Searches for Diboson Resonances at CMS
Nhan Tran Fermilab
on behalf of the CMS collaboration
Brookhaven Forum October 7, 2015
2
reconstruction techniques
background estimation
results and outlook
introduction and overview
This talk focuses on searches for resonances with mass > 1 TeV See talk by Brian Pollack for searches for heavy Higgs (with mass < 1 TeV)
3high mass resonances for diboson resonances are a staple of new physics searches
Outline of the talk
2 11 Aug 2015 Andreas Hinzmann
• Di-boson resonance models
• Final state overview
• WW/WZ/ZZ resonance searches
• WH/ZH resonance searches
• HH resonance searches
X
W
W
g
g
q0
q
⌫
`
g
gX
H
Hp
p
b
b
b
b
•model interpretations… •extra dimensional models •bulk scenario of RS models; heavy spin-2 graviton or spin-0 radion decaying mainly to WL,ZL, H
•composite Higgs •heavy vector triplet scenario; heavy spin-1 W’ or Z’ decaying mainly to to WL,ZL, H Final states with boosted W/Z/H
5 11 Aug 2015 Andreas Hinzmann
• Above W,Z(H) pT>200(300) GeV quarks merge into R=0.8 jet • Discrimination based on jet substructure in Run 1 di-boson analyses
• Reconstruct W/Z/H with CA R=0.8 jet • Pruned jet mass ! expected at W/Z/H mass • N-subjettiness τ2/τ1 ! Should look like composed of two smaller jets • Calibrated in semileptonic ttbar sample containing real boosted Ws
X
W
W
g
g
q0
q
⌫
`
quark
anti-quark m = 2.16TeV
ΔRqqmin ≈ Δθqq
min ≈ 2 MV
pT ,V
Details in backup
4
W Z H
W lν+qq, qq+qq lv+qq, ll+qqqq+qq, lv+ll
lv+bb, qq+bb,qq+ττ, qq+WW(qqqq)
Z ll+qq, qq+qq qq+bb,qq+ττ, qq+WW(qqqq)
H bb+bb
diboson final states
eν,μν,τν νν
ee,μμ,ττother
WW
ττbb
ZW H
references: EXO-12-024, EXO-12-025, EXO-12-053, EXO-13-009, EXO-13-007, EXO-14-009, EXO-14-010
5
April
25, 2
014
�27
April 25, 2014�27
April 25, 2014�27
mass of X = 500 GeV pT of W/Z/H < 250 GeV
ΔRWqq ~ 0.8
searches require new reconstruction techniques
6
April
25, 2
014
�27
April 25, 2014
�27
mass of X = 1000 GeV pT of W/Z/H < 500 GeV
ΔRWqq ~ 0.4
searches require new reconstruction techniques
7merged W/Z qq
W or Z jet q/g jet
21τ-subjettiness ratio N
0 0.2 0.4 0.6 0.8 1
Even
ts / 0
.05
0
0.5
1
1.5
2
2.5
610×
Untagged data
MADGRAPH+PYTHIA
HERWIG++
2.94E+07) (JHUGEN+PYTHIA) × ZZ (→ (1.5TeV) bulkG
1.52E+07) (JHUGEN+PYTHIA) × WW (→ (1.5TeV) bulkG
8.51E+04) PYTHIA× WZ (→W’ (1.5 TeV)
1.34E+05) HERWIG++× ZZ (→ (1.5 TeV) RSG
7.15E+04) HERWIG++× WW (→ (1.5 TeV) RSG
= 8 TeVs, -1CMS, L = 19.7 fb
CA R=0.8
Pruned-jet mass (GeV)
0 50 100 150 200
Even
ts / 5
GeV
0
0.5
1
1.5
2
2.5
3
610×
Untagged data
MADGRAPH+PYTHIA
HERWIG++
2.94E+07) (JHUGEN+PYTHIA) × ZZ (→ (1.5TeV) bulkG
1.52E+07) (JHUGEN+PYTHIA) × WW (→ (1.5TeV) bulkG
8.51E+04) PYTHIA× WZ (→W’ (1.5 TeV)
1.34E+05) HERWIG++× ZZ (→ (1.5 TeV) RSG
7.15E+04) HERWIG++× WW (→ (1.5 TeV) RSG
= 8 TeVs, -1CMS, L = 19.7 fb
CA pruned R=0.8
massive 2-prong jets: W/Z tagging in CMS Run 1 uses CA8 jets with pruned mass (70-100 GeV)
+ n-subjettiness τ2/τ1
JME-13-006
tagging efficiencies are polarization dependent!
8merged Z/H bb
Higgs jet
(GeV)jJet mass m0 50 100 150 200
Arbi
trary
sca
le
0
0.1
0.2
0.3
0.4q/g MADGRAPH+PYTHIA
q'bq → Wb →t b b→H
4q→ WW* →H q'q →W q q→Z
CA pruned R=0.8
CMSSimulation
8 TeV
[GeV/c]T
Fat jet p0 100 200 300 400 500 600 700 800 900 1000
Tagg
ing
effic
ienc
y
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1
bb→H splittingbb→g
Hadronic ZHadronic topHadronic Wudsg jets
, Subjet IVFCSVL2<135 GeV/cpruned75<mAK R=0.8
(8 TeV)CMS Simulation Preliminary
8merged Z/H bb
15
Tagging Performance
Higgs channel
Top channel
QCD mistag rate reduced up to a factor 10 with minor loss of efficiency
Higgs-tagging =double b-tagging +75 <m
jet< 135 GeV
double b-tagging Higgs tagging
tagging efficiency mistag rate
14
B-Tagging Performance
Higgs channel
Top channel
Overall subjet b-tagging performs better
medium boost regime large boost regime
Subjet b-tagging performs better
Fat-jet b-tagging suitable at very high p
T
Higgs jet
(GeV)jJet mass m0 50 100 150 200
Arbi
trary
sca
le
0
0.1
0.2
0.3
0.4q/g MADGRAPH+PYTHIA
q'bq → Wb →t b b→H
4q→ WW* →H q'q →W q q→Z
CA pruned R=0.8
CMSSimulation
8 TeV
b tagging in boosted topologies: subjet b-tagging methods developed by
CMS in Run 1 for Higgs and Z tagging
boosted Higgs mass robust for bb and WW, mass resolution sufficient to distinguish from W/Z/t jets
BTV-13-001,DP-2014/031
Even
ts
0
50
100
150
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MC W - MC W +
νµ → = 8 TeV, W s at -1CMS Preliminary, 19.3 fb
= 1.0)κjet charge (-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8
Dat
a / S
im
0.51
1.52
9merged H WW qqqqW/Z discrimination
42τ-subjettiness ratio N0 0.2 0.4 0.6 0.8 1
Acc
× X
X)
→(H
Β ×
42τddN
0
0.5
1
1.5
2
2.5
3
3.5
4-310×
b b→H 4q→ WW* →H
c c→H gg→H
ττ →H
< 135 GeVj110 < m
CMSSimulation
8 TeV
novel method for tagging HWW* using τ4/τ2
νb q
q-
blt
W
b{
W polarization
jet charge
EXO-13-009,JME-13-006
H τhτh tagging
takes advantage of subjet techniques and a mass drop criteria in a fat jet to define inputs to traditional τ reconstruction techniques
H!ττ-tagging
20 11 Aug 2015 Andreas Hinzmann
• Main discriminator of taus against q/g-jets is isolation summing reconstructed particle energies in cone around tau decay products • Decay products of one excluded from isolation
cone of other tau forming the H!ττ • Higgs mass reconstructed from visible tau decay
products and missing transverse energy
[GeV]ττm0 50 100 150 200 250
[1/G
eV]
ττ1/
dm
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16 = 125 GeVH mττ →H
ττ →Z
= 8 TeVsCMS Simulation hτµ
H
Z
10validation of QCD
νb q
q-
blt
W
b{
standard candles
Even
ts
0
1
2
3
4
5
6
310×
Data Z+Jets
Single Top WW/WZ/ZZ
W+jets Pythia W+jets Herwig
powhegtt MC Stat + Sys
= 8 TeV, W+jetss at -1CMS Preliminary, 19.3 fb
CA R = 0.8 < 350 GeV
T250 < p
|<2.4η|
1τ/2τ0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Dat
a / S
im
0.5
1
1.5
2
ν
lW{ q/gq/g q/g
Extensive studies of substructure variables in many different topologies between data and MC
unfolded measurements are used to validate parton shower tunes
efficiency measurements using real boosted W jets
in semi-leptonic tt
SMP-12-024,JME-13-006
11fully data driven backgrounds
Background estimation – all-jets final states
7 11 Aug 2015 Andreas Hinzmann
• Assumption: Background has a smooth distribution and can be described by a fit function
• Simultaneously fit signal yield and background parameters
• Advantages:
• No need for background simulation • Disadvantages:
• Arbitrary choice of background functional form and a systematic uncertainty assigned to it
• Not possible in regions of discontinuity due to trigger turn-ons or kinematic selections
• Only works for bumps, not for enhancements in tails
• Checks: • Bias-test: How much is signal yield mis-fitted when fitting toy spectra of
default fit function with alternative functional form • F-test: Increase number of parameters until fit shows no significant
improvement
310×
/dm
(p
b/T
eV
)σ
d
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Fit WW (1.5 TeV)→ RSG
= 8 TeVs, -1CMS, L = 19.7 fb
(TeV)jjm1 1.5 2 2.5
Da
taσ
Da
ta-F
it
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V(qq)H(bb) resonances
16 11 Aug 2015 Andreas Hinzmann
• Same search techniques as V(qq)V(qq) search • Lower backgrounds due to better background rejection of H(bb)-tagger
compared to W(qq)/Z(qq)-tagger
Dijet Mass (GeV)1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
< Ev
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eV >
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HZ (1.0, 1.5, 2.0 TeV)→Z' HW (1.0, 1.5 2.0 TeV)→W'
bbHHPV
(8 TeV)-119.7 fbCMSPreliminary
(TeV)jjm1 1.5 2 2.5
Dat
aσ
Dat
a-Fi
t
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arXiv:1506.01443
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HZ (1.0, 1.5, 2.0 TeV)→Z' HW (1.0, 1.5 2.0 TeV)→W'
bbHLPV
(8 TeV)-119.7 fbCMSPreliminary
(TeV)jjm1 1.5 2 2.5
Dat
aσ
Dat
a-Fi
t
-202
Highest purity Lower purity
Assuming background has smooth shape, look for bump on top of background shape; simultaneous signal/background fit
no need for background simulation, but only relevant bump hunting on a smooth background (no kinematic/trigger turn-ons)
Used for JJ (qqqq,qqbb,bbbb,qqqqqq,qqτhτh)
12partially data driven backgrounds
fully MC estimated backgrounds
Use sideband region to extrapolate into signal region
Rate and shape taken from background sideband; takes shape
extrapolation and related uncertainties from the simulation
Closure tests performed in simulation and alternative data sidebands
(GeV)WWm1000 1500 2000 2500 3000
arbi
trary
uni
t
0
0.05
0.1
0.15
0.2
0.25 SidebandSignal Regionα
: Alternate PSα: Alternate Functionα
σ 1± ασ 2± α α
0
1
2
3
4
5
6
7
HPν e → = 8 TeV, Ws at -1CMS Preliminary, 19.5 fb
Background – leptons+jets final states
10 11 Aug 2015 Andreas Hinzmann
• Assumption: Observable in signal-depleted sideband closely related to signal region
• Background rate+shape estimated from data in sideband extrapolated to signal region using simulation
• Advantages: • Limited use of background simulation • Can search for enhancements in tails,
not only bumps
• Disadvantages: • Uncertainties associated to extrapolation
to signal region sometimes arbitrary
• Checks: • Closure test in simulation and/or other
data sideband
arXiv:1405.3447
40 50 60 70 80 90 100 110 120 130
data
σdata
-fit
-4-2024
Pruned jet mass (GeV)40 50 60 70 80 90 100 110 120 130
Even
ts /
( 5 G
eV )
50
100
150
200
250 WW/WZ/ZZ ttSingle Top W+jetsdata Uncertainty
HPν e → = 8 TeV, Ws at -1CMS Preliminary, 19.5 fb
Used for smaller SM backgrounds, e.g. WZ lllν search
Used for JJ (lνqq,llqq,qqτlτl/h)
sidebandsidebandsignal
14qqqq
• ATLAS sees excess at 2 TeV with 2.5 s.d. global signficance (arXiv:1506.00962)
• CMS higher+lower purity combined signficance at 1.8 TeV is 1.3 s.d.
310×
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b/T
eV
)σ
d
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Fit WW (1.5 TeV)→ RSG
= 8 TeVs, -1CMS, L = 19.7 fb
(TeV)jjm1 1.5 2 2.5 3
Da
taσ
Data
-Fit
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310×
/dm
(p
b/T
eV
)σ
d
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Fit WW (1.5 TeV)→ RSG
= 8 TeVs, -1CMS, L = 19.7 fb
(TeV)jjm1 1.5 2 2.5
Da
taσ
Data
-Fit
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V(qq)V(qq) resonances in dijets – 2
9 11 Aug 2015 Andreas Hinzmann
Highest purity τ2/τ1 <0.5
Lower purity 0.5< τ2/τ1 <0.75
arXiv:1405.1994 1.3σ effect at 1.8 TeV
V V
analysis split into high and low purity regions
EXO-12-024
15lνqqV V
14 6 Modeling of background and signal
[GeV]WWm1000 1500 2000 2500 3000
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510 HP)νµCMS Data ( W+jets
WW/WZ tt
Single t Uncertainty
100)× = 0.5 (PlM/k = 1 TeV, G MbulkG
= 8 TeVs at -1CMS L = 19.7 fb
[GeV]WWm1000 1500 2000 2500 3000
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510 LP)νµCMS Data ( W+jets
WW/WZ tt
Single t Uncertainty
100)× = 0.5 (PlM/k = 1 TeV, G MbulkG
= 8 TeVs at -1CMS L = 19.7 fb
[GeV]WWm1000 1500 2000 2500 3000
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HP)νCMS Data (e W+jets
WW/WZ tt
Single t Uncertainty
100)× = 0.5 (PlM/k = 1 TeV, G MbulkG
= 8 TeVs at -1CMS L = 19.7 fb
[GeV]WWm1000 1500 2000 2500 3000
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LP)νCMS Data (e W+jets
WW/WZ tt
Single t Uncertainty
100)× = 0.5 (PlM/k = 1 TeV, G MbulkG
= 8 TeVs at -1CMS L = 19.7 fb
Figure 7: Final distributions in mWW for data and expected backgrounds for both the muon(top) and the electron (bottom) channels, high-purity (left) and low-purity (right) categories.The 68% error bars for Poisson event counts are obtained from the Neyman construction asdescribed in Ref. [75]. Also shown is a hypothetical bulk graviton signal with mass of 1000 GeVand k/MPl = 0.5. The normalization of the signal distribution is scaled up by a factor of 100for a better visualization.
e channelhigh purity
e channellow purity
μ channellow purity
μ channelhigh purity
EXO-13-009
16llqqV V
6.1 Background estimation 15
[GeV]ZZm500 1000 1500 2000 2500
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GeV
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10 HP)µµCMS Data (
Background estimationZ+jets
, VV)tOther Backgrounds (t = 0.5 (x100)PlM/k = 1 TeV, G MbulkG
= 8 TeVs at -1CMS L = 19.7 fb
[GeV]ZZm1000 1500 2000 2500
Even
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GeV
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1
10 LP)µµCMS Data (Background estimationZ+jets
, VV)tOther Backgrounds (t = 0.5 (x100)PlM/k = 1 TeV, G MbulkG
= 8 TeVs at -1CMS L = 19.7 fb
[GeV]ZZm500 1000 1500 2000 2500
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GeV
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10CMS Data (ee HP)Background estimationZ+jets
, VV)tOther Backgrounds (t = 0.5 (x100)PlM/k = 1 TeV, G MbulkG
= 8 TeVs at -1CMS L = 19.7 fb
[GeV]ZZm1000 1500 2000 2500
Even
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10 CMS Data (ee LP)Background estimationZ+jets
, VV)tOther Backgrounds (t = 0.5 (x100)PlM/k = 1 TeV, G MbulkG
= 8 TeVs at -1CMS L = 19.7 fb
Figure 8: Final distributions in mZZ for data and expected backgrounds for both the muon (top)and the electron (bottom) channels, high-purity (left) and low-purity (right) categories. Pointswith error bars show distributions of data; solid histograms depict the different componentsof the background expectation from simulated events. The 68% error bars for Poisson eventcounts are obtained from the Neyman construction as described in Ref. [75]. Also shown is ahypothetical bulk graviton signal with mass of 1000 GeV and k/MPl = 0.5. The normalizationof the signal distribution is scaled up by a factor of 100 for a better visualization. The solid lineshows the central value of the background predicted from the sideband extrapolation proce-dure.
ee channelhigh purity
ee channellow purity
μμ channellow purity
μμ channelhigh purity
EXO-13-009
17
[GeV]GM1000 1500 2000 2500
) [pb
]bu
lk G
→ (p
p 95
%σ
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1 observedS
Frequentist CL
σ 1± expected S
Frequentist CL
σ 2± expected S
Frequentist CL
= 0.5PlM/k), bulk G→ (pp THσ
I II III
CMS = 8 TeVs at -1L = 19.7 fb
600
Limits on spin-2 WW/ZZ resonances
13 11 Aug 2015 Andreas Hinzmann
• Run I searches start to be sensitive to gravitons in Bulk model • Cross section and width related to coupling parameter k/Mpl
• Narrow width for k/Mpl ≤ 0.5 • Model independent limits provided allowing reinterpretation for wide width
resonance and as spin-1 WZ resonance (see later)
VV
k/Mpl=0.5
k/Mpl=1.0
[GeV]GM1000 1500 2000 2500 3000
) [pb
]bu
lk G
→ (p
p 95
%σ
-210
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1
CMS (unpublished) = 8 TeVs at -1L = 19.7 fb
600
obs. (solid) / exp. (dashed)S
Frequentist CL
+V-jetµµ ee/→ ZZ → bulkG
V-jet+V-jet→ WW/ZZ → bulkG
+V-jetνµ/ν e→ WW → bulkG
Combination
VV
arXiv:1405.3447
1.2 s.d.
2.0 s.d.
1.3 s.d.
VV upper limits
6 5 Systematic uncertainties
optimal values are then plotted as a function of the WZ mass and an analytic function is fit to229
the resulting distribution. For the mass-window requirement, two regimes of linear behavior230
are observed: for masses less than 1200 GeV, a narrow mass window is optimal in order to231
reject as much background as possible. Above 1200 GeV, the background ceases to contribute232
significantly and it is better to have a large mass window. The LT requirement exhibits a linear233
relationship: as the mass increases, it is optimal to require a larger LT, until around 1000 GeV,234
at which point having LT greater than 500 GeV is sufficient. These mass windows and LT re-235
quirements are summarized in Table 1.236
0 200 400 600 800 1000 1200 1400 1600
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510CMS
(8 TeV)-119.5 fb
ObservedγZZ/Z
ttZ+JetsWZW' (1.0 TeV)W' (1.5 TeV)
(GeV)WZM0 200 400 600 800 1000 1200 1400 1600
σ(o
bs-b
kg)
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510CMS
(8 TeV)-119.5 fb
ObservedγZZ/Z
ttZ+JetsWZW' (1.0 TeV)W' (1.5 TeV)
(GeV)lT
pΣ ≡ TL0 100 200 300 400 500 600 700 800
σ(o
bs-b
kg)
-202
Figure 1: The WZ invariant mass (left) and LT (right) distributions for the background, signal,and observed events after the WZ candidate selection. The last bin includes overflow events.The (obs � bkg)/s in the lower panel is defined as the difference between the number of ob-served events and the number of expected background events divided by the total statisticaluncertainty.
5 Systematic uncertainties237
Systematic uncertainties affecting the analysis can be grouped into four categories. In the first238
group we include uncertainties that are determined from simulation. These include uncertain-239
ties in the lepton and EmissT energy scales and resolution, as well as uncertainties in the PDFs.240
Following the recommendations of the PDF4LHC group [53, 54], PDF and as variations of the241
MSTW2008 [55], CT10 [56], and NNPDF2.0 [57] PDF sets are taken into account and their im-242
pact on the WZ cross section estimated. Signal PDF uncertainties are taken into account only243
to derive uncertainty bands around the signal cross sections, as shown in Fig. 2, and do not244
impact the central limit. An uncertainty associated with the simulation of pileup is also taken245
into account.246
The second group includes the systematic uncertainties affecting the observed-to-simulated247
scale factors for the efficiencies of the trigger, reconstruction, and identification requirements.248
These efficiencies are derived from tag-and-probe studies, and the uncertainty in the ratio of249
the efficiencies is typically taken as the systematic uncertainty. For the Z ! ee channel, we250
assign a 2% uncertainty related to the trigger scale factors, another 2% to account for the dif-251
ference between the observed and simulated reconstruction efficiencies, and an additional 1%252
uncertainty related to the electron identification and isolation scale factors. For the Z ! µµ253
WZ lllν
10 7 Summary
(GeV)TCρW', M
500 1000 1500 2000
B (p
b)⋅
σ
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(GeV)TCρW', M
500 1000 1500 2000
B (p
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1 CMS
(8 TeV)-119.5 fb
Observed 95% CL
Expected 95% CL
σ 1±Expected
σ 2±Expected
W'σ
TCσ
Figure 2: Limits at 95% CL on s ⇥B(W0 ! 3`n) as a function of the mass of the EGM W0 (blue)and rTC (red), along with the 1 s and 2 s combined statistical and systematic uncertainties in-dicated by the green (dark) and yellow (light) band, respectively. The theoretical cross sectionsinclude a mass-dependent NNLO K-factor. The thickness of the theory lines represents thePDF uncertainty associated with the signal cross sections. The predicted cross sections for rTCassume that MpTC = 3
4 MrTC � 25 GeV and that the LSTC parameter sin c = 1/3.
Acknowledgments316
We congratulate our colleagues in the CERN accelerator departments for the excellent perfor-317
mance of the LHC and thank the technical and administrative staffs at CERN and at other CMS318
institutes for their contributions to the success of the CMS effort. In addition, we gratefully319
acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid320
for delivering so effectively the computing infrastructure essential to our analyses. Finally, we321
acknowledge the enduring support for the construction and operation of the LHC and the CMS322
detector provided by the following funding agencies: BMWFW and FWF (Austria); FNRS and323
FWO (Belgium); CNPq, CAPES, FAPERJ, and FAPESP (Brazil); MES (Bulgaria); CERN; CAS,324
MoST, and NSFC (China); COLCIENCIAS (Colombia); MSES and CSF (Croatia); RPF (Cyprus);325
MoER, ERC IUT and ERDF (Estonia); Academy of Finland, MEC, and HIP (Finland); CEA and326
CNRS/IN2P3 (France); BMBF, DFG, and HGF (Germany); GSRT (Greece); OTKA and NIH327
(Hungary); DAE and DST (India); IPM (Iran); SFI (Ireland); INFN (Italy); NRF and WCU (Re-328
public of Korea); LAS (Lithuania); MOE and UM (Malaysia); CINVESTAV, CONACYT, SEP,329
and UASLP-FAI (Mexico); MBIE (New Zealand); PAEC (Pakistan); MSHE and NSC (Poland);330
FCT (Portugal); JINR (Dubna); MON, RosAtom, RAS and RFBR (Russia); MESTD (Serbia);331
SEIDI and CPAN (Spain); Swiss Funding Agencies (Switzerland); MST (Taipei); ThEPCenter,332
IPST, STAR and NSTDA (Thailand); TUBITAK and TAEK (Turkey); NASU and SFFR (Ukraine);333
STFC (United Kingdom); DOE and NSF (USA).334
Individuals have received support from the Marie-Curie programme and the European Re-335
search Council and EPLANET (European Union); the Leventis Foundation; the A. P. Sloan336
Foundation; the Alexander von Humboldt Foundation; the Belgian Federal Science Policy Of-337
fice; the Fonds pour la Formation a la Recherche dans l’Industrie et dans l’Agriculture (FRIA-338
Br(lllν)
18VH qqbb, 6q
V(qq)H(bb) resonances
16 11 Aug 2015 Andreas Hinzmann
• Same search techniques as V(qq)V(qq) search • Lower backgrounds due to better background rejection of H(bb)-tagger
compared to W(qq)/Z(qq)-tagger
Dijet Mass (GeV)1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6
< Ev
ents
/ G
eV >
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V(qq)H(WW!qqqq) resonances
19 11 Aug 2015 Andreas Hinzmann
• Exclusive search channel: Only events that fail H(bb) tagger
• Factor 4 less stringent limits on cross section than H(bb) channel • Still adds 10% to combination with H(bb)
• For Run II also consider H(WW!lvqq) jets
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(8 TeV)-119.7 fbCMSPreliminary
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Dat
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Dat
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arXiv:1506.01443
VH VWW 6q (also VHPHLP, VLPHHP categories)
W/Z+H qqbb
19WH lνbb
W(lv)H(bb) resonances
17 11 Aug 2015 Andreas Hinzmann
• Same search techniques as W(lv)V(qq) search • Lower backgrounds due to better background rejection of H(bb)-tagger
compared to W(qq)/Z(qq)-tagger • Excess in W(lv)H(bb) at 1.8 TeV has a global significance of 2.2 s.d.
(GeV)WHM800 1000 1200 1400 1600 1800 2000 2200 2400
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muon channel electron channel
20ZH qqττ all τ modes are covered!EXO-13-007
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7Background estimationObservedVV (V = Z or W)
and single-toptt+jetsγZ/
5)×σ = 1.5 TeV, Z'Signal (M
(8 TeV)-119.7 fbCMS
categoryµτµτ
Figure 1: Observed distributions of mZH for the all-leptonic channels along with the corre-sponding MC expectations for signal and background, as well as background estimation de-rived from data: (top left) tete category; (top right) tetµ category; (bottom) tµtµ category. Tenequal-size histogram bins cover the region from 0 to 2.5 TeV, while a single bin is used at highermZH because of the limited number of MC and data events. The signal cross section is scaledby a factor of 5.
[GeV]ZHm0 500 1000 1500 2000 2500 3000
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and single-toptt+jetsγZ/
5)×σ = 1.5 TeV, Z'Signal (M
(8 TeV)-119.7 fbCMS
categoryhτµτ
Figure 2: Observed distributions of mZH for the semileptonic channels along with the corre-sponding MC expectations for signal and background, as well as background estimation de-rived from data: (left) teth category; (right) tµth category. Ten equal-size histogram bins coverthe region from 0 to 2.5 TeV, while a single bin is used at higher mZH because of the limitednumber of MC and data events. The signal cross section is scaled by a factor of 5.
8 8 Systematic uncertainties
[GeV]ZHm0 500 1000 1500 2000 2500 3000
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and single-toptt+jetsγZ/
5)×σ = 1.5 TeV, Z'Signal (M
(8 TeV)-119.7 fbCMS
categoryµτµτ
Figure 1: Observed distributions of mZH for the all-leptonic channels along with the corre-sponding MC expectations for signal and background, as well as background estimation de-rived from data: (top left) tete category; (top right) tetµ category; (bottom) tµtµ category. Tenequal-size histogram bins cover the region from 0 to 2.5 TeV, while a single bin is used at highermZH because of the limited number of MC and data events. The signal cross section is scaledby a factor of 5.
[GeV]ZHm0 500 1000 1500 2000 2500 3000
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and single-toptt+jetsγZ/
5)×σ = 1.5 TeV, Z'Signal (M
(8 TeV)-119.7 fbCMS
categoryhτµτ
Figure 2: Observed distributions of mZH for the semileptonic channels along with the corre-sponding MC expectations for signal and background, as well as background estimation de-rived from data: (left) teth category; (right) tµth category. Ten equal-size histogram bins coverthe region from 0 to 2.5 TeV, while a single bin is used at higher mZH because of the limitednumber of MC and data events. The signal cross section is scaled by a factor of 5.
8 8 Systematic uncertainties
[GeV]ZHm0 500 1000 1500 2000 2500 3000
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and single-toptt+jetsγZ/
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7Background estimationObservedVV (V = Z or W)
and single-toptt+jetsγZ/
5)×σ = 1.5 TeV, Z'Signal (M
(8 TeV)-119.7 fbCMS
categoryµτµτ
Figure 1: Observed distributions of mZH for the all-leptonic channels along with the corre-sponding MC expectations for signal and background, as well as background estimation de-rived from data: (top left) tete category; (top right) tetµ category; (bottom) tµtµ category. Tenequal-size histogram bins cover the region from 0 to 2.5 TeV, while a single bin is used at highermZH because of the limited number of MC and data events. The signal cross section is scaledby a factor of 5.
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and single-toptt+jetsγZ/
5)×σ = 1.5 TeV, Z'Signal (M
(8 TeV)-119.7 fbCMS
categoryhτµτ
Figure 2: Observed distributions of mZH for the semileptonic channels along with the corre-sponding MC expectations for signal and background, as well as background estimation de-rived from data: (left) teth category; (right) tµth category. Ten equal-size histogram bins coverthe region from 0 to 2.5 TeV, while a single bin is used at higher mZH because of the limitednumber of MC and data events. The signal cross section is scaled by a factor of 5.
8 8 Systematic uncertainties
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and single-toptt+jetsγZ/
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(8 TeV)-119.7 fbCMS
categoryµτµτ
Figure 1: Observed distributions of mZH for the all-leptonic channels along with the corre-sponding MC expectations for signal and background, as well as background estimation de-rived from data: (top left) tete category; (top right) tetµ category; (bottom) tµtµ category. Tenequal-size histogram bins cover the region from 0 to 2.5 TeV, while a single bin is used at highermZH because of the limited number of MC and data events. The signal cross section is scaledby a factor of 5.
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and single-toptt+jetsγZ/
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(8 TeV)-119.7 fbCMS
categoryhτµτ
Figure 2: Observed distributions of mZH for the semileptonic channels along with the corre-sponding MC expectations for signal and background, as well as background estimation de-rived from data: (left) teth category; (right) tµth category. Ten equal-size histogram bins coverthe region from 0 to 2.5 TeV, while a single bin is used at higher mZH because of the limitednumber of MC and data events. The signal cross section is scaled by a factor of 5.
8 8 Systematic uncertainties
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and single-toptt+jetsγZ/
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(8 TeV)-119.7 fbCMS
categoryµτµτ
Figure 1: Observed distributions of mZH for the all-leptonic channels along with the corre-sponding MC expectations for signal and background, as well as background estimation de-rived from data: (top left) tete category; (top right) tetµ category; (bottom) tµtµ category. Tenequal-size histogram bins cover the region from 0 to 2.5 TeV, while a single bin is used at highermZH because of the limited number of MC and data events. The signal cross section is scaledby a factor of 5.
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and single-toptt+jetsγZ/
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categoryhτµτ
Figure 2: Observed distributions of mZH for the semileptonic channels along with the corre-sponding MC expectations for signal and background, as well as background estimation de-rived from data: (left) teth category; (right) tµth category. Ten equal-size histogram bins coverthe region from 0 to 2.5 TeV, while a single bin is used at higher mZH because of the limitednumber of MC and data events. The signal cross section is scaled by a factor of 5.
9
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Fit to ObservationFit to Simulation
and single-topttQCDW+jetsVV (V = Z or W)
+jetsγZ/
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categoryhτµτ
Figure 3: Observed distributions of mPjet for the semileptonic channels along with the corre-
sponding MC expectations for signal and background: (left) teth category; (right) tµth category.Fits are performed for MC and data (as discussed in the text).
[GeV]Pjetm
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]ττ
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Figure 4: Definitions of the A, B, C, and D regions in the mPjet / mtt plane used in the background
estimation for the all-hadronic channel.
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categoryhτhτ
ObservedQCDW+jetsVV (V = Z or W)tt
+jetsγZ/5)×σ = 1.5 TeV, Z'Signal (M
Figure 5: Observed distributions of mZH for the thth category along with the correspondingMC expectations for signal and background. Ten equal-size histogram bins cover the regionfrom 0 to 2.5 TeV, while a single bin is used at higher mZH because of the limited number of MCand data events. The signal cross section is scaled by a factor of 5.
τeτe τeτμ τμτμ
τμτhτeτh τhτh
21• Heavy Vector Triplet model B (composite Higgs-like model) m(W’)=m(Z’) excluded up to 1.8 TeV • WV(lvqq), VV(qqqq) and VH(qqbb) have best sensitivity at high masses
Resonance mass [TeV]1 1.5 2 2.5 3
X) [
pb]
→(p
p 95
%σ
-210
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(EXO-12-025)ν lll→ WZ →X qqqq (EXO-12-024)→ WV →X
qq (EXO-13-009)ν l→ WV →X qqll (EXO-13-009)→ WZ →X
bb (EXO-14-010)ν l→ WH →X (EXO-13-007)ττ qq→ VH →X
qqbb,6q (EXO-14-009)→ VH →X )0 X→(pp THσ)+- X→(pp THσ)++ X→(pp THσ
= 3)V
HVT Model B (gWH)→ BR(X≈WZ) →BR(X
ZH)→ BR(X≈WW) → BR(X≈
, V = W / Z0 / X±X = X
(8 TeV)-119.7 fbCMS Preliminary
Limits on spin-1 WW/WZ/ZH/WH resonances
22 11 Aug 2015 Andreas Hinzmann
VV/VH
N.B. different signal model, heavy vector triplet
VH upper limits
VH channels
22HH bbbb
[GeV]jjM1000120014001600180020002200
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(8 TeV)-119.7 fbPreliminaryCMS
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D
ata
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ata
- Bac
kg
-1.5-1.0-0.50.00.51.01.5
H(bb)H(bb) resonances – 2
23 11 Aug 2015 Andreas Hinzmann
• Require ≥3 b-tagged subjets in event • If subjets closer than ΔR<0.3: Require b-tagged fatjet instead of 2 subjets
• Categorize in purity via τ2/τ1 • Background estimate is intermediate approach between fitting background
shape in signal region (limited by statistics) and estimation from sideband (limited by understanding of b-tagging fake rate) • Background shape from pruned jet mass sideband 70<mJ<100 GeV • Background yield fit together in signal region excluding resonance window
CMS-PAS-EXO-12-053
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1st jet lower purity 2nd jet lower purity
analysis split into high and low τ2/τ1 purity regions
subjet b-tagging based on 3 highest subjets or 2 fat jet b tags
EXO-12-053
23
Findings for spin-0 HH resonances
24 11 Aug 2015 Andreas Hinzmann
• Extra dimension spin-0 radion (ΛR=1 TeV) excluded around 1.2-1.5 TeV
(note: model at edge of validity of narrow width approximation)
HH
Resonance Mass (TeV)1 1.5 2 2.5 3
H(b
b)H(
bb))
(fb)
→ B
R (X
×
σ
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410 (8 TeV)-119.7 fb
CMSPreliminary Observed
Expectedσ 1 ±Expected σ 2 ±Expected
= 1 TeV)RΛRadion ( = 3 TeV)RΛRadion (
CMS-PAS-EXO-12-053
(GeV)spin-0Xm
500 1000 1500 2000 2500
HH)
(fb)
→ sp
in-0
X→
(pp
σ95
% C
L lim
it on
10
210
310
410
510)bbγγCMS-PAS-HIG-13-032 () - spin-0 bbbCMS arXiv:1503.04114 (b
)γ 90 (2014) 112013 (l and DCMS Phys. Rev. low mass (CMS-PAS-HIG-14-034)ττbCMS b
(CMS-PAS-EXO-12-053)bbbCMS b
ObservedExpected
(8 TeV)-117.9-19.7 fb
HH bbbb
N.B. boosted regime taking over > ~1 TeV
24conclusions and outlook
•rich program of diboson searches at CMS •broad searches for VV, VH and HH
•common techniques used amongst various different analyses •many analyses use jet substructure techniques and subjet b-tagging to identify highly boosted W,Z,H
•similar jet substructure background techniques across the analyses
•
W Z H
W lν+qq, qq+qq lv+qq, ll+qqqq+qq, lv+ll
lv+bb, qq+bb,qq+ττ, qq+WW(qqqq)
Z ll+qq, qq+qq qq+bb,qq+ττ, qq+WW(qqqq)
H bb+bb
25conclusions and outlook
•Some mild excesses to keep an eye on for Run 2!
•Run 2 will be present new challenges for diboson searches •higher pileup and boost, CMS is prepared!
• To reconstruct high pT jet substructure make full use of ECAL granularity • Rather than assigning ΣEcalo-Σptrack excess to single photon or neutral
hadron (“merged PF neutrals”) with HCAL granularity • Split photon excess according to ECAL clusters (“split PF photons”) • Split hadron excess energy in ECAL+HCAL according to direction and
energy distribution of ECAL clusters (“split PF neutrals”)
jet constituent multiplicity0 50 100 150
Nor
mal
ized
Dis
tribu
tion
0
0.2
0.4
0.6merged PF neutrals
split PF photons
split PF photons+neutrals
AK R=0.8 (W) < 2.4 TeV
T1.6 < p
|<2.4η|
13 TeV
CMSSimulation Preliminary
pruned jet mass (GeV)0 50 100 150
Nor
mal
ized
Dis
tribu
tion
0
0.1
0.2
0.3
merged PF neutrals
split PF photons
split PF photons+neutrals
AK R=0.8 (W) < 2.4 TeV
T1.6 < p
|<2.4η|
13 TeV
CMSSimulation Preliminary
PF improvements for Run II
9 19 Aug 2014 Andreas Hinzmann
JME-14-002
W pT = 2 TeV
Particle flow improvements keep substructure robust at very high pT
Pileup Per Particle Id (PUPPI)keeps substructure observables robust
in high pileup environments
(GeV)gen - mrecom-100 -80 -60 -40 -20 0 20 40 60 80 100
arbi
trary
uni
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> = 40PU<n < 600 GeV
T200 GeV < p
| < 2.5 η|
13 TeVCMS Simulation Preliminary
PF+PUPPIm>=-0.5 GeV∆<
RMS=10.0 GeV
PFm>=12.1 GeV∆<
RMS=15.4 GeV
PF+CHSm>=-5.0 GeV∆<
RMS=12.4 GeV
PF(Cleansing)m>=-1.9 GeV∆<
RMS=12.1 GeV
PF+CHS(Const.Sub.)m>=0.6 GeV∆<
RMS=11.4 GeV
13 TeVCMS Simulation Preliminary
27CMS jet reconstruction in a nutshellJet reconstruction in CMS
27 11 Aug 2015 Andreas Hinzmann
HCAL |η|<5
Muon |η|<2.4 ECAL |η|<3
Tracker |η|<2.5
η-5 -4 -3 -2 -1 0 1 2 3 4 5
PF e
nerg
y fra
ctio
n
00.10.20.30.40.50.60.70.80.9
MC DATACharged pile-up: Charged hadrons: Photons: Neutral hadrons: Electrons & Muons: Forward hadrons: Forward photons:
= 8 TeVs-1CMS preliminary, L = 1.6 fb
< 114 GeVT 49 GeV < pTag & Probe method
Detector pT-resolution η/Φ-segmentation Tracker 0.6% (0.2 GeV) – 5% (500 GeV) 0.002 x 0.003 (first pixel layer) ECAL 1% (20 GeV) – 0.4% (500 GeV) 0.017 x 0.017 (barrel) HCAL 30% (30 GeV) – 5% (500 GeV) 0.087 x 0.087 (barrel)
ECAL
Tracker
HCAL
Jet energy fractions
CMS-DP-2012/012
Particle Flow algorithm benefits from sub-detectors with best spatial+energy resolution
Jet clustering (anti-kT or CA)
Particle flow reconstruction
Andreas Hinzmann
April 25, 2014 28observables
•pT,Y,φ + tracking
•mass •4-vector sum of jet constituents •highly sensitive to soft QCD and pileup; grooming can be used to mitigate these dependencies
•shapes •several classes: declustering/reclustering, generalized jet shapes and energy flow, statistical interpretation (Qjets), jet charge
•algorithms •some combination of cuts on mass, shapes, tracking •most typical in top tagging
1 2
34
1 2
3
cluster with rfilt < R
keep nfilt
1 2
34
1 2
3
cluster with rfilt < R
keep pTi > pTfrac
4
veto soft and large angle recombinations
min(pTi,pTj)/pTi+j < zcut or dij > rcut×2m/pT
filtering
trimming
pruning
1 2
34
1 2
3
cluster with rfilt < R
keep nfilt
1 2
34
1 2
3
cluster with rfilt < R
keep pTi > pTfrac
4
veto soft and large angle recombinations
min(pTi,pTj)/pTi+j < zcut or dij > rcut×2m/pT
filtering
trimming
pruning
1 2
34
1 2
3
cluster with rfilt < R
keep nfilt
1 2
34
1 2
3
cluster with rfilt < R
keep pTi > pTfrac
4
veto soft and large angle recombinations
min(pTi,pTj)/pTi+j < zcut or dij > rcut×2m/pT
filtering
trimming
pruning
Trim
ming
Filte
ring
Prun
ing
arXiv:0802.2470 arXiv:0912.1342 arXiv:0903.5081
April 25, 2014 30groomed mass
)PV
Reconstructed vertex multiplicity (N0 2 4 6 8 10 12 14
[GeV
]〉
jet
m〈
20
40
60
80
100
120
140
160 ATLAS = 7 TeVs, -1 Ldt = 1 fb∫Data 2011 LCW jets with R=1.0tanti-k
| < 0.8η < 300 GeV, |Tjet p≤200
No jet grooming =0.3sub=0.01, Rcutf=0.3sub=0.03, Rcutf =0.3sub=0.05, Rcutf=0.2sub=0.01, Rcutf =0.2sub=0.03, Rcutf=0.2sub=0.05, Rcutf
(a) Trimmed anti-kt
: 200 GeV p
jetT <
300 GeV
)PV
Reconstructed vertex multiplicity (N0 2 4 6 8 10 12 14
[GeV
]〉
1jet
m〈
100
150
200
250
300ATLAS = 7 TeVs, -1 Ldt = 1 fb∫Data 2011
LCW jets with R=1.0tanti-k| < 0.8η < 800 GeV, |
Tjet p≤600
No jet grooming =0.3sub=0.01, Rcutf=0.3sub=0.03, Rcutf =0.3sub=0.05, Rcutf=0.2sub=0.01, Rcutf =0.2sub=0.03, Rcutf=0.2sub=0.05, Rcutf
(b) Trimmed anti-kt
: 600 GeV p
jetT <
800 GeV
)PV
Reconstructed vertex multiplicity (N0 2 4 6 8 10 12 14
[GeV
]〉
jet
m〈
60
80
100
120
140ATLAS = 7 TeVs, -1 Ldt = 1 fb∫Data 2011
LCW jets with R=1.0tanti-k| < 0.8η < 300 GeV, |
Tjet p≤200
No jet grooming =0.05cut=0.10, zcutR=0.10cut=0.10, zcutR =0.05cut=0.20, zcutR=0.10cut=0.20, zcutR =0.05cut=0.30, zcutR=0.10cut=0.30, zcutR
(c) Pruned anti-kt
: 200 GeV p
jetT < 300 GeV
)PV
Reconstructed vertex multiplicity (N0 2 4 6 8 10 12 14
[GeV
]〉
1jet
m〈
100
120
140
160
180
200
220
240 ATLAS = 7 TeVs, -1 Ldt = 1 fb∫Data 2011 LCW jets with R=1.0tanti-k
| < 0.8η < 800 GeV, |Tjet p≤600
No jet grooming =0.05cut=0.10, zcutR=0.10cut=0.10, zcutR =0.05cut=0.20, zcutR=0.10cut=0.20, zcutR =0.05cut=0.30, zcutR=0.10cut=0.30, zcutR
(d) Pruned anti-kt
: 600 GeV p
jetT < 800 GeV
)PV
Reconstructed vertex multiplicity (N0 2 4 6 8 10 12 14
[GeV
]〉
jet
m〈
0
20
40
60
80
100
120
140
160 ATLAS = 7 TeVs, -1 Ldt = 1 fb∫Data 2011C/A LCW jets with R=1.2
| < 0.8η < 300 GeV, |Tjet p≤200
No jet grooming=0.67
fracµ
=0.33frac
µ=0.20
fracµ
(e) Filtered C/A: 200 GeV p
jetT < 300 GeV
)PV
Reconstructed vertex multiplicity (N0 2 4 6 8 10 12 14
[GeV
]〉
1jet
m〈
50
100
150
200
250
300 ATLAS = 7 TeVs, -1 Ldt = 1 fb∫Data 2011C/A LCW jets with R=1.2
| < 0.8η < 800 GeV, |Tjet p≤600
No jet grooming=0.67
fracµ
=0.33frac
µ=0.20
fracµ
(f) Filtered C/A: 600 GeV p
jetT < 800 GeV
Figure 18. Evolution of the mean uncalibrated jet mass, hmjeti, for jets in the central region|⌘| < 0.8 as a function of the reconstructed vertex multiplicity, N
PV
for jets in the range 200 GeV pjet
T
< 300 GeV (left) and for leading-pjet
T
jets (hmjet
1
i) in the range 600 GeV pjet
T
< 800 GeV(right). (a)-(b) show trimmed anti-kt jets with R = 1.0, (c)-(d) show pruned anti-kt jets withR = 1.0, and (e)-(f) show mass-drop filtered C/A jets with R = 1.2. The error bars indicate thestatistical uncertainty on the mean value in each bin.– 32 –
pruned jet mass0 50 100 150
arb
itrary
units
0
0.1
0.2
SM Higgs, m = 600 GeV
ungroomed jet mass
W+Jets, MadGraph+Pythia6
ungroomed jet mass
CMS Simulation
•Grooming tends to push the jet mass scale of the background to lower values while preserving the hard scale of the heavy resonance
•Grooming techniques are also vital in reducing the pileup dependence of the jet mass
CMS-PAS-HIG-13-008 JHEP 1309 (2013) 076
April 25, 2014 31shapes
•Declustering and reclustering •For modern sequential recombination jet algorithms, the jet has a history — a series of 2-to-1 combinations
•examples: mass drop - (msj1/m) , √d12 - first splitting scale of kT algorithm
•Generalized jet shapes •some simple questions: How wide is a jet? How prong-y is a jet? How asymmetric is a jet? How stable is a jet?
•N-subjettiness (τN) [1], how consistent a jet is with having N subjets, ratios are typically used, e.g. τ2/τ1 for W-jets, τ3/τ2 for top jets
•energy correlation functions [2], axis-less version of N-subjettiness •Qjets [3], Exploiting the “quantum” nature of jets •jet charge [4], an oldy but a goody •jet width; pTD; r-cores; planar flow...
[1] Thaler, Van Tilburg, JHEP 1103:015,2011[2] Larkoski, Salam, Thaler, arXiv:1305.0007[3] Ellis et al., PRL 108, 182003 (2012)[4] Krohn et al., Phys. Rev. Lett. 110 (2013) 21200
April 25, 2014 32example: N-subjettiness�58N-subjettiness
N-subjettiness
10
calculations and resummation techniques (see, e.g. recent work in Ref. [29, 30]) compared
to algorithmic methods for studying substructure. Finally, N -subjettiness gives favorable
efficiency/rejection curves compared to other jet substructure methods. While a detailed
comparison to other methods is beyond the scope of this work, we are encouraged by these
preliminary results.
The remainder of this paper is organized as follows. In Sec. 2, we define N -subjettiness
and discuss some of its properties. We present tagging efficiency studies in Sec. 3, where we
use N -subjettiness to identify individual hadronic W bosons and top quarks, and compare
our method against the YSplitter technique [2, 3, 4] and the Johns Hopkins Top Tagger [6].
We then apply N -subjettiness in Sec. 4 to reconstruct hypothetical heavy resonances de-
caying to pairs of boosted objects. Our conclusions follow in Sec. 5, and further information
appears in the appendices.
2. Boosted Objects and N-subjettiness
Boosted hadronic objects have a fundamentally different energy pattern than QCD jets
of comparable invariant mass. For concreteness, we will consider the case of a boosted
W boson as shown in Fig. 1, though a similar discussion holds for boosted top quarks or
new physics objects. Since the W decays to two quarks, a single jet containing a boosted
W boson should be composed of two distinct—but not necessarily easily resolved—hard
subjets with a combined invariant mass of around 80 GeV. A boosted QCD jet with an
invariant mass of 80 GeV usually originates from a single hard parton and acquires mass
through large angle soft splittings. We want to exploit this difference in expected energy
flow to differentiate between these two types of jets by “counting” the number of hard lobes
of energy within a jet.
2.1 Introducing N-subjettiness
We start by defining an inclusive jet shape called “N -subjettiness” and denoted by τN .
First, one reconstructs a candidate W jet using some jet algorithm. Then, one identifies
N candidate subjets using a procedure to be specified in Sec. 2.2. With these candidate
subjets in hand, τN is calculated via
τN =1
d0
!
k
pT,k min {∆R1,k,∆R2,k, · · · ,∆RN,k} . (2.1)
Here, k runs over the constituent particles in a given jet, pT,k are their transverse momenta,
and ∆RJ,k ="
(∆η)2 + (∆φ)2 is the distance in the rapidity-azimuth plane between a
candidate subjet J and a constituent particle k. The normalization factor d0 is taken as
d0 =!
k
pT,kR0, (2.2)
where R0 is the characteristic jet radius used in the original jet clustering algorithm.
It is straightforward to see that τN quantifies how N -subjetty a particular jet is, or
in other words, to what degree it can be regarded as a jet composed of N subjets. Jets
– 3 –(a)
0 0.5 1 1.5
4.5
5
5.5
6
Boosted Top Jet, R = 0.8
η
φ
(b)
(c)
−1.5 −1 −0.5 0−0.15
0.35
0.85
1.35
1.75Boosted QCD Jet, R = 0.8
η
φ
(d)
Figure 4: Left: Decay sequences in (a) tt and (c) dijet QCD events. Right: Event displays for(b) top jets and (d) QCD jets with invariant mass near mtop. The labeling is similar to Fig. 1,though here we take R = 0.8, and the cells are colored according to how the jet is divided intothree candidate subjets. The open square indicates the total jet direction, the open circles indicatethe two subjet directions, and the crosses indicate the three subjet directions. The discriminatingvariable τ3/τ2 measures the relative alignment of the jet energy along the crosses compared to theopen circles.
a b jet and a W boson, and if the W boson decays hadronically into two quarks, the top jet
will have three lobes of energy. Thus, instead of τ2/τ1, one expects τ3/τ2 to be an effective
discriminating variable for top jets. This is indeed the case, as sketched in Figs. 4, 5, 6,
and 7.
– 7 –
☐ = τ1
◯ = τ2
× = τ3
generalizing subjets...N-subjettiness: a measure of how consistent a jet is with having N subjets, τN
k, sum over particles in the jetN subjet axes for computing τN
calculations and resummation techniques (see, e.g. recent work in Ref. [29, 30]) compared
to algorithmic methods for studying substructure. Finally, N -subjettiness gives favorable
efficiency/rejection curves compared to other jet substructure methods. While a detailed
comparison to other methods is beyond the scope of this work, we are encouraged by these
preliminary results.
The remainder of this paper is organized as follows. In Sec. 2, we define N -subjettiness
and discuss some of its properties. We present tagging efficiency studies in Sec. 3, where we
use N -subjettiness to identify individual hadronic W bosons and top quarks, and compare
our method against the YSplitter technique [2, 3, 4] and the Johns Hopkins Top Tagger [6].
We then apply N -subjettiness in Sec. 4 to reconstruct hypothetical heavy resonances de-
caying to pairs of boosted objects. Our conclusions follow in Sec. 5, and further information
appears in the appendices.
2. Boosted Objects and N-subjettiness
Boosted hadronic objects have a fundamentally different energy pattern than QCD jets
of comparable invariant mass. For concreteness, we will consider the case of a boosted
W boson as shown in Fig. 1, though a similar discussion holds for boosted top quarks or
new physics objects. Since the W decays to two quarks, a single jet containing a boosted
W boson should be composed of two distinct—but not necessarily easily resolved—hard
subjets with a combined invariant mass of around 80 GeV. A boosted QCD jet with an
invariant mass of 80 GeV usually originates from a single hard parton and acquires mass
through large angle soft splittings. We want to exploit this difference in expected energy
flow to differentiate between these two types of jets by “counting” the number of hard lobes
of energy within a jet.
2.1 Introducing N-subjettiness
We start by defining an inclusive jet shape called “N -subjettiness” and denoted by τN .
First, one reconstructs a candidate W jet using some jet algorithm. Then, one identifies
N candidate subjets using a procedure to be specified in Sec. 2.2. With these candidate
subjets in hand, τN is calculated via
τN =1
d0
!
k
pT,k min {∆R1,k,∆R2,k, · · · ,∆RN,k} . (2.1)
Here, k runs over the constituent particles in a given jet, pT,k are their transverse momenta,
and ∆RJ,k ="
(∆η)2 + (∆φ)2 is the distance in the rapidity-azimuth plane between a
candidate subjet J and a constituent particle k. The normalization factor d0 is taken as
d0 =!
k
pT,kR0, (2.2)
where R0 is the characteristic jet radius used in the original jet clustering algorithm.
It is straightforward to see that τN quantifies how N -subjetty a particular jet is, or
in other words, to what degree it can be regarded as a jet composed of N subjets. Jets
– 3 –
Ratios of τN are traditionally used for discriminating signal
from background
J. Thaler, K. Van Tilburg, arXiv:1011.2268
generalizing subjets…N-subjettiness: a measure of how consistent a jet is with having N subjets, τN
As τN → 0, jet is more consistent with having N subjets
e.g. τ2 → 0, more like a W jet e.g. τ1 → 0, more like a quark jet
Ratios are typically used:τ2/τ1 for separating W jets from quark and gluon jets
April 25, 2014 33shapes for boosted objects
mass drop Qjet volatility τ2/τ1
prun
ed m
ass
cut
beware the correlations! CMS PAS JME-13-006
34W tagger optimization
W-tagger optimization
33 11 Aug 2015 Andreas Hinzmann
arXiv:1410.4227
W/Z/H tagging comparison
34 11 Aug 2015 Andreas Hinzmann
• Compare Run I W/Z/H taggers at 35% efficiency working point
• H(bb) can be discriminated from background by a factor >2 better than W(qq)/Z(qq)
Tagger BR(W/Z/H!xx) Efficiency (W/Z/H) Mistag rate (q/g-jets) W/Z(qq)-tagger 70% / 68% 35% 1.2% H(bb)-tagger 57% 35% 0.5% H(WW!qqqq)-tagger 10% 35% 1.5% H(ττ)-tagger 6% 35% 0.03%
April 25, 2014 35data-driven methodsEv
ents
0
100
200
300
400
500
600
700
800Data Z+Jets
Single Top WW/WZ/ZZ
W+jets Pythia powhegtt
mc@nlott MC Stat + Sys
= 8 TeV, W+jetss at -1CMS Preliminary, 19.3 fb
CA R = 0.8 > 200 GeV
Tp
|<2.4η| < 130 GeVj40 < m
1τ/2τ0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Dat
a / S
im
0.5
1
1.5
2
Data-driven estimation of W-tagging scale factors via simultaneous fit to pass/fail samples
mean (MC) = 83.2 ± 0.3 GeV mean (data) = 84.4 ± 0.4 GeV
σ (MC) = 7.1 ± 0.4 GeV σ (data) = 7.4 ± 0.6 GeV
scale factor = 0.93 ± 0.08
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