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Estimation of W+jets backgroundin the ATLAS H → WW → lνlν analysisusing a likelihood-based matrix technique
Phuong Nguyen Dang
University of Freiburg
Graduiertenkolleg − April 30, 2014
IntroductionMatrix method
W+jets background estimationConclusion
Outline
1 Introduction
2 Matrix method
3 W+jets background estimation
4 Conclusion
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 2 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Higgs productionHiggs decay channelsH→ WW analysisFake lepton categorization
Introduction
Higgs main production processes:
gluon-gluon Fusion (ggF)
Vector Boson Fusion (VBF)
WH/ZH AssociatedProduction
ttH Associated Production
[GeV] HM80 100 200 300 1000
H+
X)
[pb]
→(p
p σ
-210
-110
1
10
210= 8 TeVs
LH
C H
IGG
S X
S W
G 2
012
H (NNLO+NNLL QCD + NLO EW)
→pp
qqH (NNLO QCD + NLO EW)
→pp
WH (NNLO QCD + NLO EW
)
→pp
ZH (NNLO QCD +NLO EW)
→pp
ttH (NLO QCD)
→pp
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 3 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Higgs productionHiggs decay channelsH→ WW analysisFake lepton categorization
Introduction
Higgs decay branching ratios:
tt / bb / cc
gg
ττ / µµ
γγ / Zγ
WW / ZZ
[GeV]HM80 100 120 140 160 180 200
Hig
gs B
R +
Tota
l U
ncert
410
310
210
110
1
LH
C H
IGG
S X
S W
G 2
01
3
bb
ττ
µµ
cc
gg
γγ γZ
WW
ZZ
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 4 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Higgs productionHiggs decay channelsH→ WW analysisFake lepton categorization
Introduction
2 leptons selection
Same flavor (SF): ee, µµ
Different flavor (DF): eµ, µe
Missing ET
Jet multiplicities
0 jet, 1 jet, ≥2 jets
Many challenges
Many backgrounds (reducibleand irreducible)
Poor mass resolution (largemissing ET )
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 5 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Higgs productionHiggs decay channelsH→ WW analysisFake lepton categorization
Introduction
The W+jets background in the H → WW → lνlν similar to the Higgs signal.
[GeV]Tm
60 80 100 120 140 160 180 200 220 240
Eve
nts
/ 1
0 G
eV
20
40
60
80
100
120 Data stat)⊕ SM (sys
WW γ WZ/ZZ/W
t t Single Top
Z+jets W+jets
H [125 GeV]
ATLAS
1 L dt = 4.7 fb∫ = 7 TeV, s
+ 0 jetsνlνl→(*)
WW→H
Phys. Lett. B 716 (2012) 62-81
d
ν
e+
W+
u
g
u
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 6 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Higgs productionHiggs decay channelsH→ WW analysisFake lepton categorization
Fake lepton categorization (1/4)
Sources of fake leptons:
Hadrons reconstructed as leptons (light flavour).
Calorimeter shower from charged hadron fluctuates to look like electronshower.
Charged hadron punches through the calorimeter and is seen in muonsystem.
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 7 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Higgs productionHiggs decay channelsH→ WW analysisFake lepton categorization
Fake lepton categorization (2/4)
Sources of fake leptons:
Semi-leptonic heavy quark decays (heavy flavour).
c
ν
`+
W+
b s
ν
`+
W+
c
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 8 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Higgs productionHiggs decay channelsH→ WW analysisFake lepton categorization
Fake lepton categorization (3/4)
Sources of fake leptons:
Muon from inflight hadron (pion, kaon) decay.
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 9 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Higgs productionHiggs decay channelsH→ WW analysisFake lepton categorization
Fake lepton categorization (4/4)
Sources of fake leptons:
Electron from photon conversion.
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 10 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Fake factor methodMotivationIdentification variables
General idea of fake factor method
Consider a simple case with two nature type of leptons (real and fake).
Define the loose and tight selections for leptons, which differ from each otherby one (or more) lepton ID variable cuts (e.g. isolation).
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 11 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Fake factor methodMotivationIdentification variables
General idea of fake factor method
Consider a simple case with two nature type of leptons (real and fake).
Define the loose and tight selections for leptons, which differ from each otherby one (or more) lepton ID variable cuts (e.g. isolation).
Observe number of events in loose and tight selection:
N loose = N loosereal + N loose
fake
Ntight = Ntightreal + Ntight
fake
= εrealNloosereal + εfakeN
loosefake
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 11 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Fake factor methodMotivationIdentification variables
General idea of fake factor method
Consider a simple case with two nature type of leptons (real and fake).
Define the loose and tight selections for leptons, which differ from each otherby one (or more) lepton ID variable cuts (e.g. isolation).
Observe number of events in loose and tight selection:
N loose = N loosereal + N loose
fake
Ntight = Ntightreal + Ntight
fake
= εrealNloosereal + εfakeN
loosefake
ε is efficiency of loose leptons passing tight cut
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 11 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Fake factor methodMotivationIdentification variables
General idea of fake factor method
Observe number of events in loose and tight selection:
N loose = N loosereal + N loose
fake
Ntight = Ntightreal + Ntight
fake
= εrealNloosereal + εfakeN
loosefake
Matrix form [N loose
Ntight
]=
[1 1εreal εfake
]×
[N loose
realN loose
fake
]
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 12 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Fake factor methodMotivationIdentification variables
General idea of fake factor method
Observe number of events in loose and tight selection
N loose = N loosereal + N loose
fake
Ntight = Ntightreal + Ntight
fake
= εrealNloosereal + εfakeN
loosefake
Matrix form [N loose
Ntight
]=
[1 1εreal εfake
]×
[N loose
realN loose
fake
]Fake component[
N loosereal
N loosefake
]=
1
εfake − εreal
[εfake −1−εreal 1
]×
[N loose
Ntight
]Ntightfake =
εfake
εreal − εfake(εrealN
loose − Ntight)
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 12 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Fake factor methodMotivationIdentification variables
Motivation of matrix method
Estimated in Z-rich sample (Z control region).
Ntightfake =
εfake
εreal − εfake( εreal N loose − Ntight)
Estimated in jet-rich sample (di-jet sample, Z+jets sample).
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 13 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Fake factor methodMotivationIdentification variables
Motivation of matrix method
Estimated in Z-rich sample (Z control region).
Ntightfake =
εfake
εreal − εfake( εreal N loose − Ntight)
Estimated in jet-rich sample (di-jet sample, Z+jets sample).
→ Sample dependence error (∼30−50%)
New matrix method for W+jets background without sample dependence
Using directly di-lepton samples
Based on the response of each lepton category with ID variable cuts.
Matrix built from MC truth
→ systematic uncertainty = MC/data difference.
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 13 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Fake factor methodMotivationIdentification variables
Identification variables
Isolation variables
Track Isolation =lepPtcone30
lepPt
Calo Isolation =lepEtcone30
lepPt
non-isolated isolated
Track Isolation (lep 2)0 0.5 1 1.5 2
0
0.2
0.4
0.6
0.8
1
ATLAS Work in Progress Gamma
Hadron
Neutral Pion
True Electron
Arb
itra
ryunits
Electron
Track Isolation (lep 2)0 0.5 1 1.5 2
0
0.2
0.4
0.6
0.8
1
ATLAS Work in Progress Light Flavor
Heavy Flavor
True Muon
Arb
itra
ryunits
Muon
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 14 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Fake factor methodMotivationIdentification variables
Identification variables for electrons
TRT ratio & B-layer hits
TR_ratio10 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35ATLAS Work in Progress Gamma
Hadron
Neutral Pion
True Electron
Arb
itra
ryunits
lepnBLHits10 0.5 1 1.5 2 2.5 3 3.5 4
0
0.2
0.4
0.6
0.8
1ATLAS Work in Progress Gamma
Hadron
Neutral Pion
True Electron
Arb
itra
ryunits
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 15 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Fake factor methodMotivationIdentification variables
Identification variables for muons
Momentum Imbalance
P1/P1∆
0.2 0 0.2 0.4 0.6 0.8 10
0.1
0.2
0.3
0.4
0.5ATLAS Work in Progress Light Flavor
Heavy Flavor
True Muon
Arb
itra
ryunits
Impact parameter
lepsigd0PV10 5 10 15 20 25 30
0
0.2
0.4
0.6
0.8
1
ATLAS Work in Progress Light Flavor
Heavy Flavor
True Muon
Arb
itra
ryunits
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 16 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Monte Carlo closure testApplying to dataComparison with fake factor method
Matrix method application
Pre-Selection
Beginningof Cutstage
Building Matrix
MC closure test
Signal Region
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 17 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Monte Carlo closure testApplying to dataComparison with fake factor method
Monte Carlo closure test after pre-selection cuts (all jet inclusive)
Matrix is built at the beginning of cutstage.
Assume that lepton identification variables are orthogonal to the kinematicvariables (e.g. mT ) → use one matrix for all bins
[GeV]Tm
60 80 100 120 140 160 180 200 220 2400
50
100
150
200
250
300 ATLAS Work in Progress
Wjets
MC truth
Extracted
Wjets
MC truth = 975.70 ± 263.61
Extraction = 975.54 ± 22.7
Nu
mb
erof
even
ts
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 18 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Monte Carlo closure testApplying to dataComparison with fake factor method
Matrix method application
Pre-Selection
Beginningof Cutstage
Building Matrix
Apply
Signal Region
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 19 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Monte Carlo closure testApplying to dataComparison with fake factor method
W+jets background results in µe 0jet signal region
[GeV]Tm
60 80 100 120 140 160 180 200 220 2400
20
40
60
80
100 ATLAS Work in Progress
Wjets
MC truth
Extracted
WjetsN
um
ber
of
even
ts
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 20 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Monte Carlo closure testApplying to dataComparison with fake factor method
W+jets background results in µe 1jet signal region
[GeV]Tm
60 80 100 120 140 160 180 200 220 2400
10
20
30
40
50
60
70 ATLAS Work in Progress
Wjets
MC truth
Extracted
WjetsN
um
ber
of
even
ts
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 21 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Monte Carlo closure testApplying to dataComparison with fake factor method
Comparison with fake factor method in µe channel
ATLAS Work in Progress
0jet 1jet 2jet≥
Nu
mb
er
of
W+
jets
eve
nts
0
20
40
60
80
100
120
140
160 Matrix Method
Monte Carlo
Fake Factor
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 22 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Conclusion
Matrix technique is used for W+jets background in H→WW→ lνlν analysis.
This method is applied directly to dilepton sample, therefore no sample depen-dence error.
The systematic uncertainties of matrix method is about 20% for electron and30% for muon channels.
There is quite agreement between matrix and fake factor methods in signalregions after all selection cutstages.
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 23 / 29
IntroductionMatrix method
W+jets background estimationConclusion
BACKUP
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 24 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Event Selection
Preselection
2 tight leptons PT > 22(15)GeV
Mll > 10(12)GeV
|Mll −MZ | > 15GeV
EmissT ,rel > 20(40)GeV
0jet
P llT > 30GeV
Mll < 55GeV∆Φll < 1.8
1jet
b-tag vetoZττ vetoMll < 55GeV∆Φll < 1.8
≥2jet (VBF)
EmissT > 20(45)GeV
b-tag vetoZττ veto∆Yjj > 2.8, Mll > 500GeVMll < 60GeV , ∆Φll < 1.8
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 25 / 29
IntroductionMatrix method
W+jets background estimationConclusion
General case
In case the fake lepton has more than 1 component:
Nfake = Nfake1 + Nfake2 + ... (2)
We need more than 1 lepton ID variables to separate each lepton component.N loose
Ntight1
Ntight2
...
NtightN
=
1 1 1 ... 1
εtight1real εtight1
fake1 εtight1fake2 ... εtight1
fakeM
εtight2real εtight2
fake1 εtight2fake2 ... εtight2
fakeM... ... ... ... ...
εtightNreal εtightNfake1 εtightNfake2 ... εtightNfakeM
×
N loosereal
N loosefake1
N loosefake2...
N loosefakeM
(3)
The efficiencies are estimated from MC instead of building control region for eachfake component.
Systematic uncertainty mainly come from the incorrect MC description of leptonID variable cuts.
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 26 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Matrix building
N(C1, x)N(C2, x)
...N(Cn, x)
=
εs1 (C1) εs2 (C1) ... εsm (C1)εs1 (C2) εs2 (C2) ... εsm (C2)... ... ... ...
εs1 (Cn) εs2 (Cn) ... εsm (Cn)
× Ns1 (x)
Ns2 (x)...
Nsm (x)
(4)
Ci = WJET (2 tight lepton selection), STD (1tight+1loose lepton selection), ISO(passing isolation cut), INV ISO (passing inverted isolation cut), TRT, BL (Blayer), IMB (imbalance momentum),...
In this study, the variable x is one of the kinematic quantities: MT , MET , Mll , ∆φll ,. . .
The matrix elements are obtained from the MC truth.→ The systematic uncertainty is due to the MC/data difference.
The matrix does not rely on the knowledge of cross section of non-Wjetsbackground.
Not only the total number of events from each source but also the shape ofkinematic variables can be extracted.
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 27 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Monte Carlo closure test
[rad]ll
φ∆
0 0.5 1 1.5 2 2.5 30
50
100
150
200
250
300
ATLAS Work in Progress
Wjets
MC truth
Extracted
Wjets
Figure: ∆Φll distributions extracted after EmissT cut in µe channel
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 28 / 29
IntroductionMatrix method
W+jets background estimationConclusion
Comparison with fake factor method
Cross-check with MC W+jets and fake factor method.
Only statistic uncertainties are presented for both data-driven methods.
ATLAS Work in Progress
Channel Number of jets Matrix method MC W+jets Fake factorµµ 0jet 9.45 ± 3.82 0.01 ± 0.01 11.34 ± 1.31
1jet 4.95 ± 1.25 0.35 ± 0.35 2.77 ± 0.91≥2jet 0.49 ± 0.65 0.00 ± 0.00 0.05 ± 0.39
eµ 0jet 101.28 ± 30.81 207.66 ± 68.40 32.38 ± 1.881jet 27.13 ± 5.73 30.35 ± 12.13 19.25 ± 1.58≥2jet 1.52 ± 1.76 0.31 ± 0.31 0.93 ± 0.35
µe 0jet 46.89 ± 3.34 89.40 ± 34.62 55.12 ± 1.611jet 32.94 ± 3.68 83.92 ± 36.42 20.65 ± 1.28≥2jet 0.29 ± 0.45 0.00 ± 0.00 0.89 ± 0.36
ee 0jet 10.78 ± 0.89 32.87 ± 18.20 12.20 ± 0.551jet 3.63 ± 0.94 14.30 ± 7.90 2.11 ± 0.24≥2jet 0.16 ± 0.23 0.00 ± 0.00 0.08 ± 0.02
Phuong Nguyen Dang (Uni. Freiburg) Graduiertenkollegs der Universitat Freiburg − April 30, 2014 29 / 29