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

CDF

2/54

Aidan Robson Glasgow University

Limit-setting Analysis techniques Analysis stability Improving sensitivity

Motivation

Higgs WW

Low mass channels

CDF and Tevatron Combination

Outlook

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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

4/54


Higgs?WW scattering

W+

W–

Z/ W+

W–

W+

W–

W+

W–

Z/W+

W–

W+

W–

W+

W–

W+

W–

W+

W–

W+

W–

HH

required to cancel high-energy behaviour

5/54


Direct searches / Indirect limits

e+

e–

Z

H

Z b

bmH>114GeV mH<160GeV

6/54


Tevatron

proton–antiproton

√s = 1.96TeV

7/54


CDF

proton–antiproton

√s = 1.96TeV

= 1.0= 1.0

= 0.6= 0.6

= 2.0= 2.0

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Decay

Br.

Br

Production

mH/GeV

/ fb

mH/GeV

ggH

qqWH

qqqqH

qqZHbbH

gg,qqttH

Br

q

q’

W,Z

H ’

9/54


A physics programme

In last year

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

10/54


Higgs WW

H0W+

W–

11/54


Hadronic Ws?

WW ljj

Signal + background

Background only!

W

W

W

W

l

q

q’

l

q

q’

Leptonic final statesfor our Higgs search

12/54


HWW

H0W+

l+

W–l–

ee, e, ; ET

Isolation

mll > 16

Z and topsuppression

Dilepton sample composition

Dre

ll-Y

ando

min

ated

WW

dom

inat

ed

Hig

gs e

nhan

ced

B

Z Z

tt ttWW WW WW WW

H H HH

signalseparation

H0W+

l+

W–l–

W+

W–

q

q’

q

q’

90%

10%

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Limit settingIsolation

mll > 16 or 25Z and topsuppression

signalseparation

Background

Higgs signal x 10

eve

nts

X

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

xpec

ted

ou

tcom

es

95% CL Limit/SM

Median = expected limit

H (pb)

95%

H/SM

95%

0 20 1 2

rescalePD

F

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Limit setting (2)

95%

CL

Lim

it /

SM

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

median 12

illustrative

mH=160

15/54


What have we found?95

% C

L L

imit

/ S

M

mH / GeV mH / GeV

expected limitobserved limit

illustrative


illustrative

Deficit Excess

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Analysis overview

Spin structure WW vs H->WW lepton

NNscore

0 1

var1

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

HW–

W+l –

l+

ex

tend

sen

stiv

ityextend senstivity

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

17/54


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

HWWllWWllZZllW+partonl+jetWl+

ET modellepton energy resn

px

py

pz

lep1

LO |M|2 :px

py

pz

lep2

Ex , Eyparton lepton fake rate conversion rate

xobs:

(with true values y)

Compute likelihood ratio discriminator

R =Ps

Ps + kbiPb

i

i

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)

18/54


Neural network method

NNscore

0 1

var1

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,

x10

Pass signal/all backgrounds through net Form templates

NN

0 1

Pass templates and data to fitter

ET

ET

mll

Elep1

Elep2

ETsigData

HWWWWDYWgWZZZt t

fakes

ETjet1

Rleptons

leptons

ET lep or jet

ETjet2

Njets

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

19/54


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...

20/54


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!

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Grounding in SM measurements

TCE

TC

ETC

E P

HX

PH

X P

HX

TCE

CM

UP

TCE

CM

X

TCE

CM

IOC

ES

TCE

CM

IOP

ES

PH

X C

MU

PP

HX

CM

X

PH

X C

MIO

PE

S

PH

X C

MIO

CE

S

Neural net: Matrix element:

Different subdetector combinations

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ME Input Variables

leading lepton px subleading lepton px

Ex Ey

ET sin( ET ,nearest lep or jet)

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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

tra

inin

g e

sti

ma

tor

tra

inin

g e

sti

ma

tor

control regions…

24/54


Complementarity

Redefine discriminant for WW hypothesis:

R’ =PWW

PWW + kbiPb

i

i

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

25/54


NN same-sign

After DYnet cut

WW NN score WW NN scoreWW NN score

W

26/54


NN correlations

Data

WWHiggs

ET :mll

ETsubleading lepton

:mll

ET :mll

ET :ET

27/54


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

Lepton coverage

electrons muons

28/54


Luminosity

QuickTime™ and aTIFF (LZW) decompressor

are needed to see this picture.

Inst luminosity (1030)

(n

b)

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

To

tal

lum

ino

sity

(p

b–1

)

4fb–1 delivered

CDF: 3.3fb–1 to tape

Store number

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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

30/54


CDF H WW

expected signalevents

Current result is NN trained on kinematic + ME likelihood variables

31/54


CDF limit

1.6

2.5

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CDF limit development

Oct 05

Jan 07Mar 07

Aug 07

Expected limits

Feb 08

2.8

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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

E2lep

E2lep

NjetET

lep

ETjet

ET

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

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Ongoing improvements

W+

W–

VBF

W+

W–

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

W

+ Increase WW analysissensitivity at lower masses

factor 1.5in sensitivity?

Sensitivity to low mH from low mll

35/54


Low mass channels

q

q’

W,Z

H

36/54


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…

37/54


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

q’

Z

H

Z

l

l

b

b

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WHlbb

25% improvement

10% gain

q

q’

W

H

W

l

b

b

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)

39/54


WHlbbq

q’

W

H

W

l

b

b

ME technique from single top search

40/54


VHETbb



ET > 70GeVOne tight or two loose tagsveto leptons

q

q’

W/Z

H

W/Z

(l)

b

b

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

1 tight tag 2 loose tags

41/54


VHETbb

q

q’

W/Z

H

W/Z

(l)

b

b

Track-based discriminant for removing QCD background

double tight tagone tight tag + one jet probability tag

42/54


VHqqbb4 jets2 tags

ET trigger

SMH=120

/B

5 / 20000



data-driven bg

Tag rate fractionmeasured as fnET, ntracks, mbb,Rjj



mbb

mqq

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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

0 100

44/54


CDF and Tevatron Combinations

45/54


1.62.6

46/54


Current limit

47/54


Current limit

48/54




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

To

tal

lum

ino

sity

(p

b–1

)

4fb–1 delivered

CDF: 3.3fb–1 to tape

Store number

49/54


Achievable Sensitivity

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

CDF+D0 combined- curves are sqrt(L)

95%

CL

50/54


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

51/54


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

Mis

tag

rat

e Tagging Efficiency

Standard secondary vertex

b-tagging

52/54


Achievable Sensitivity

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

CDF+D0 combined- curves are sqrt(L)

95%

CL

115 GeV

53/54


Exclusion region grows

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

7.0

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

5.5

54/54


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.

2008

2009

2008

March 2007May 2008

2010

55/54


Jets

W/Z

HiggsSusy

bottom-quark

top-quark

56/54


Backup...

57/54


58/54


59/54




60/54


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.

61/54


CDF H WW

expected signalevents

62/54


Systematics: CDF HWW



%

63/54


Systematics: D0 HWW

64/54


Systematics: WHWWW

65/54


Neural network method TMVA

DYNN

score

0 1

var1

var2var n

WWNN

score

0 1

var1

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,

DYNN

x3 x30

0 1

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

WWNN

0 1 Fit templates

ET

ET

mll

Elep1

Elep2

ETsig

DataHWW

WWDYWgWZZZt t

fakes

ETjet1

Rleptons

leptons

ET lep or jet

ETjet2

Njets

66/54


Achievable sensitivity (CDF only)

67/54


NN: low-mass, low mll


68/54


Sensitivity at low mass

ee

e

mH=130 160 200

WW NN score

Low mass: W understanding becomes key

W

69/54


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

ee

mH=130 mH=160

W+ Increase WW analysissensitivity at lower masses

70/54


Selection

B

Z Z

t t

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

71/54


Selection

Matrix element Neural net

Using extended lepton coveragefrom WZ observation

Using standard leptons



Zee

(Met,nearest l or j)

Me

t


Me

t


HWW 160

Me

t

WW

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

Leptons:

“Metspecial”:(Matrix element)

Avoid Met pointing along lepton or jet direction

72/54


Tevatron

proton–antiproton

√s = 1.96TeV



73/54


CDF

= 1.0= 1.0

= 0.6= 0.6

= 2.0= 2.0muonchambers=

2= 3

0 1 2 3 m

2

1

0

tracker had cal

hadronic calEM cal

had cal

solenoid

pre-radiator shower max

silicon

EM cal

= 1

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

74/54


CDF muon patchwork

75/54


Production Decay

mH/GeV

Br

Br( W l) ≈ 0.32 Br( W jj ) ≈ 0.68

H0W+

W–

/ fb

mH/GeV

ggH

qqWH

qqqqH

qqZHbbH

gg,qqttH

76/54


Precision EWK fits

1-sided 95%CL upper limit 144 GeV

increases to 182 GeV including LEP direct exclusion to 114GeV

LEPEWWG, July 2007

77/54


ME Input Variables

leading lepton px subleading lepton px

Ex Ey

ET sin( ET ,nearest lep or jet)

78/54


Higgs yields

Matrix element analysis1/fb

Neural net analysis1/fb

Matrix element analysis2/fb

79/54


ME same sign

80/54


ME Likelihood Ratio

81/54


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

82/54


Systematics

Common uncertainties treated as nuisance parameters:

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

Shape uncertainties:

83/54


NN: low-mass, low mll


84/54


D0 HWW (e/)

e

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

W+

W–

VBF

includes VBF

W+

W–ggH

85/54


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

W+

W–

VH VWWl

l /

86/54


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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