Higgs Boson Searches at the Tevatron Harald Fox Department of - - PowerPoint PPT Presentation

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Higgs Boson Searches at the Tevatron Harald Fox Department of - - PowerPoint PPT Presentation

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Higgs Boson Searches at the Tevatron Harald Fox Department of Physics h.fox@lancaster.ac.uk Contents evatron, D and CDF T Higgs production Search for heavy Higgs H WW ll b jet Search for light Higgs WH l bb ZH

slide-1
SLIDE 1

Higgs Boson Searches at the Tevatron

Harald Fox Department of Physics h.fox@lancaster.ac.uk

slide-2
SLIDE 2

Contents

b jet b jet

T evatron, DØ and CDF Higgs production Search for heavy Higgs H → WW → llνν Search for light Higgs WH → lνbb̅ ZH → ννbb̅ ZH → llbb̅ Outlook Conclusion

slide-3
SLIDE 3

The Standard Model

H

  • W −

W + W − W +

  • H
  • H

Kane, Scientific American, June 2003

slide-4
SLIDE 4

The Higgs Mechanism

  • The Higgs field acquires a

vacuum expectation value

  • Particles interact with the Higgs

field and acquire an effective mass

V(Ф)=μ2|Ф|+λ(|Ф|2)2

  • The mass relation between

γ, W and Z bosons is determined

  • Couplings and branching

ratios are determined. v =

  • −µ2

2λ = 246GeV

mγ = mW = 1 2vg mZ = 1 2vg 1 cos θW mH = √ 2λv2 mf = 1 √ 2gfv

slide-5
SLIDE 5

Constraints on the Higgs Mass

  • Excluded by LEP

1 2 3 4 5 6 100 30 300

mH [GeV] ∆χ2

Excluded

Preliminary

∆αhad = ∆α(5)

0.02758±0.00035 0.02749±0.00012

  • incl. low Q2 data

Theory uncertainty

mLimit = 144 GeV

mH < 144 GeV 95%C.L. LEP EWWG

Kolda, Murayama: JHEP 0007 (2000) 035

100 200 300 400 500 600 1 10 10

2

Higgs mass (GeV) (TeV)

Vacuum Stability Triviality Electroweak 10% 1%

Fine Tuning ΔFT < 10%/1%

slide-6
SLIDE 6

Tevatron

slide-7
SLIDE 7

Run IIa Run IIb

Delivered Recorded Run IIa 1.6 fb-1 1.3 fb-1 Run IIb (so far) 1.9 fb-1 1.7 fb-1 Total 3.5 fb-1 3.0 fb-1

2006 shutdown:

  • new Layer 0 silicon installed
  • trigger upgrades installed

April 02 Jan 08 Passed 3fb-1 milestone in recorded luminosity on 16 January 2008

slide-8
SLIDE 8

Two General Purpose Detectors: CDF DØ Electron acceptance |η|<2.0 |η|<3.0 Muon acceptance |η|<1.5 |η|<2.0 Silicon Precision tracking |η|<2.0 |η|<3.0 Hermetic Calorimeter |η|<3.6 |η|<4.2 Powerful trigger systems (2.5MHz →50Hz) Dilepton triggers with pT>4GeV

protons antiprotons 3 Layer Muon System Tracker Solenoid Magnet

slide-9
SLIDE 9

Tevatron Cross Sections

The Higgs cross section is 10-11 orders of magnitudes lower than the total inelastic cross section.

Evidence of single top production is an important milestone towards the Higgs boson. Light quarks are ubiquitous. Plenty of W and Z bosons → calibration. Total inelastic cross section.

slide-10
SLIDE 10

Higgs Production and Decay

cross section (pb)

slide-11
SLIDE 11

High Mass Higgs Channels

ℓ ℓ

ℓ ℓ ℓ ℓ

  • final states with charged leptons:
  • e±e∓
  • e±µ∓ ← counts twice
  • µ±µ∓
  • l±τ∓h ← difficult
  • hadronic final state:
  • very difficult

W+ e+ W- e- ν n

Angular correlation of leptons due to V−A as H is a spin 0 particle:

(e,e) φ ∆

0.5 1 1.5 2 2.5 3 3.5 4

entries

  • 1

10 1 10

2

10

3

10

4

10

5

10

(e,e) φ ∆

0.5 1 1.5 2 2.5 3 3.5 4

entries

  • 1

10 1 10

2

10

3

10

4

10

5

10

data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

slide-12
SLIDE 12

High Mass Higgs Channels

ℓ ℓ

ℓ ℓ ℓ ℓ

  • 2 leptons with high pT
  • Isolation of e/µ against QCD and b-jets
  • E̷T due to 2 neutrinos
  • E̷T significance:
  • not from mis-measured lepton pT
  • not from mis-measured jet pT
  • mll < mZ
  • Σjets pT < 100 against tt̅ background

MET / GeV 10 20 30 40 50 60 70 80 90 100

  • 2

10

  • 1

10 1 10

2

10

3

10

4

10 MET / GeV 10 20 30 40 50 60 70 80 90 100

  • 2

10

  • 1

10 1 10

2

10

3

10

4

10

=160

H

After Preselection, M

WZ t t QCD WW

  • µ
  • W

ZZ

  • Z

µ µ

  • Z

Data H160

S/B ≈ 15/300k

}(250-500)

/ GeV

µ µ

M 20 40 60 80 100 120 140 160 180 200 220 240

  • 2

10

  • 1

10 1 10

2

10 / GeV

µ µ

M 20 40 60 80 100 120 140 160 180 200 220 240

  • 2

10

  • 1

10 1 10

2

10

Before Cut

  • Z

QCD t t ZZ WZ WW

  • µ
  • W

µ µ

  • Z

Data H120

S/B ≈ 5/50

  • HT / GeV

50 100 150 200 250 300 350 400 450 500

  • 1

10 1 10 HT / GeV 50 100 150 200 250 300 350 400 450 500

  • 1

10 1 10

Before Cut

  • Z

QCD t t ZZ WZ µ µ

  • Z
  • µ
  • W

WW Data H160

  • )
2

µ ,

1

µ (

  • 0.5

1 1.5 2 2.5 3

  • 2

10

  • 1

10 1 10 )

2

µ ,

1

µ (

  • 0.5

1 1.5 2 2.5 3

  • 2

10

  • 1

10 1 10

=160

H

After HT Cut, M

  • Z

QCD t t ZZ WZ µ µ

  • Z
  • µ
  • W

WW Data H160

slide-13
SLIDE 13

Cuts Optimised for mH =120 - 200

Selection criterion mH = 120 mH = 140 mH = 160 mH = 180 mH = 200 Cut 1 Preselection Trigger, ID, leptons with opposite charge, zV T X < 60 cm, Mµµ > 17 GeV pT > 20/10GeV 20/15 25/15 25/15 25/15 Cut 2 Missing trans- verse energy E / T 25 < E / T < 70 25 < E / T < 80 30 < E / T < 90 35 < E / T < 100 35 < E / T < 110 Cut 3 Sig(E / T ) Sig(E / T ) > 5 (for NJet > 0) Cut 4 M T

min (l, E

/ T ) M T

min > 30

M T

min > 30

M T

min > 40

M T

min > 45

M T

min > 45

Cut 5 Invariant mass Mµµ 17 < Mµµ < 60 17 < Mµµ < 70 17 < Mµµ < 75 17 < Mµµ < 85 17 < Mµµ < 95 Cut 6 ΣpT = pl

T + pl T +

E / T 60 < ΣpT < 135 70 < ΣpT < 160 80 < ΣpT < 170 90 < ΣpT < 180 90 < ΣpT < 200 Cut 7 HT (scalar sum of pJet

T )

HT < 60 HT < 60 HT < 60 HT < 60 HT < 50 Info Neural Net NN > 0.5

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SLIDE 14

Neural Net

[GeV] inv M 20 40 60 80 100 120 140 160 180 200 entries
  • 1
10 1 10 2 10 3 10 4 10 [GeV] inv M 20 40 60 80 100 120 140 160 180 200 entries
  • 1
10 1 10 2 10 3 10 4 10 data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

[GeV] T miss E 20 40 60 80 100 120 140 160 180 200 entries
  • 1
10 1 10 2 10 3 10 4 10 [GeV] T miss E 20 40 60 80 100 120 140 160 180 200 entries
  • 1
10 1 10 2 10 3 10 4 10 data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

[GeV] T min M 20 40 60 80 100 120 entries
  • 1
10 1 10 2 10 3 10 4 10 [GeV] T min M 20 40 60 80 100 120 entries
  • 1
10 1 10 2 10 3 10 4 10 data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

(e,e) φ ∆ 0.5 1 1.5 2 2.5 3 3.5 4 entries
  • 1
10 1 10 2 10 3 10 4 10 5 10 (e,e) φ ∆ 0.5 1 1.5 2 2.5 3 3.5 4 entries
  • 1
10 1 10 2 10 3 10 4 10 5 10 data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

[GeV] T p

∑

50 100 150 200 250 300 entries
  • 1
10 1 10 2 10 3 10 4 10 [GeV] T p

∑

50 100 150 200 250 300 entries
  • 1
10 1 10 2 10 3 10 4 10 data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

NN

NN

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

entries

  • 1

10 1 10

2

10

3

10

NN

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

entries

  • 1

10 1 10

2

10

3

10 e+e−

L=1.2fb−1

DØ Run II Preliminary

NN Output ΣpT Mll E̷T MTmin (l,E̷T) Δϕll ≈30% improvement from NN

mH=160 x 10

data 10 × WW →

160

H

e e → Z Diboson γ W+jets/ QCD ttbar

slide-15
SLIDE 15

Neural Net

[GeV] inv M 20 40 60 80 100 120 140 160 180 200 entries
  • 1
10 1 10 2 10 3 10 4 10 [GeV] inv M 20 40 60 80 100 120 140 160 180 200 entries
  • 1
10 1 10 2 10 3 10 4 10 data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

[GeV] T miss E 20 40 60 80 100 120 140 160 180 200 entries
  • 1
10 1 10 2 10 3 10 4 10 [GeV] T miss E 20 40 60 80 100 120 140 160 180 200 entries
  • 1
10 1 10 2 10 3 10 4 10 data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

[GeV] T min M 20 40 60 80 100 120 entries
  • 1
10 1 10 2 10 3 10 4 10 [GeV] T min M 20 40 60 80 100 120 entries
  • 1
10 1 10 2 10 3 10 4 10 data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

(e,e) φ ∆ 0.5 1 1.5 2 2.5 3 3.5 4 entries
  • 1
10 1 10 2 10 3 10 4 10 5 10 (e,e) φ ∆ 0.5 1 1.5 2 2.5 3 3.5 4 entries
  • 1
10 1 10 2 10 3 10 4 10 5 10 data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

[GeV] T p

∑

50 100 150 200 250 300 entries
  • 1
10 1 10 2 10 3 10 4 10 [GeV] T p

∑

50 100 150 200 250 300 entries
  • 1
10 1 10 2 10 3 10 4 10 data 10 × WW → 160 H e e → Z Diboson γ W+jets/ QCD ttbar

e+e−

L=1.2fb−1

DØ Run II Preliminary

NN

NN

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

entries

  • 1

10 1 10

2

10

3

10

NN

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

entries

  • 1

10 1 10

2

10

3

10 e+e−

L=1.2fb−1

DØ Run II Preliminary

NN Output ΣpT Mll E̷T MTmin (l,E̷T) Δϕll

pt1 pt2 met dphiMetL1 dphiMetL2 M minMt dphiL1L2 type

slide-16
SLIDE 16

Multivariate Techniques :: TNG

  • Very loose selection
  • More variables (~10 → ~20)
  • T

rain against more backgrounds

nnout

  • 0.4
  • 0.2

0.2 0.4 0.6 0.8 1 1.2 1.4

  • 2

10

  • 1

10 1 10

2

10

Data (551) =160) h Signal (M Signal+Bkgd. Total Bkgd. Z+jets Bkgd. Diboson Bkgd. W+jets Bkgd. QCD Bkgd. Top Bkgd.

µ+µ−

L=1.2fb−1

DØ Run II Preliminary

NN Output

Improved NN

:

ME dscr.

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

entries

  • 1

10 1 10

2

10

3

10

ME dscr.

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

entries

  • 1

10 1 10

2

10

3

10 e+e−

L=1.2fb−1

DØ Run II Preliminary

ME discr.

Matrix Element Additional input to NN

slide-17
SLIDE 17

Combination of Channels

Run IIa Run IIb

NN Output

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Events

  • 1
10 1 10 Data Sum of Backgrounds ) 2 =160 GeV/c H Signal (m
  • 1
DØ Preliminary, L=1.10 fb , RunIIa ν , e ν e →
  • W
+ W → H (a)

NN Output

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Events

  • 1
10 1 10 2 10 Data Sum of Backgrounds ) 2 =160 GeV/c H Signal (m
  • 1
DØ Preliminary, L=1.10 fb , RunIIa ν µ , ν e →
  • W
+ W → H (b)

NN Output NN Output

0.2 0.4 0.6 0.8 1 1.2

Events

  • 1
10 1 10 2 10 Data Sum of Backgrounds ) 2 =160 GeV/c H Signal (m
  • 1
DØ Preliminary, L=1.20 fb , RunIIb ν , e ν e →
  • W
+ W → H (c)

NN Output

  • 0.2
0.2 0.4 0.6 0.8 1

Events

  • 1
10 1 10 2 10 Data Sum of Backgrounds ) 2 =160 GeV/c H Signal (m
  • 1
DØ Preliminary, L=1.20 fb , RunIIb ν µ , ν e →
  • W
+ W → H (d)

NN Output

0.2 0.4 0.6 0.8 1 1.2

Events

  • 1
10 1 10 2 10 Data Sum of Backgrounds ) 2 =160 GeV/c H Signal (m
  • 1
DØ Preliminary, L=1.10 fb , RunIIa ν µ , ν µ →
  • W
+ W → H

NN Output

  • 0.4
  • 0.2
0.2 0.4 0.6 0.8 1 1.2 1.4

Events

  • 1
10 1 10 2 10 3 10 Data Sum of Backgrounds ) 2 =160 GeV/c H Signal (m
  • 1
DØ Preliminary, L=1.20 fb , RunIIb ν µ , ν µ →
  • W
+ W → H (a) (b)

ee eμ μμ Combination of

  • 3 final states
  • 2 run periods

Uncertainties

  • Statistical
  • Correlated systematics
  • Bg cross sections (6-18%)
  • Normalisation (6%)
  • Jet energy scale
  • Uncorrelated (some channels)
  • Lepton ID & resolution(3-10%)

LEP CLs Method

NN Output NN Output NN Output

  • 0.2
0.2 0.4 0.6 0.8 1

Events

  • 1
10 1 10 2 10 Data Sum of Backgrounds ) 2 =160 GeV/c H Signal (m
  • 1

DØ Preliminary, L=1.20 fb , RunIIb ν µ , ν e →

  • W
+

W → H

(d)

NN Output
  • 0.2
0.2 0.4 0.6 0.8 1 Events 1 2 3 4 5 6 7 8 9 10 Background Prediction (stat only) Most Probable Shape B Only Hyp Most Probable Shape S+B Hyp =160GeV) H Signal (m Data
slide-18
SLIDE 18

Profile Likelihood

1-CLb CLsb LLR = −2 ln Q CLs = 1 − CLs+b CLb Q( s, b, d) =

NChan

  • i=0

Nbins

  • j=0

(s + b) dij

ij

e (s + b)ij dij! / b d

ij e b

dij! Systematics taken into account via Gaussian marginalisation Correlations taken into account

slide-19
SLIDE 19

CLs Technique

0.02 0.04 0.06 0.08 0.1 0.12 0.14

  • 15
  • 10
  • 5

5 10 15

  • 2 ln(Q)

Probability density

Observed Expected for background Expected for signal (mH=115.6 GeV/c2) + background

LEP

15

250 500 750 1000
  • 100 -80 -60 -40 -20
20 40 60 80 100 500 1000 1500
  • 40
  • 30
  • 20
  • 10
10 20 30 40 2000 4000
  • 10 -8
  • 6
  • 4
  • 2
2 4 6 8 10
  • 2ln(Q)

(a)

  • 2ln(Q)

(b)

  • 2ln(Q)

(c) Figure 1. Left: The pdfs of the combined Higgs search at LEP for the background (right) and signal + background hypotheses (left) for mH = 115.6 GeV/c2. The light grey region to the left

  • f the observation is 1 − CLb and the dark grey region to the right of the observation is CLs+b.

Right: Illustration of the evolution of the pdfs with falling search sensitivity from (a) to (c) as the Higgs mass hypothesis is increased and the production cross-section falls.

  • A. L. Read, J. Phys. G: Nucl. Part. Phys. 28 (2002) 2693-2704
slide-20
SLIDE 20

H→WW DØ Combination

200

)

2

(GeV/c

H

m

120 130 140 150 160 170 180 190 200

LLR

  • 3
  • 2
  • 1

1 2 3 4

σ 2-

b

LLR σ 1-

b

LLR

b

LLR

s+b

LLR

  • bs

LLR

  • 1

DØ Preliminary, L=2.3 fb

  • W

+

W → H

LLR

)

2

(GeV/c m

120 130 140 150 160 170 180 190 200

)

  • W

+

W → BR(H × H) → p (p σ Limit /

1 10

  • 1

DØ Preliminary, L=2.3 fb

  • W

+

W → H

Observed Limit Expected Limit σ 1- ± Expected σ 2- ± Expected

Standard Model = 1.0

LLR

  • bs: 2.1

exp: 2.4

Limits relative to SM expectation!

slide-21
SLIDE 21

DØ Final States

)

2

(GeV/c

H

m 110 120 130 140 150 160 170 180 190 200 (SM) ! 95% C.L. Limit / 1 10

2

10

  • 1

: 1.1 fb b llb " ZH

  • 1

: 2.1 fb b b # # " ZH

  • 1

: 1.7 fb b b # l " WH

  • 1

: 1.1 fb

  • W

+

W W " WH

  • 1

: 2.3 fb

  • W

+

W " H

  • 1

: 2.3 fb $ $ " H DZero Combination

Standard Model = 1.0

  • 1

DØ Preliminary, L=1.1-2.3 fb (SM) ! 95% C.L. Expected Limit /

slide-22
SLIDE 22

H → WW @ CDF

signal ee eµ µµ etrk µtrk

signal

electrons muons

  • L. Ž. High Mass Higgs at Tevatron

signal NN output

signal

s Higgs at Tevatron 1

  • bs: 1.6

exp: 2.4

slide-23
SLIDE 23

Low Mass Higgs Channels

ZH → l+l- bb 2 b jets ~ 1/2 MH each 2 leptons ~ 45 GeV each Z mass constraint Cleanest signal WH → lνbb 2 b jets ~ 1/2 MH each 1 lepton ~ 50 GeV each Missing ET ~ 50 GeV Highest production X-sec ZH → νν bb 2 b jets ~ 1/2 MH each 0 leptons Missing ET ~ 100 GeV Largest expected signal

slide-24
SLIDE 24

lepton

frag

PV jet

B hadron

K

frag

B h a d r

  • n

K π D SV b b

Lxy

Tools: b-tagging

Fake Rate (%) 2 4 6 8 10 12 b-Jet Efficiency (%) 20 30 40 50 60 70 80

46 % 28 %

Tagger NN JLIP

  • > 15 and All
T

p

Z bb

slide-25
SLIDE 25

WH → lνbb

  • f Lepton (GeV)

T

P

20 40 60 80 100 120 140 160 180

Events

1000 2000 3000 4000

  • f Lepton (GeV)

T

P

20 40 60 80 100 120 140 160 180

Events

1000 2000 3000 4000

DØ Preliminary

  • 1

L = 1.7 fb

W + 2 jets

Data W + jets QCD SM bkgd

  • f Lepton (GeV)

T

P

20 40 60 80 100 120 140 160 180

Events

1000 2000 3000 4000 (a)

2 20 30 40 50 60

46 % 28 %

Fake Rate % b-Jet Efficiency %

4 different analyses:

  • Double b-tag (S/B ~ 2.3/204)
  • Single b-tag (S/B ~ 4/1400)
  • W → eν
  • W → μν
slide-26
SLIDE 26

WH → lνbb̅: Neural Net

pT 2nd jet Δϕ jets pT di-jet pT (l,ETmiss)

Dijet Mass (GeV)

50 100 150 200 250 300

Events

20 40 60

Dijet Mass (GeV)

50 100 150 200 250 300

Events

20 40 60

DØ Preliminary

  • 1

L = 1.7 fb

W + 2 jets / 2 b-tags

Data W + jets QCD t t b Wb

  • ther

WH

115 GeV (x10)

Dijet Mass (GeV)

50 100 150 200 250 300

Events

20 40 60

(d)

R

  • 0.5

1 1.5 2 2.5 3 3.5 4 4.5 5

Events

10 20 30 40

R

  • 0.5

1 1.5 2 2.5 3 3.5 4 4.5 5

Events

10 20 30 40

DØ Preliminary

  • 1

L = 1.7 fb

W + 2 jets / 2 b-tags

Data W + jets QCD t t b Wb

  • ther

WH

115 GeV (x10)

R

  • 0.5

1 1.5 2 2.5 3 3.5 4 4.5 5

Events

10 20 30 40

(b)
  • f b-tagged jet (GeV)

T

P

50 100 150 200 250

Events

50 100 150

  • f b-tagged jet (GeV)

T

P

50 100 150 200 250

Events

50 100 150

DØ Preliminary

  • 1

L = 1.7 fb

W + 2 jets / 2 b-tags

Data W + jets QCD t t b Wb

  • ther

WH

115 GeV (x10)
  • f b-tagged jet (GeV)

T

P

50 100 150 200 250

Events

50 100 150

(a)

NN output - 2 tags

0.2 0.4 0.6 0.8 1 1.2 1.4

Events

1 10

2

10

3

10

NN output - 2 tags

0.2 0.4 0.6 0.8 1 1.2 1.4

Events

1 10

2

10

3

10

NN output - 2 tags

0.2 0.4 0.6 0.8 1 1.2 1.4

Events

1 10

2

10

3

10

DØ Preliminary

  • 1

L = 1.7 fb

W + 2 jets / 2 b-tags

Data W + jets QCD t t b Wb

  • ther

WH

115 GeV (x10)

NN output - 2 tags

0.2 0.4 0.6 0.8 1 1.2 1.4

Events

1 10

2

10

3

10

WH115 x10

NN

pT leading jet ΔR jets m(jet1,jet2)

slide-27
SLIDE 27

Combining WH Results

single b-tag double b-tag

Dijet Mass (GeV) 50 100 150 200 250 300 Events 1 10 2 10 3 10 Dijet Mass (GeV) 50 100 150 200 250 300 Events 1 10 2 10 3 10 Dijet Mass (GeV) 50 100 150 200 250 300 Events 1 10 2 10 3 10 DØ Preliminary
  • 1
L = 1.7 fb W + 2 jets / 1 b-tag Data W + jets QCD t t b Wb
  • ther
WH 115 GeV (x10) Dijet Mass (GeV) 50 100 150 200 250 300 Events 1 10 2 10 3 10 (a) Dijet Mass (GeV) 50 100 150 200 250 300 Events 1 10 2 10 3 10 Dijet Mass (GeV) 50 100 150 200 250 300 Events 1 10 2 10 3 10 Dijet Mass (GeV) 50 100 150 200 250 300 Events 1 10 2 10 3 10 DØ Preliminary
  • 1
L = 1.7 fb W + 2 jets / 2 b-tags Data W + jets QCD t t b Wb
  • ther
WH 115 GeV (x10) Dijet Mass (GeV) 50 100 150 200 250 300 Events 1 10 2 10 3 10 (c)
  • bs: 8.2

exp: 7.3

CDF

)

2

(GeV/c

H

m 105 110 115 120 125 130 135 140 145 ) b b

  • BR(H
  • WH)
  • p

(p

  • Limit /

10 20 30 40 50 60 70

  • 1

DØ Preliminary, L=1.7 fb b b

  • l
  • WH

Observed Limit Expected Limit

(a)

  • bs: 11.1

exp: 9.05

Limits relative to SM expectation

slide-28
SLIDE 28

ZH → ννbb̅ (+ WH → l ̸νbb̅)

b jet b jet

ETmiss + 2 jets b jet b jet MET 1 tight b-tag + 1 loose b-tag S/B ~ 3.7/443

DiJet Invariant Mass (GeV)

50 100 150 200 250 300

Events / 12.00 GeV

0.5 1 1.5 2 2.5 3 3.5 4 4.5 3 10
  • 50
100 150 200 250 300 0.5 1 1.5 2 2.5 3 3.5 4 4.5 3 10
  • Data
Top Z+b/c-jets Z+jets(l.f.) W+b/c-jets W+jets(l.f.) Diboson Multijet Hx500 (115 GeV)

)

  • 1

DØ preliminary (2.1 fb DiJet Invariant Mass (GeV)

50 100 150 200 250 300

Events / 12.00 GeV

10 20 30 40 50 60 50 100 150 200 250 300 10 20 30 40 50 60 Data Top Z+b/c-jets Z+jets(l.f.) W+b/c-jets W+jets(l.f.) Diboson Multijet Hx10 (115 GeV)

)

  • 1

DØ preliminary (2.1 fb DT discriminant

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Events / 0.05

10 20 30 40 50

DT discriminant

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Events / 0.05

10 20 30 40 50 Data Top Z+b/c-jets Z+jets(l.f.) W+b/c-jets W+jets(l.f.) Diboson Multijet VHx25 (115 GeV)

)

  • 1

DØ preliminary (1.2 fb

Decision Tree (Run IIb)

slide-29
SLIDE 29

Decision Tree

Boosting: as single top (adaptive boosting, AdaBoost) give mis-classified events a higher weight before re- training to make the tree work harder . Asymmetric b-tagging: ϵ=73%/48%; f=5%/0.5% (@pT>30, η<0.8)

/ ET

pT jets

scalar ET pT jet1 / HT pT jet2 HT ηjet1 A(/ ET , / HT ) . = (/ ET − / HT )/(/ ET + / HT ) ηjet2 / HT /HT dijet invariant mass min ∆φ(/ ET , jets) dijet transverse mass ∆φ(/ ET , jet1) (/ ET − pT tracks)/(/ ET + pT tracks)) ∆φ(/ ET , jet2) max(∆φ(/ ET , jets)) − min(∆φ(/ ET , jets)) ∆φ(/ ET , pT jet1 + pT jet2) max(∆φ(/ ET , jets)) + min(∆φ(/ ET , jets)) ∆φ(jet1, jet2)

pT tracks

∆R(jet1, jet2) pT tracks from dijets ( pT tracks − dijets pT tracks)/ pT tracks

Table 15: Variables used as input to the Decision Tree

H H H H H H H H HT

T T T T T T T T>212

>212 >212 >212 >212 >212 >212 >212 >212 P F P F p p p p p p p p pt

t t t t t t t t<31.6

<31.6 <31.6 <31.6 <31.6 <31.6 <31.6 <31.6 <31.6 P F M M M M M M M M Mt

t t t t t t t t<352

<352 <352 <352 <352 <352 <352 <352 <352 purity purity purity purity purity purity purity purity purity

hes, XLIII Recontres de Moriond (QCD)

ℓ ed from data between

slide-30
SLIDE 30

ZH→ννbb̅ @ CDF

2 b-tagging requirements: both jets with secondary vertex tag 1 jet with SVT , 1 jet with low probability that all tracks originate from the primary vertex 2 separate NN:

  • against fake E̷T in QCD multi-jet like events: E̷T is related to

jets and un-correlated to tracks; track based quantities enter the NN

  • ZH discriminating NN for limit setting: NNE̷T

, m(jj), E̷T(cal), met-dot-product: E̷T(cal) · E̷T(trk), dR(jj)

)

2

= 115 GeV/c

H

Neural Network Output (m

0.2 0.4 0.6 0.8 1 5 10 15 20 25 30 35

)

2

= 115 GeV/c

H

Neural Network Output (m

0.2 0.4 0.6 0.8 1 5 10 15 20 25 30 35

Vertex + Probability Tag (Signal Region)

)
  • 1
CDF Run II Preliminary (1.7 fb Ttbar W+h.f. Z+h.f Single Top WZ/WW ZZ Mistags QCD Bckgnd Err VH*8 (115 GeV) Data

)

2

Neural Network Output (115 GeV/c

0.2 0.4 0.6 0.8 1 5 10 15 20 25

)

2

Neural Network Output (115 GeV/c

0.2 0.4 0.6 0.8 1 5 10 15 20 25

Double Vertex Tag (Signal Region)

)
  • 1
CDF Run II Preliminary (1.7 fb Ttbar W+h.f. Z+h.f Single Top WZ/WW ZZ Mistags QCD Bckgnd Err VH*8 (115 GeV) Data
slide-31
SLIDE 31

ZH → ννbb̅

)

2

= 115 GeV/c

H

Neural Network Output (m

0.2 0.4 0.6 0.8 1 5 10 15 20 25 30 35

)

2

= 115 GeV/c

H

Neural Network Output (m

0.2 0.4 0.6 0.8 1 5 10 15 20 25 30 35

Vertex + Probability Tag (Signal Region)

)

  • 1

CDF Run II Preliminary (1.7 fb

Ttbar W+h.f. Z+h.f Single Top WZ/WW ZZ Mistags QCD Bckgnd Err VH*8 (115 GeV) Data

)

2

(GeV/c

H

M

110 115 120 125 130 135 140 145 150 155

95% CL Upper Limit/SM

1 10 )

  • 1

CDF Run II Preliminary (1.7 fb Observed Limit σ 1 ± Expected Limit

Met+Jets Search for ZH/WH

exp: 8.3

  • bs: 8.0

(GeV)

H

m 105 110 115 120 125 130 135 140 145 ) b b

  • BR(H
  • ZH)
  • p

(p

  • Limit /

5 10 15 20 25 30 35 40

)

  • 1

DØ Preliminary (2.1 fb DT, VH Signal b b

  • ZH

Observed Limit Expected Limit

  • bs: 7.5

exp: 8.4

Upcoming improvements:

  • QCD-multijet understanding.
  • Run IIb Level 1 CAL trigger upgrade.
  • Include single-tag.
slide-32
SLIDE 32

7

ZH → llbb̅: DØ

)

2

(GeV/c

H

m 105 110 115 120 125 130 135 140 145 ) b b → BR(H × ZH) → p (p σ Limit / 10 20 30 40 50 60 70 80 90 100

  • 1

DØ Preliminary, L=1.1 fb b ll b → ZH

Observed Limit Expected Limit

  • bs: 18

exp: 20

Mee (GeV)

50 100 150 200 250

Events / 2 GeV

  • 2

10

  • 1

10 1 10

2

10

3

10 50 100 150 200 250

  • 2

10

  • 1

10 1 10

2

10

3

10

Data QCD Z+jets Z+2b tt ZZ WZ ZH 115

)

  • 1

DØ Preliminary (920 pb

Leading-Pt di-jet mass (GeV)

40 60 80 100 120 140 160

Number of Events / 10 GeV

  • 1
10 1 10 40 60 80 100 120 140 160
  • 1
10 1 10 Data QCD Z+jets Z+bb(cc) tt WZ ZZ ZH 115

DØ Preliminary Leading-Pt di-jet mass (GeV)

40 60 80 100 120 140 160

Number of Events / 10 GeV

1 10 2 10 3 10 40 60 80 100 120 140 160 1 10 2 10 3 10 Data QCD Z+jets Z+bb(cc) tt WZ ZZ ZH 115

DØ Preliminary

)

Neural Network output

  • 0.4 -0.2 -0
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8

Number of Events / 0.05

  • 2
10
  • 1
10 1 10
  • 0.4 -0.2 -0
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
  • 2
10
  • 1
10 1 10

Data QCD Z+jets Z+bb(cc) tt WZ ZZ ZH 115 DØ Preliminary

No b-tag Double b-tag Neural Net S/B ~ 0.53/74 before NN

slide-33
SLIDE 33

ZH → llbb̅: CDF

exp: 16

  • bs: 16

Two independent neural nets are trained to separate ZH from

  • t t̅ background
  • Z+jet background

Z+Jets Vs. ZH 0.2 0.4 0.6 0.8 1 t Z H V s . t 0.2 0.4 0.6 0.8 1 Number of Events 5 10 15 20

  • 1

Ldt = 1 fb

∫

CDF II Preliminary NN Output - Data (Single Tag)

  • 1

Ldt = 1 fb

∫

CDF II Preliminary NN Output - Data (Single Tag)

Z+Jets

t t̅

ZH

slide-34
SLIDE 34

CDF: H → ττ

Use τlepτhad mode.

  • Lepton pT > 10 GeV
  • Hadronic τ pT > 15 GeV

3 Neural Nets are trained: Signal vs Z-> ττ + jets Signal vs ttbar Signal vs QCD Select Minimum of 3 NN to fit data.

slide-35
SLIDE 35

DØ: H → γγ

(GeV)

γ γ

M

50 100 150 200 250 300 350 400 450 500

Events/5 GeV

1 10

2

10

3

10 50 100 150 200 250 300 350 400 450 500 1 10

2

10

3

10

data γ γ QCD j γ jj *->ee γ Z/ signal(M=130GeV)

SM Higgs mass (GeV)

100 110 120 130 140 150

SM value ) γ γ BR( × σ 95% CL

20 40 60 80 100 120 140 160

Observed Limit Expected Limit σ 1 ± Expected Limit σ 2 ± Expected Limit preliminary

  • 1

DØ, 2.27 fb

2 isolated em clusters QCD and γj background estimated from data

slide-36
SLIDE 36

Tevatron Combination

1 10 10 2 110 120 130 140 150 160 170 180 190 200 1 10 10 2 mH(GeV/c2) 95% CL Limit/SM

Tevatron Run II Preliminary, L=1.0-2.4 fb-1 D∅ Exp CDF Exp Tevatron Expected Tevatron Observed ±1σ ±2σ

LEP Limit SM

March 2, 2008

  • bs: 5.1

exp: 3.3

  • bs: 1.1

exp: 1.6

slide-37
SLIDE 37

37

Projecting Higgs Reach to 2010

Improvements assumed in projections

✦ b-tagging

  • b-tagging with Layer 0 (~8% per tag efficiency increase, DØ)
  • add semileptonic b-tags (~5% per tag efficiency increase, DØ)
  • improved usage of existing taggers (~25%, CDF)
  • add single-b-tag channel to ZH→vvbb (DØ)

✦

Acceptance

  • include forward electrons in WH (DØ)
  • include 3-jet sample in WH (DØ)
  • 25% trigger acceptance (CDF)

✦

Analysis techniques

  • improved multivariate analyses (~20% in sensitivity)
  • better usage of ETmiss
  • di-jet mass resolution (from 18% to 15% in σ(m)/m, DØ)

✦ scaling of systematic uncertainties as a function of luminosity

Additional improvements not yet included in projection

inclusion of tau channels charm rejection in single b-tag analyses

  • ptimizing H→WW at low mass

…

slide-38
SLIDE 38

Higgs Projections 115GeV

=160 GeV

Summer 2005 Channels Summer 2006 Channels Summer 2007 Channels Winter 2008 Channels With Improvements

2 4 6 8 10 12 14 16 1 2 3 4 5 6 7 Integrated luminosity/Experiment (fb-1) Expected Limit/SM

Summer 2005 Channels Summer 2006 Channels arXiv:0712.2383 (2007) With CDF Update Winter 2008 With Improvements

!"#$%&%'%&()*+,-.'"+"#&$ #$%&'(

115 GeV CDF & DØ combined Rob Roser, P5 Meeting, 01/02/08

2009 2010

slide-39
SLIDE 39

Higgs Projection 160 GeV

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

CDF+D0 combined

  • curves are sqrt(L)

95% CL

slide-40
SLIDE 40

Conclusion The rise

  • r

setting

  • f the

Higgs is close

H

slide-41
SLIDE 41

Backup Slides

slide-42
SLIDE 42

Higgs @ATLAS