low mH from low mll

Low mass channels

b-tagging

Secondary vertex-finding algorithm

Attempt to fit tracks to decay vertex

Jet probability

Compares track impact parameters to measured resolution functions

Neural network filters

ntracks in secondary vertexpT fraction carried by those tracksgoodness of vertex fitvertex masstransverse decay length & significance…

ZHllbb

Double tag

One tight or two loose b-tagsZ from ee or

ANN2 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 tagq

25% improvement

10% gain

double tight tag

one tight tagwith NN tag

17% gain

Three categories: double tight tag one tight tag + one jet probability tag one tight tag with NN tagANN to improve signal discriminationAdd isolated tracks

mHNN output (120)

WHlbbq

ME technique from single top search

VHETbb

ET > 70GeVOne tight or two loose tagsveto leptons

mjj (GeV/c2) mjj (GeV/c2)

1 tight tag 2 loose tags

VHETbb

Track-based discriminant for removing QCD background

double tight tagone tight tag + one jet probability tag

VHqqbb4 jets2 tags

ET trigger

SMH=120

5 / 20000

data-driven bg

Tag rate fractionmeasured as fnET, ntracks, mbb,Rjj

lephad: Br 45%

leptonhadronic tau (1&3prong)2 jets

opposite charge

3 ANN: sig vs: Z, tt, QCDcombined for fit

Alpgen Z+jetsMT(lep,ET) / GeV/c2

CDF and Tevatron Combinations

1.62.6

Current limit

1 Oct 07

1 Oct 09

3.3 fb-1

4.5 fb-1

LuminosityYear2002 2003 2004 2005 2006 2007 2008

4fb–1

3fb–1

2fb–1

4fb–1 delivered

CDF: 3.3fb–1 to tape

Store number

Achievable Sensitivity

Sensitivity factors Minimum = x1.5Further = x2.25

CDF+D0 combined- curves are sqrt(L)

Improving sensitivity: low mH

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

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

All improvements validated on analysis/studies with real data/tools

Additional x1.5 avg. sensitivity improvement for all analysis

e Tagging Efficiency

Standard secondary vertex

b-tagging

Achievable Sensitivity

Sensitivity factors Minimum = x1.5Further = x2.25

CDF+D0 combined- curves are sqrt(L)

115 GeV

Exclusion region grows

With 7 fb-1 • exclude all masses (except real mass)• 3 150:170

With 5.5 fb-1 tougher:• Exclude 140:180 range• 3 in one point: 160

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 140-170 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 2009. Perhaps SM fit narrowing on this range.

• Higgs is 130-150 OR 170-185. Perhaps SM Fit excludes upper range.

Tev 2009: 3at 116: ATLAS 2011? for discovery. CDF keeps running!?LHC 2010: 10fb-1 : Discover it for > 130.

March 2007May 2008

HiggsSusy

bottom-quark

top-quark

Backup...

Do we have to get lucky?

“further” @ 115 GeV

7 fb-1 => 70% experiments w/230% experiments w/3

“further” @ 160 GeV

7 fb-1 => 95% experiments w/275% experiments w/ 3

Solid lines = 2.25 improvementDash lines = 1.50 improvement

Analyzed Lum. Analyzed Lum.

CDF H WW

expected signalevents

Systematics: CDF HWW

Systematics: D0 HWW

Systematics: WHWWW

Neural network method TMVA

var2var n

var2var nDY Higgs WW Higgs

Use TMVA neural nets twice Train on Drell-Yan and Higgs mH=160 ee,e,

Train on WW and Higgs mH=110,120…160…200; ee,e,

x3 x30

Pass signal/data/background through DY–H net Cut Pass remaining events through WW–H net

0 1 Fit templates

DataHWW

WWDYWgWZZZt t

ETjet1

Rleptons

leptons

ET lep or jet

ETjet2

Achievable sensitivity (CDF only)

NN: low-mass, low mll

Sensitivity at low mass

mH=130 160 200

WW NN score

Low mass: W understanding becomes key

Improving sensitivity: high mH

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% VHVWW and VBF (jj in final state)• Expect good S/B• Ongoing studies

– 10-15% more triggers (existing triggers)+ more leptons

Improvements from x1.5 to x2 in sensitivityAll improvements validated on analysis/studies with real data/tools

mH=130 mH=160

W+ Increase WW analysissensitivity at lower masses

Selection

WW WW WW WWH H H H

Matrix Element

mll>25 GeV

Metspecial

N jets<=1

Matrix element discriminator

Neural Net

mll>16 GeV

N jets(ET>15)=0 || N jets(ET<55)=1 || N jets(ET<40)=2

DY–Higgs neural net

WW–Higgs neural net

Isolation20GeV/10GeV

>25(ee,)>15(e)

Cosmic rejectionOpposite sign

Selection

Matrix element Neural net

Using extended lepton coveragefrom WZ observation

Using standard leptons

(Met,nearest l or j)

HWW 160

Metspecial = Met x sin(Met) Met (if Met > 900)

Leptons:

“Metspecial”:(Matrix element)

Avoid Met pointing along lepton or jet direction

Tevatron

proton–antiproton

√s = 1.96TeV

= 1.0= 1.0

= 0.6= 0.6

= 2.0= 2.0muonchambers=

0 1 2 3 m

tracker had cal

hadronic calEM cal

had cal

solenoid

pre-radiator shower max

silicon

EM cal

Drift chamber to ||<1Further tracking from SiCalorimeter to ||<3Muon system to ||<1.5

CDF muon patchwork

Production Decay

mH/GeV

Br( W l) ≈ 0.32 Br( W jj ) ≈ 0.68

mH/GeV

qqZHbbH

gg,qqttH

Precision EWK fits

1-sided 95%CL upper limit 144 GeV

increases to 182 GeV including LEP direct exclusion to 114GeV

LEPEWWG, July 2007

ME Input Variables

leading lepton px subleading lepton px

ET sin( ET ,nearest lep or jet)

Higgs yields

Matrix element analysis1/fb

Neural net analysis1/fb

Matrix element analysis2/fb

ME same sign

ME Likelihood Ratio

Systematics

JESLepton Energy ScaleISRPDFFakes

Shape uncertainties(neural net):

40 weights stored per event(Higgs and all backgrounds!)

(neural net)

– and similar for matrix element analysis

Systematics

Common uncertainties treated as nuisance parameters:

LuminosityTrack isolationHiggs: s, NNLOWW: Jet veto, PDF/Q2, generatorDY: Met modeling, low mass modeling

Shape uncertainties:

NN: low-mass, low mll

D0 HWW (e/)

ee, e channels 1.1fb–1 ; channel 1.7fb–1

includes VBF

W–ggH

D0: Other channels HWW had channel 1fb–1

– select using neural net – event likelihoods to separate signal (not currently contributing to overall limit)

VHVWW 1.1fb–1 search for ll’+X (like-sign dileptons) 2d likelihood: physics/instrumental backgrounds

VH VWWl

mH (GeV) 120 140 160 180 200

Median /SM 22.2 6.7 2.8 4.4 9.7Observed /SM 47.3 12.0 2.4 4.7 11.1

D0 RunIIa+b Preliminary

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

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