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Search for Dijet Resonance in pp Collisions
at CMS
1
Sertac Ozturk Cukurova University / LPC
Sertac Ozturk
About Myself
✓ I am graduate student at Cukurova University, in Turkey.
✓ I have been at LPC for 2 years and my Ph.D. thesis research has been being done at LPC.
‣ Search for new physics with jets with Dr. Robert M. Harris
‣ HCAL with Dr. Shuichi Kunori
✓ I plan to graduate in December.
2
Sertac Ozturk
Search for Dijet Resonance
A MC based analysis was done for 10 TeV collisions and a CMS reviewed paper (PAS QCD 2009-006) and an analysis note (CMS AN-2009/070) were written
The results based on 120 nb-1 (PAS EXO 2010-001) were approved for ICHEP 2010
The results based on 836 nb-1 data (PAS EXO 2010-010) were approved for HCP 2010
The results based on 2.88 pb-1 data will be submitted to PRL (hopefully this Friday) and it will cover my Ph.D. thesis.
My talk will be based on the latest data.
3
Sertac Ozturk
Dijet in Standard Model• What is a Dijet?
✓ Dijet results from simple 2→2 scattering of “partons”
✓ Dijets are events which primarily consist of two jets in the final state.
• We search for the new particles in “Dijet Mass” spectrum
✓ If a resonance exist, It can show up as a bump in Dijet Mass spectrum
• Dijet Mass from final state
Dijets in Standard Model• What is a dijet?
• Parton Level
✓ Dijet results from simple 2→2 scattering of “partons”
✓ quarks, anti-quarks and gluons
• Particles Level
✓ Partons come from colliding protons
✓ The final state partons become jets of observable particles via the following chain of events
‣ The partons radiate gluons.
‣ Gluons splits into quarks and antiquarks
‣ All colored object “hadronize” into color neutral particles
‣ Jet made of π, k, p, n, etc
• Dijets are events which primarily consist of two jets in the final state.
Jet
Jet
Particle Level
6
m =�
(E1 + E2)2 − (�p1 + �p2)2
4
61
D Event Displays and Table of High Mass Dijet Events725
Figure 38: Lego (left) and ρ− φ (right) displays of the 1st to 3rd Highest Masss Dijet Events
Sertac Ozturk
Resonance Models
• The models which are considered in this analysis are listed.
✓ Produced in “s-channel”
✓ Parton-Parton Resonances
‣ Observed as dijet resonances.
• Search for model with narrow width Γ.
3
Dijet Resonances
! New particles that decay to dijets" Produced in “s-channel” " Parton - Parton Resonances
# Observed as dijet resonances.
" Many models have small width !
X
q, q, g
q, q, g
q, q, g
q, q, g
Time
Spa
ce
Mass
Rat
e
Breit-Wigner
!!!!
M
1"1"2"1"1"1+!+0+J P
qq0.01SingletZ ‘Heavy Z
q1q20.01SingletW ‘Heavy W
qq,gg0.01SingletGR S Graviton
qq,gg0.01Octet#T8Octet Technirho
qq0.05OctetCColoron
qq0.05OctetAAxigluon
qg0.02Triplet q*Excited Quark
ud0.004Triplet DE6 Diquark
Chan! / (2M)Color XModel Name
Sertac Ozturk, University of Cukurova3
Dijet Resonances
! New particles that decay to dijets" Produced in “s-channel” " Parton - Parton Resonances
# Observed as dijet resonances.
" Many models have small width !
X
q, q, g
q, q, g
q, q, g
q, q, g
Time
Spa
ce
Mass
Rat
e
Breit-Wigner
!!!!
M
1"1"2"1"1"1+!+0+J P
qq0.01SingletZ ‘Heavy Z
q1q20.01SingletW ‘Heavy W
qq,gg0.01SingletGR S Graviton
qq,gg0.01Octet#T8Octet Technirho
qq0.05OctetCColoron
qq0.05OctetAAxigluon
qg0.02Triplet q*Excited Quark
ud0.004Triplet DE6 Diquark
Chan! / (2M)Color XModel Name
Sertac Ozturk, University of Cukurova
5
1.3 Summary of Experimental Technique 3
Sundrum gravitons [10], with coupling k/MPL = 0.1, from a model of large extra dimensions65
are produced from gluons or quark-antiquark pairs in the initial state (qq̄, gg → G). Heavy W66
bosons [11] inspired by left-right symmetric grand unified models have electroweak couplings67
and require antiquarks for their production(q1q̄2 → W �), giving small cross sections. Heavy68
Z bosons [11] inspired by grand-unified models are widely anticipated by theorists, but they69
are electroweakly produced, and require an antiquark in the initial state(qq̄ → Z�), so their70
production cross section is around the lowest of the models considered. The model with the71
largest cross section is a recent model of string resonances, Regge excitations of the quarks and72
gluons in open string theory, which includes resonances in all parton-parton channels (qq̄, qq,73
gg and qg) with multiple spin states and quantum numbers [12, 13]. Table 1 summarizes some74
properties of these models.75
Model Name X Color JP Γ/(2M) ChanExcited Quark q* Triplet 1/2+ 0.02 qg
E6 Diquark D Triplet 0+ 0.004 qqAxigluon A Octet 1+ 0.05 qq̄Coloron C Octet 1− 0.05 qq̄
RS Graviton G Singlet 2+ 0.01 qq̄ , ggHeavy W W’ Singlet 1− 0.01 qq̄Heavy Z Z’ Singlet 1− 0.01 qq̄
String S mixed mixed 0.003− 0.037 qq̄, qq, gg and qgTable 1: Properties of Some Resonance Models
Published lower limits [14] on the mass of these models in the dijet channel are listed in table 2.76
q∗ A or C D ρT8 W � Z� G S0.87 1.25 0.63 1.1 0.84 0.74 - -
Table 2: Published lower limits in dijet channel in TeV on the mass of new particles consideredin this analysis. These 95% confidence level exclusions are from the Tevatron [14].
77
1.3 Summary of Experimental Technique78
Our experimental technique starts with a measurement of the inclusive process pp → jet + jet +79
anything. Inclusive means we measure processes containing at least two jets in the final state,80
but the events are allowed to contain additional jets, or anything else. The dijet in the event81
is simply the two highest pt jets, the leading jets. Within the standard model our dataset is82
expected to be overwhelming dominated by the 2 → 2 process of hard parton scatters, with83
additional radiation off the initial and final state partons naturally giving additional jets. We84
do not cut away events that contain this radiation, which would reduce signals that also have85
similar amounts of radiation, and un-necessarily restrict signals to a narrow topology. The86
events can also contain additional particles, such as leptons or photons, but this will occur very87
rarely in the standard model. Finally, even more rarely within the standard model, the two88
leading CaloJets in the event can result from electrons, photons or taus producing energy in the89
calorimeter, and we do not exclude these insignificant contributions to our sample either. Our90
dijet selection is then open to many signals of new physics including high pt jets, leptons and91
photons. However, our selection is optimized for signals in the 2 → 2 parton scattering process,92
and is overwhelmingly dominated by the signal background of dijets from QCD within the93
standard model.94
Sertac Ozturk
Experimental Technique
• Measurement of dijet mass spectrum
• Comparison to PYTHIA QCD Monte Carlo prediction
• Fit of the measured dijet mass spectrum with a smooth function and search for resonance signal (bump)
• If no evidence, calculate cross section upper limit and compare with model cross section.
6
Sertac Ozturk
Data Sample• Dataset
✓ (135059-135735) - /MinimumBias/Commissioning10-SD_JetMETTau-Jun14thSkim_v1/RECO
✓ (136066-137028) - /JetMETTau/Run2010A-Jun14thReReco_v2/RECO
✓ (137437-139558) - /JetMETTau/Run2010A-PromptReco-v4/RECO
✓ (139779-140159) - /JetMETTau/Run2010A-Jul16thReReco-v1/RECO
✓ (140160-141899) - /JetMETTau/Run2010A-PromptReco-v4/RECO
✓ (141900-142664) - /JetMET/Run2010A-PromptReco-v4/RECO
• /QCDDijet_PtXXtoYY/Spring10-START3X_V26_S09-v1/GEN-SIM-RECO
• Official JSON Files
• Estimated Integrated Luminosity: 2.875 pb-1 (with 11% uncertanity)
7
Sertac Ozturk
Event Selection
8
• Trigger
✓ Technical Bit TT0 (for beam crossing)
✓ HLT_Jet50U (un-prescaled)
• Event Selection
✓ Good primary vertex
✓ At least two reconstructed jets
‣ AK7caloJets
‣ JEC: L2+L3, "Summer10" + Residual Data-Driven
✓ Require both |Jet η|< 2.5 and |Δη|<1.3
‣ Suppress QCD process significantly.
✓ Require both leading jets passing the "loose" jet id & Mjj > 220 GeV (corrected)
5. MEASUREMENT OF DIJET MASS SPECTRUM Sertac Ozturk
Dijet Mass (GeV)50 100 150 200 250 300 350 400 450
Trig
ger E
ffici
ency
0.2
0.4
0.6
0.8
1
DijetMass
= 220 GeVjjM
HLT_Jet50U
= 7 TeVs
| < 1.3! "| < 2.5 & |2!,
1!|
(GeV)T
Corrected Leading Jet P60 80 100 120 140 160
Trig
ger E
ffici
ency
0.2
0.4
0.6
0.8
1
pt50U
HLT_Jet50U = 7 TeVs
| < 1.3! "| < 2.5 & |2!,
1!|
= 105 GeVTJet P
Figure 5.2 HLT Jet50U trigger efficiency as a function of dijet mass (left) and as a function of
corrected pT of leading jet is measured in data.
5.1.2 Data Quality
The number of events in the analysis after the basic cuts are shown for each cut in
Table 5.1.
Events after pre-selection cut 6126910 100%
Events after vertex cut 6125930 99.98%
Events after dijet eta cuts 2088922 34.09%
Events after dijet mass cut 414645 6.78%
Events after jet id cut 414131 6.76%
Table 5.1 Cuts and Events
Only 514 events which are mostly HPD noise are rejected by JetID cut and the fraction
of events removed by JetID cut is very small. Because the reqirement kinematic cuts
(|η| < 2.5 and |∆η| < 1.3) and dijet mass cut (M j j > 220 GeV) gives higher the jet purity.
The distributions of the loose JetID variable are shown in Fig.5.3. Electromagnetic
fraction of jet energy, Jet EMF, doesn’t habe a peak near zero or one which indicate a
problem from ECAL and HCAL, such as hot channel. The fraction of jet energy in the
27
Events Fraction
Sertac Ozturk
Trigger Efficiency• Start analysis of Dijet Mass distribution at 220 GeV.
✓ 220 GeV chosen for full trigger efficiency
9
Dijet Mass (GeV)50 100 150 200 250 300 350 400 450
Trig
ger E
ffici
ency
0.2
0.4
0.6
0.8
1
DijetMass
= 220 GeVjjM
HLT_Jet50U
= 7 TeVs
| < 1.3η Δ| < 2.5 & |2η,
1η|
(GeV)T
Corrected Leading Jet P60 80 100 120 140 160
Trig
ger E
ffici
ency
0.2
0.4
0.6
0.8
1
pt50U
HLT_Jet50U = 7 TeVs
| < 1.3η Δ| < 2.5 & |2η,
1η|
= 105 GeVTJet P
Sertac Ozturk
Dijet Data Quality
10
η-3 -2 -1 0 1 2 3
φ
-3-2-1
012
3
Two
Lead
ing
Jets
0
50
100
150
200
)-1
CMS Data (2.875 pb
)-1CMS Data (2.875 pb
1φ
-3 -2 -1 0 1 2 3
2φ
-3
-2
-1
0
1
2
3
0
100
200
300
400
500
600
700Phi_corr
Jet EMF0 0.2 0.4 0.6 0.8 1
Two
Lead
ing
Jets
0
20000
40000
60000
80000
)-1CMS Data (2.875 pb
QCD PYTHIA
TMET/Sum E0 0.2 0.4 0.6 0.8 1
Even
ts
1
10
210
310
410
510)-1CMS Data (2.875 pb
QCD PYTHIA
Sertac Ozturk
Dijet Data Stability
• Average dijet mass and average pt of two leading jets for each runs are shown.
• There is a good stability.
11
hDijetMassRunbyRun
Entries 148Mean 74.03RMS 43.57
Run Number
1351
4913
5528
1355
7513
7028
1387
4613
8747
1387
5013
8919
1389
2113
9020
1390
9813
9100
1391
0313
9195
1392
3913
9347
1393
6513
9368
1393
7013
9372
1394
0713
9411
1394
5813
9781
1399
6713
9969
1399
7113
9973
1400
5814
0059
1401
2314
0124
1401
2614
0158
1401
5914
0160
1403
3114
0361
1403
6214
0379
1403
8214
0383
1403
8514
0387
1404
0114
1956
1419
5714
1958
1419
5914
1960
1419
6114
2035
1420
3614
2038
1420
4014
2076
1421
2914
2130
1421
3214
2135
1421
3614
2137
1421
8714
2188
1421
8914
2191
1422
6414
2265
1423
0314
2305
1423
0814
2309
1423
1114
2312
1423
1314
2317
1423
1814
2319
1424
1314
2414
1424
1514
2418
1424
1914
2420
1424
2214
2513
1425
1414
2524
1425
2514
2528
1425
3014
2535
1425
3714
2557
1425
5814
2659
1426
6014
2661
1426
6214
2663
1426
6414
2928
1429
3314
2935
1429
5314
2954
1429
7014
2971
1430
0414
3005
1430
0614
3007
1430
0814
3179
1431
8114
3187
1431
9114
3192
1431
9314
3318
1433
1914
3320
1433
2114
3322
1433
2614
3327
1433
2814
3657
1436
6514
3727
1437
3114
3827
1438
3314
3835
1439
5314
3954
1439
5514
3956
1439
5714
3959
1439
6014
3961
1439
6214
4011
1440
8614
4089
1441
1214
4114
Aver
age
Dije
t Mas
s (G
eV)
220
240
260
280
300
320
340
hDijetMassRunbyRun
Entries 148Mean 74.03RMS 43.57
DijetMassRunbyRun
HLT_Jet50U
|<1.3η Δ|<2.5 & |2η,
1η|
>220 GeVjjM
-1Run with L>1 nb
> = 279.7 GeVjj<M
hcorPt1RunbyRun
Entries 148Mean 74.02RMS 43.56
Run Number
1351
4913
5528
1355
7513
7028
1387
4613
8747
1387
5013
8919
1389
2113
9020
1390
9813
9100
1391
0313
9195
1392
3913
9347
1393
6513
9368
1393
7013
9372
1394
0713
9411
1394
5813
9781
1399
6713
9969
1399
7113
9973
1400
5814
0059
1401
2314
0124
1401
2614
0158
1401
5914
0160
1403
3114
0361
1403
6214
0379
1403
8214
0383
1403
8514
0387
1404
0114
1956
1419
5714
1958
1419
5914
1960
1419
6114
2035
1420
3614
2038
1420
4014
2076
1421
2914
2130
1421
3214
2135
1421
3614
2137
1421
8714
2188
1421
8914
2191
1422
6414
2265
1423
0314
2305
1423
0814
2309
1423
1114
2312
1423
1314
2317
1423
1814
2319
1424
1314
2414
1424
1514
2418
1424
1914
2420
1424
2214
2513
1425
1414
2524
1425
2514
2528
1425
3014
2535
1425
3714
2557
1425
5814
2659
1426
6014
2661
1426
6214
2663
1426
6414
2928
1429
3314
2935
1429
5314
2954
1429
7014
2971
1430
0414
3005
1430
0614
3007
1430
0814
3179
1431
8114
3187
1431
9114
3192
1431
9314
3318
1433
1914
3320
1433
2114
3322
1433
2614
3327
1433
2814
3657
1436
6514
3727
1437
3114
3827
1438
3314
3835
1439
5314
3954
1439
5514
3956
1439
5714
3959
1439
6014
3961
1439
6214
4011
1440
8614
4089
1441
1214
4114
(GeV
)T
Aver
age
Jet P
40
60
80
100
120
140
160
180
200
220
hcorPt1RunbyRun
Entries 148Mean 74.02RMS 43.56
corpt1RunbyRun
hcorPt2RunbyRun
Entries 148Mean 74.05RMS 43.57
HLT_Jet50U|<1.3η Δ|<2.5 & |
2η,
1η|
>220 GeVjjM
> = 143.0 GeVLeading JetT<P
> = 114.8 GeVSecond JetT<P
-1Runs with L>1 nb
Sertac Ozturk
Dijet Mass and QCD
12
• The data is in good agreement with the full CMS simulation of QCD from PYTHIA.
Dijet Mass (GeV)500 1000 1500 2000
/dm
(pb/
GeV
)σd
-310
-210
-110
1
10
210
310
410
)-1CMS Data (2.875 pbQCD Pythia + CMS Simulation10% JES Uncertainty
= 7 TeVs | < 1.3η Δ| < 2.5 & |2η, 1η|
>220 GeVjjM
Anti-kt R=0.7 CaloJets
Dijet Mass (GeV)500 1000 1500 2000
Dat
a / P
YTH
IA
1
2
3
4
5
6
1
2
3
4
5
6
)-1CMS Data (2.875 pb
10% JES Uncertainty
= 7 TeVs | < 1.3 | < 2.5 & |2, 1|
>220 GeVjjM
Anti-kt R=0.7 CaloJets
Sertac Ozturk
Dijet Mass and Fit• We fit the data to a function containing 4 parameters used by
CDF Run 11
13
Pulls
Dijet Mass (GeV)500 1000 1500 2000
/dm
(pb/
GeV
)σd
-310
-210
-110
1
10
210
310
410 / ndf 2χ 32.33 / 31
Prob 0.4011p0 5.398e-08± 2.609e-06 p1 0.1737± 5.077 p2 0.006248± 6.994 p3 0.001658± 0.2658
/ ndf 2χ 32.33 / 31Prob 0.4011p0 5.398e-08± 2.609e-06 p1 0.1737± 5.077 p2 0.006248± 6.994 p3 0.001658± 0.2658
)-1CMS Data (2.875 pb
Fit
= 7 TeVs | < 1.3η Δ| < 2.5 & |
2η,
1η|
>220 GeVjjM
Anti-kt R=0.7 CaloJets
Graph
Dijet Mass (GeV)500 1000 1500 2000
(Dat
a-Fi
t)/Fi
t
-1
0
1
2
Graph
)-1CMS Data (2.875 pb
= 7 TeVs
Dijet Mass (GeV)500 1000 1500 2000
(Dat
a-Fi
t)/Er
ror
-3
-2
-1
0
1
2
3 )-1CMS Data (2.875 pb
= 7 TeVs
5. Measurament of Dijet Mass Spectrum Sertac Ozturk
QCD MC prediction is in good agreement with the data.
Dijet Mass (GeV)500 1000 1500 2000
Dat
a / P
YTH
IA
1
2
3
4
5
6
1
2
3
4
5
6
)-1CMS Data (2.875 pb
10% JES Uncertainty
= 7 TeVs | < 1.3 | < 2.5 & |2, 1|
>220 GeVjjM
Anti-kt R=0.7 CaloJets
Figure 5.11 The dijet mass spectrum data (points) divided by the QCD PYTHIA prediction.
The band shows the sensitivity to a 10% systematic uncertainty on the jet energy scale.
The data points and corresponding uncertainty are listed in Table 5.X.
5.2.1 Dijet Mass Spectrum and Fit
Dijet mass spectrum is compared to a fit in Fig.5.X. The parametrization of smooth fit
function is
dσdm
= p0(1−X)p1
X p2+p3 ln(X) (5.2)
where x = m j j/√
s and p0,1,2,3 are free parameters. The (1−X) term is motivated by
the parton distribution fall of with fractional momentum. The X−p3 ln(x) factor describes
34
5. Measurament of Dijet Mass Spectrum Sertac Ozturk
QCD MC prediction is in good agreement with the data.
Dijet Mass (GeV)500 1000 1500 2000
Dat
a / P
YTH
IA
1
2
3
4
5
6
1
2
3
4
5
6
)-1CMS Data (2.875 pb
10% JES Uncertainty
= 7 TeVs | < 1.3 | < 2.5 & |2, 1|
>220 GeVjjM
Anti-kt R=0.7 CaloJets
Figure 5.11 The dijet mass spectrum data (points) divided by the QCD PYTHIA prediction.
The band shows the sensitivity to a 10% systematic uncertainty on the jet energy scale.
The data points and corresponding uncertainty are listed in Table 5.X.
5.2.1 Dijet Mass Spectrum and Fit
Dijet mass spectrum is compared to a fit in Fig.5.X. The parametrization of smooth fit
function is
dσdm
= p0(1−X)p1
X p2+p3 ln(X) (5.2)
where x = m j j/√
s and p0,1,2,3 are free parameters. The (1−X) term is motivated by
the parton distribution fall of with fractional momentum. The X−p3 ln(x) factor describes
34
Sertac Ozturk
Another Fit Parametrization
• In addition to the default fit, 2 alternate functional forms are considered.
• Default 4 parameters fit gives best result.
14
Dijet Mass (GeV)500 1000 1500 2000
/dm
(pb/
GeV
)σd
-310
-210
-110
1
10
210
310
410
/ ndf 2χ 32.33 / 31
p0 5.398e-08± 2.609e-06
p1 0.1737± 5.077
p2 0.006248± 6.994
p3 0.001658± 0.2658
/ ndf 2χ 32.33 / 31
p0 5.398e-08± 2.609e-06
p1 0.1737± 5.077
p2 0.006248± 6.994
p3 0.001658± 0.2658
)-1Data (2.875 pbDefault Fit (4 Par.)
Alternate Fit A (4 Par.)
Alternate Fit B (3 Par.)
= 7 TeVs | < 1.3η Δ| < 2.5 & |
2η,
1η|
>220 GeVjjM
Anti-kt R=0.7 CaloJets
Graph
χ2/NDF for Default Fit: 32.3 / 31 χ2/NDF for Fit A: 36.8 / 31χ2/NDF for Fit B: 39.3 / 32
2.8 Dijet Mass Spectrum and Fit 27
The parameterizations are listed in equation 3.371
dσ
dm=
P0 · (1−m/√
s )P1
mP2, (Default Fit with 3-parameters)
=P0
(P1 + m)P2, (Alternate Fit A with 3-parameters)
=P0 ·
�1−m/
√s + P3 · (m/
√s)2
�P1
mP2(Alternate Fit B with 4-parameters).
=P0 · (1−m
√s)p1
(m/√
s)p2+p3ln(m√
s)(Alternate Fit C with 4-parameters).
(3)
The default three parameter fit is motivated by QCD. It includes a power law fall off with mass372
in the denominator, motivated by the QCD matrix element. It also has a term in the numerator373
motivated by the parton distribution fall off with fractional momentum (1− m/√
s)P1 (where374 √s = 7000 GeV is the center-of-mass energy). This three parameter function was used by CDF375
in run IA. We find that the default fit gives a good χ2/DF of 17.1/18 (probability 52%), and this376
is the best fit we can find of our data.377
We have also explored three alternate parameterizations. All parameterizations have a power378
law in them, because without a power law we cannot get a good fit with only 2, 3 or 4 pa-379
rameters. A 2-parameter fit with just a power law and a constant, p0/mp1 , gives a reasonable380
fit χ2/DF = 19.3/19 (probabilty 44%), but we have been advised to only consider parame-381
terizations with the same number of parameters as our default fit or greater, in order to have382
reasonable flexibility in the fit parameterization. The 2-parameter fit has only one shape pa-383
rameter. Alternate fit A is a 3-parameter fit with a modified power law, obtained by simply384
adding an offset to the mass, and we get a good fit with χ2/DF = 17.9/18 (probability 46%).385
Alternate fit B is a 4-parameter fit very much like our default fit, but we have added a term386
quadratic in m/√
s to the term in the numberator to give the fit a little more flexibility to de-387
scribe data at high mass tails. This 4 parameter function was used by CDF in run IB [16]. We388
find that this function gives a good fit to our data, with χ2/DF of 16.8/17 (probability 47%).389
Alternate fit C is another 4 parameter function which again has our characteristic numerator390
and denominator but includes another term in the power of the power law, again just to give391
the fit more flexibiliity. This 4 parameter function was used by CDF in run II [14]. Again we392
find this function ives a good fit to our data, with χ2/DF of 16.8/17 (probability 47%).393
Figure 18 shows the fractional differences between data and the fit function, (data-fit)/fit, and394
the pulls, (data-fit)/error, for all four fits.395
Notice from both Fig. 17 and 18 that the largest difference from the default 3-parameter fit396
occurs when using the alternate fit A with 3 parameters. We will use this alternate 3-parameter397
function from fit A to find our systematic uncertainty on the background due to the fit parame-398
terization. Notice that there is very little difference between the default 3-parameter fit and the399
alternate 4-parameter fits which were introduced to give the 3-parameter fit more flexibility.400
From this we conclude that no more flexibility is needed to fit this data, and we have found the401
best possible smooth fit with a few parameters. When using these parameterizations to find402
systematic uncertainties on the background we do not find as large a systematic as with the403
alternate 3-parameter function.404
2.8 Dijet Mass Spectrum and Fit 27
The parameterizations are listed in equation 3.371
dσ
dm=
P0 · (1−m/√
s )P1
mP2, (Default Fit with 3-parameters)
=P0
(P1 + m)P2, (Alternate Fit A with 3-parameters)
=P0 ·
�1−m/
√s + P3 · (m/
√s)2
�P1
mP2(Alternate Fit B with 4-parameters).
=P0 · (1−m
√s)p1
(m/√
s)p2+p3ln(m√
s)(Alternate Fit C with 4-parameters).
(3)
The default three parameter fit is motivated by QCD. It includes a power law fall off with mass372
in the denominator, motivated by the QCD matrix element. It also has a term in the numerator373
motivated by the parton distribution fall off with fractional momentum (1− m/√
s)P1 (where374 √s = 7000 GeV is the center-of-mass energy). This three parameter function was used by CDF375
in run IA. We find that the default fit gives a good χ2/DF of 17.1/18 (probability 52%), and this376
is the best fit we can find of our data.377
We have also explored three alternate parameterizations. All parameterizations have a power378
law in them, because without a power law we cannot get a good fit with only 2, 3 or 4 pa-379
rameters. A 2-parameter fit with just a power law and a constant, p0/mp1 , gives a reasonable380
fit χ2/DF = 19.3/19 (probabilty 44%), but we have been advised to only consider parame-381
terizations with the same number of parameters as our default fit or greater, in order to have382
reasonable flexibility in the fit parameterization. The 2-parameter fit has only one shape pa-383
rameter. Alternate fit A is a 3-parameter fit with a modified power law, obtained by simply384
adding an offset to the mass, and we get a good fit with χ2/DF = 17.9/18 (probability 46%).385
Alternate fit B is a 4-parameter fit very much like our default fit, but we have added a term386
quadratic in m/√
s to the term in the numberator to give the fit a little more flexibility to de-387
scribe data at high mass tails. This 4 parameter function was used by CDF in run IB [16]. We388
find that this function gives a good fit to our data, with χ2/DF of 16.8/17 (probability 47%).389
Alternate fit C is another 4 parameter function which again has our characteristic numerator390
and denominator but includes another term in the power of the power law, again just to give391
the fit more flexibiliity. This 4 parameter function was used by CDF in run II [14]. Again we392
find this function ives a good fit to our data, with χ2/DF of 16.8/17 (probability 47%).393
Figure 18 shows the fractional differences between data and the fit function, (data-fit)/fit, and394
the pulls, (data-fit)/error, for all four fits.395
Notice from both Fig. 17 and 18 that the largest difference from the default 3-parameter fit396
occurs when using the alternate fit A with 3 parameters. We will use this alternate 3-parameter397
function from fit A to find our systematic uncertainty on the background due to the fit parame-398
terization. Notice that there is very little difference between the default 3-parameter fit and the399
alternate 4-parameter fits which were introduced to give the 3-parameter fit more flexibility.400
From this we conclude that no more flexibility is needed to fit this data, and we have found the401
best possible smooth fit with a few parameters. When using these parameterizations to find402
systematic uncertainties on the background we do not find as large a systematic as with the403
alternate 3-parameter function.404
2.8 Dijet Mass Spectrum and Fit 27
The parameterizations are listed in equation 3.371
dσ
dm=
P0 · (1−m/√
s )P1
mP2, (Default Fit with 3-parameters)
=P0
(P1 + m)P2, (Alternate Fit A with 3-parameters)
=P0 ·
�1−m/
√s + P3 · (m/
√s)2
�P1
mP2(Alternate Fit B with 4-parameters).
=P0 · (1−m
√s)p1
(m/√
s)p2+p3ln(m√
s)(Alternate Fit C with 4-parameters).
(3)
The default three parameter fit is motivated by QCD. It includes a power law fall off with mass372
in the denominator, motivated by the QCD matrix element. It also has a term in the numerator373
motivated by the parton distribution fall off with fractional momentum (1− m/√
s)P1 (where374 √s = 7000 GeV is the center-of-mass energy). This three parameter function was used by CDF375
in run IA. We find that the default fit gives a good χ2/DF of 17.1/18 (probability 52%), and this376
is the best fit we can find of our data.377
We have also explored three alternate parameterizations. All parameterizations have a power378
law in them, because without a power law we cannot get a good fit with only 2, 3 or 4 pa-379
rameters. A 2-parameter fit with just a power law and a constant, p0/mp1 , gives a reasonable380
fit χ2/DF = 19.3/19 (probabilty 44%), but we have been advised to only consider parame-381
terizations with the same number of parameters as our default fit or greater, in order to have382
reasonable flexibility in the fit parameterization. The 2-parameter fit has only one shape pa-383
rameter. Alternate fit A is a 3-parameter fit with a modified power law, obtained by simply384
adding an offset to the mass, and we get a good fit with χ2/DF = 17.9/18 (probability 46%).385
Alternate fit B is a 4-parameter fit very much like our default fit, but we have added a term386
quadratic in m/√
s to the term in the numberator to give the fit a little more flexibility to de-387
scribe data at high mass tails. This 4 parameter function was used by CDF in run IB [16]. We388
find that this function gives a good fit to our data, with χ2/DF of 16.8/17 (probability 47%).389
Alternate fit C is another 4 parameter function which again has our characteristic numerator390
and denominator but includes another term in the power of the power law, again just to give391
the fit more flexibiliity. This 4 parameter function was used by CDF in run II [14]. Again we392
find this function ives a good fit to our data, with χ2/DF of 16.8/17 (probability 47%).393
Figure 18 shows the fractional differences between data and the fit function, (data-fit)/fit, and394
the pulls, (data-fit)/error, for all four fits.395
Notice from both Fig. 17 and 18 that the largest difference from the default 3-parameter fit396
occurs when using the alternate fit A with 3 parameters. We will use this alternate 3-parameter397
function from fit A to find our systematic uncertainty on the background due to the fit parame-398
terization. Notice that there is very little difference between the default 3-parameter fit and the399
alternate 4-parameter fits which were introduced to give the 3-parameter fit more flexibility.400
From this we conclude that no more flexibility is needed to fit this data, and we have found the401
best possible smooth fit with a few parameters. When using these parameterizations to find402
systematic uncertainties on the background we do not find as large a systematic as with the403
alternate 3-parameter function.404
Default
Fit A
Fit B
Sertac Ozturk
Resonance Shapes• We have simulated dijet resonances using CMS simulation +
PYTHIA.
• qq, qg and gg resonances have different shape mainly due to FSR.
✓ The width of dijet resonance increases with number of gluons because gluons emit more radiation than quarks.
• We search for these three basic types of narrow dijet resonance in our data.
15Resonance Mass (GeV)500 1000 1500 2000 2500 3000 3500
/Mea
n)R
esol
utio
n (
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16 gluon-gluonquark-gluonquark-quark
[0]+[1]/x
Dijet Mass (GeV)400 600 800 1000 1200 1400 1600
Prob
abilit
y
0.05
0.1
0.15
0.2
0.25
gluon-gluonquark-gluonquark-quark
q* Resonance Shape
CMS Simulation
= 1.2 TeVResM
| < 1.3| < 2.5 & ||
Dijet Mass (GeV)500 1000 1500 2000 2500 3000
Prob
abilit
y
0
0.05
0.1
0.15
0.2
0.25q* Resonance Shape
0.5 TeV1 TeV
1.2TeV1.5 TeV
2 TeV
CMS Simulation qg)(q*
Paper
Sertac Ozturk
Dijet Mass (GeV)500 1000 1500 2000
/dm
(pb/
GeV
)d
-410
-310
-210
-110
1
10
210
310
410
/ ndf 2
32.33 / 31
Prob 0.4011
p0 5.398e-08± 2.609e-06
p1 0.1737± 5.077
p2 0.006248± 6.994
p3 0.001658± 0.2658
/ ndf 2
32.33 / 31
Prob 0.4011
p0 5.398e-08± 2.609e-06
p1 0.1737± 5.077
p2 0.006248± 6.994
p3 0.001658± 0.2658
)-1CMS Data (2.9 pbFit10% JES UncertaintyQCD Pythia + CMS SimulationExcited QuarkString = 7 TeVs
| < 1.3 | < 2.5 & ||
q* (0.5 TeV)
S (1 TeV)
q* (1.5 TeV)
S (2 TeV)
Fit and Signal• We search for dijet resonance signal in our data.
• Excited quark signals are shown at 0.5 TeV and 1.5 TeV.
• String resonance is shown at 1 TeV and 2 TeV.
16
Dijet Mass (GeV)500 1000 1500 2000
Dat
a / F
it1
10
Graph
q* (0.5 TeV)
S (1 TeV)
q* (1.5 TeV)
S (2 TeV)
)-1CMS Data (2.9 pb
= 7 TeVs
Paper Pap
er
Sertac Ozturk
The Largest Fluctuation in Data
• Upward fluctuations around 600 GeV and 900 GeV
• Best fit resonance is at 622 GeV with local significance 1.86 sigma from log likelihood ratio.
• There is no evidence for dijet resonance.
17
Dijet Mass (GeV)200 400 600 800 1000 1200 1400
(Dat
a-Fi
t)/Fi
t
-0.1
0
0.1
0.2
0.3
Graph
)-1CMS Data (2.875 pb
= 7 TeVs
q* (622 GeV)
(Background + Signal) Fit
Dijet Mass (GeV)500 1000 1500 2000
(Dat
a-Fi
t)/Er
ror
-3
-2
-1
0
1
2
3 )-1CMS Data (2.875 pb
= 7 TeVs
Sertac Ozturk
Setting Limits• For setting upper limit on the resonance production cross
section, a Bayesian formalism with a uniform prior is used.
L =�
i
µnii e−µi
ni!µi = αNi(S) + Ni(B).
• The signal comes from our dijet resonance shapes.
• The background comes from Background+Signal fit.
Measured # of events in data
# of event from signal
Expected # of event from background
18
Sertac Ozturk
Early Limits with Stat. Error Only
19
• 95% CL Upper limit with Stat. Error. Only compared to cross section for various model.
✓ Show quark-quark and quark-gluon and gluon-gluon resonances separately.
✓ gluon-gluon resonance has the lowest response and is the widest and gives worst limit.
Resonance Mass (GeV)500 1000 1500 2000 2500
Cro
ss S
ectio
n*BR
*Acc
. (pb
)
-110
1
10
210
310
410
510
)-1CMS Data (2.875 pb
= 7 TeVs | < 1.3 | < 2.5 & |
2,
1|
StringExcited quarkAxigluon
Diquark6EW’Z’RS Graviton
Gluon-GluonQuark-GluonQuark-Quark
Graph
95% CL Upper Limit
STAT. ERROR ONLYResonance Mass (GeV)500 1000 1500 2000 2500
A (p
b)×
BR
×
-110
1
10
210
310
410
510StringExcited quarkAxigluon / Coloron
Diquark6E
)-1CMS Data (2.875 pb
= 7 TeVs | < 1.3 | < 2.5 & ||
Smooth Limit with Stat. Error OnlySmooth Limit with 10% JES Uncertainty
Graph
(quark-gluon resonances)
Sertac Ozturk
Systematics
• We found the uncertainty in dijet resonance cross section from following sources.
✓ Jet Energy Scale (JES)
✓ Jet Energy Resolution (JER)
✓ Choice of Background Parametrization
✓ Luminosity
20
Sertac Ozturk
Jet Energy Scale (JES)• JetMET guidance is 10% uncertainty in jet energy scale.
✓ Shifting the resonance 10% lower in dijet mass gives more QCD background
✓ Increases the limit between 14% and 42% depending on resonance mass and type.
21
Resonance Mass (GeV)500 1000 1500 2000 2500 3000
Frac
t. C
hang
e on
Lim
it w
ith J
ES S
ys.
0.1
0.2
0.3
0.4
0.5
0.6
gluon-gluon
quark-gluon
quark-quark
)-1CMS Data (2.875 pb
= 7 TeVs
| < 1.3 | < 2.5 & |2
, 1
Jet |
Graph
Resonance Mass (GeV)500 1000 1500 2000 2500
A (p
b)×
BR
×
-110
1
10
210
310
410
510StringExcited quarkAxigluon / Coloron
Diquark6E
)-1CMS Data (2.875 pb
= 7 TeVs | < 1.3 | < 2.5 & ||
Smooth Limit with Stat. Error OnlySmooth Limit with 10% JES Uncertainty
Graph
(quark-gluon resonances)
Sertac Ozturk
Jet Energy Resolution (JER)• JetMET guidance is 10% uncertainty in jet energy resolution.
• We smear our resonance shapes with a gaussian designed to increase the core width by 10%.
✓ σGaus = √{(1.1)2-1} σRes
• This increases our limit between 7% and 22% depending on resonance mass and type.
22
Dijet Mass (GeV)200 400 600 800 1000 1200 1400 1600
Prob
abilit
y
0
0.02
0.04
0.06
0.08
0.1
0.12 Orginal
Convoluted
q* Resonance Shape
=1.2 TeVResM
gg)(G
Resonance Mass (GeV)500 1000 1500 2000 2500 3000
Frac
t. C
hang
e on
Lim
it w
ith J
ER S
ys.
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
gluon-gluon
quark-gluon
quark-quark
)-1CMS Data (2.875 pb
= 7 TeVs
| < 1.3 | < 2.5 & |2
, 1
Jet |
Graph
Sertac Ozturk
Background Parametrization Systematics
• We have varied the choice of background parametrization
• We use the 4 parameter fit as a systematic on our background shape.
• This increases our limit between 8% and 19% depending on resonance mass and type.
23
Dijet Mass (GeV)500 1000 1500 2000
/dm
(pb/
GeV
)σd
-310
-210
-110
1
10
210
310
410
/ ndf 2χ 32.33 / 31
p0 5.398e-08± 2.609e-06
p1 0.1737± 5.077
p2 0.006248± 6.994
p3 0.001658± 0.2658
/ ndf 2χ 32.33 / 31
p0 5.398e-08± 2.609e-06
p1 0.1737± 5.077
p2 0.006248± 6.994
p3 0.001658± 0.2658
)-1Data (2.875 pbDefault Fit (4 Par.)
Alternate Fit A (4 Par.)
Alternate Fit B (3 Par.)
= 7 TeVs | < 1.3η Δ| < 2.5 & |
2η,
1η|
>220 GeVjjM
Anti-kt R=0.7 CaloJets
Graph
Resonance Mass (GeV)500 1000 1500 2000 2500 3000
Frac
t. C
hang
e on
Lim
it w
ith B
gk S
ys.
0
0.05
0.1
0.15
0.2
0.25
0.3
gluon-gluon
quark-gluon
quark-quark
)-1CMS Data (2.875 pb
= 7 TeVs
| < 1.3 | < 2.5 & |2
, 1
Jet |
Graph
Sertac Ozturk
Total Systematic Uncertainties• We add all mentioned systematic uncertainties in quadrature, also 11% for
luminosity.
• JEC is dominant systematic uncertainty.
• Total systematic uncertainty varies from 24% to 48% depending on resonance mass and type.
24
Resonance Mass (GeV)500 1000 1500 2000 2500 3000
Frac
tiona
l Unc
erta
inty
0.1
0.2
0.3
0.4
0.5
0.6
0.7 TotalJESResolutionBackgroundLuminosity
)-1CMS Preliminary (2.875 pb
= 7 TeVs
| < 1.3 | < 2.5 & |2
, 1
|
Graph
qg)(q*
Resonance Mass (GeV)500 1000 1500 2000 2500 3000
Frac
tiona
l Unc
erta
inty
0.1
0.2
0.3
0.4
0.5
0.6
0.7gluon-gluon
quark-gluon
quark-quark
)-1CMS Data (2.875 pb
= 7 TeVs
| < 1.3 | < 2.5 & |2
, 1
Jet |
Graph
Sertac Ozturk
Incorporating Systematic• We convolute posterior PDF with Gaussian systematics
uncertainties.
✓ Posterior PDF including systematics is broader and gives higher upper limit.
25
30 4 Systematic Uncertainties
4.3 Background Parameterization398
We considered two others functional forms with 2 and 4 parameters to parametrize the QCD399
background as discussed in section 2.6.1 and shown in Equation 3. Fig. 25 show comparison400
of fits with the data points. We find that the 2 parameter form, which is a marginal fit to our401
data, gives the largest fractional change over the vast majority of resonance masses, and we402
conservatively use it for our background parametrization systematic at this time.403
Dijet Mass (GeV)200 400 600 800 1000 1200 1400
/dm
(pb/
GeV
)!d
-110
1
10
210
310
410
/ ndf 2" 11.25 / 16
Prob 0.7937
p0 0.0003714± 0.001181
p1 1.885± 23.43
p2 0.07701± 4.489
/ ndf 2" 11.25 / 16
Prob 0.7937
p0 0.0003714± 0.001181
p1 1.885± 23.43
p2 0.07701± 4.489
)-1Data (7.2 nb
Default Fit with 3 Par.
Fit with 4 Par.
Fit with 2 Par.
= 7 TeVs|<1.3#|Jet
>156 GeVjjM
Graph
)-1CMS Data (7.2 nb
Default Fit with 3 Par.Fit with 4 Par.Fit with 2 Par.
400 600 800 1000 1200 1400 1600
Frac
t. C
hang
e on
Lim
it w
ith B
gk S
ys.
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
gluon-gluonquark-gluonquark-quark
Graph
)-1CMS Data (7.2nb=7 TeVs
|<1.3!|Jet
Figure 25: Left) The data and the default 3 parameter fit and the 2 and 4 parameter fits use toevaluate the systematics. Right) Fractional absolute change in the limit when using th 2 and 4parameter fits for the background.
4.4 Total Uncertainty404
We determine 1σ change for each systematic uncertainty in signal that we can discovery or405
exclude. In addition to the sources already mentioned, we include an uncertainty of 10% on406
the integrated luminosity.407
To find total total systematics, we add the these 1σ changes as quadrature. The individual and408
total systematic uncertainties as a function of resonance mass are illustrated in Fig. 26. Absolute409
uncertainty in each resonance mass is calculated as total systematics uncertainty multiply by410
upper cross section limit.411
4.5 Incorporating Systematics in the Limit412
We convolute the posterior probability density with a Gaussian for each resonance mass. The413
equation of convolution is414
L(σ) =� ∞
0L(σ�)G(σ, σ�)dσ� (7)
Where L(σ�) is the posterior probability density at signal cross section σ�, and G(σ, σ�) is the415
Gaussian probability from systematics to observe σ if σ� is expected. The width of the Gaussian416
is taken as the absolute uncertainty in each resonance mass, equal to the fractional uncertainty417
times the limit on the cross section. This procedure, identical to what was done at CDF, con-418
servatively assigns the same width to the Gaussian in units of pb at each point in the posterior419
G: Gaussian distribution withRMS width equal to systematic
uncertainty in cross section
Cross Section (pb)0 10 20 30 40 50 60 70 80
Post
erio
r PD
F
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
Stat. Error Only
Including Systematic
Likelihood vs sigma
= 1 TeVq*M
Sertac Ozturk
Effect of Systematics on Limit
26
• 95% CL Upper limit with Stat. Error. Only and Including Sys. Uncertainties are shown separately.
• The mass limits are reduced by 0.1 TeV for both string resonance and excited quark.
Resonance Mass (GeV)500 1000 1500 2000 2500
Cro
ss S
ectio
n*BR
*Acc
. (pb
)
-110
1
10
210
310
410
510
610
)-1CMS Data (2.875 pb
= 7 TeVs
| < 1.3 | < 2.5 & |2
, 1
|
String
Excited quarkIncluding SystematicsStat. Error Only
Graph
95% CL Upper Limit
qg)(q*
Resonance Mass (GeV)500 1000 1500 2000 2500
Frac
t. C
hang
e In
clud
ing
Sys.
0.1
0.2
0.3
0.4
0.5
0.6
0.7
gluon-gluonquark-gluonquark-quark
)-1CMS Data (2.875 pb
= 7 TeVs | < 1.3 | < 2.5 & |2, 1|
Graph
Sertac Ozturk
Results
27
• We excluded the mass limits as following:
• String
✓ 0.50<M(S)<2.50 TeV
‣ M(S)<1.40 from CDF
• Excited Quark
✓ 0.50<M(q*)<1.58 TeV
‣ 0.40<M(q*)<1.26 from ATLAS
• Axigluon/Coloron
✓ 0.50<M(A)<1.17 TeV & 1.47<M(A)<1.52 TeV
‣ 0.12<M(A)<1.25 TeV from CDF
• E6 Diquark
✓ 0.50<M(D)<0.58 TeV & 0.97<M(D)<1.08 TeV & 1.45<M(D)<1.60 TeV
‣ 0.29<M(D)<0.63 TeV from CDF
Resonance Mass (GeV)500 1000 1500 2000 2500
A (p
b)×
BR
×
-210
-110
1
10
210
310
410
510 )-1CMS Data (2.9 pb
= 7 TeVs | < 1.3 | < 2.5 & ||
StringExcited QuarkAxigluon/Coloron
Diquark6EW’Z’RS Graviton
Gluon-GluonQuark-GluonQuark-Quark
Graph
Resonance Models
95% CL Upper Limit
Paper
Sertac Ozturk
Expected Limit• Due to downward fluctuation around 1.2 TeV, measured limit
is 250 GeV grater than expected limit for excited quark.
• Due to upward fluctuation around 600 GeV, we lost sensitivity to a E6 diquark at low resonance mass.
28
Resonance Mass (GeV)500 1000 1500 2000 2500
Cro
ss S
ectio
n*BR
*Acc
. (pb
)
1
10
210
310
410
)-1CMS Data (2.875 pb
= 7 TeVs
| < 1.3 | < 2.5 & |2
, 1
|
Axigluon/Coloron
Diquark6EObserved
Expected
Graph
95% CL Upper Limit
(quark-quark resonances)
Resonance Mass (GeV)500 1000 1500 2000 2500
Cro
ss S
ectio
n*BR
*Acc
. (pb
)
-110
1
10
210
310
410
510
610
)-1CMS Data (2.875 pb
= 7 TeVs
| < 1.3 | < 2.5 & |2
, 1
|
String
Excited quarkObserved
Expected
Graph
95% CL Upper Limit
(quark-gluon resonances)
Sertac Ozturk
Conclusion• We have a dijet mass spectrum that extends to
2 TeV GeV with ~2.9 pb-1 data.
• The dijet mass data is in good agreement with a full CMS simulation of QCD from PYTHIA.
• There is no evidence for dijet resonances
• We extended excluded mass limit for dijet resonance models beyond Tevatron and ATLAS.
• First CMS research paper based on these result will be submitted to PRL in this week.
29
Sertac Ozturk
Back-up
30
Sertac Ozturk
Beam Splash 2009
• I spent some effort to determine HF calibration constant in 2009 Beam splash data.
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Sertac Ozturk
Estimation of IFB Signal
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6/21/10 Latife Vergili, Shuichi Kunori
HPD Ion feedback & pulse shape (ROC PFG Report)
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w/o ion feedback w/ ion feedback
0.8
Mean 0.88
Low energy
High energy Ion feedback rate: 2 e-4 per p.e. 3 e-3 per GeV ~1% additional energy (in CMSSW 360 pre3 w/o time structure)
2ts/10ts
Pulse Shape without/with ion feedback
time
time time
time
hLedPulse0HBPall
Entries 6122030
Mean 3.868
RMS 1.738
TS0 1 2 3 4 5 6 7 8 9
0
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hLedPulse0HBPall
Entries 6122030
Mean 3.868
RMS 1.738
h_ifbEntries 10Mean 4.537RMS 1.489
LED HBP pulse 0
Charge<40 fCCharge>60 fc
Estimated Ion FeedbackhLed_plus_HB_iphi_15
Entries 632898Mean 30.45RMS 4.144
Charge (fC)0 20 40 60 80 100
1
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510hLed_plus_HB_iphi_15
Entries 632898Mean 30.45RMS 4.144
LED fcsum
P1 P2 P3
=15i=0,...,16i
IFB Signal = Signal in P3 - Signal in P1
• Ion feedback signal starts 1 TS later.
Sertac Ozturk
PFG task N.9 - Investigations on HBP14/4 operating at 40V
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• Compare Pulse Shape in 80 BV, 40 BV and 30 BV
• HBP14_RM4
✓ iphi:54, depth:1, zside>0 and ieta:1,2,3, ..., 16
HBHE uses 4 TS for reconstruction.Containment becomes worse as we lower the BV.
Sertac Ozturk
Jet Commissioning with First Data
• I looked at JetID cuts criteria in 2009 MinimumBias Collision Data. (CMS AN-2010/030 & PAS JME 10-001)
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4 Survival fraction for loose jet ID selection criteria in dijet eventsEvents having dijet topology are greatly enriched in real jets. For this reason, the same cuts on jet variables passa much higher fraction of reconstructed hadronic jets in these events than they do in the super-sample of inclusivejets. Here we present the fractional survival fractions for the two leading jets in data and in Monte Carlo.
Within this sample set, we define a dijet topology as one in which the two jets leading in transverse momentummeet a loose cut of being back-to-back in azimuth to within 1 radian. This is done to extract the set of events mostlikely to contain the parton- parton scattering events. Some such di-jet events include more than two jets, but theyare ignored in this section to focus on the di-jet itself.
For the leading two jets comprising a dijet the following four figures show the fractional survival fractions as afunction of corrected jet pT to pass cuts of Fig. 13 n90hits > 1, Fig. 14 EMF > 0.01, and Fig. 15 fHPD < 0.98,separately.
The same survival fraction measure for all cuts simultaneously is given in Fig. 16.
In all four cases:
Left: For the numerator we count the number of jets for which both jets in the dijet event pass the cut, and for thethe denominator we count the number of jets in all dijets before cuts.
Right: For the numerator we count the number of jets which pass the cut, and for the denominator we count thenumber of jets in all dijets before cuts.
(GeV)tJet P10 20 30 40 50 60
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n90hist Cut Efficieny
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n90hits>1
=900 GeVs|<3!|Jet
n90hist Cut Efficieny
Figure 13: Dijet N90Hits> 1 cut survival fraction as a function of corrected jet pT . Left: The cut is required forboth leading jets. Right: The cut is required jet-by-jet for the two leading jets..
Comparing the survival fractions for jets comprising dijets to those of inclusive jets we see that in the dijet case,survival fractions are much improved across the whole pT range for which statistics are available.
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(GeV)tJet P10 20 30 40 50 60
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|>2.6!EMF>0.01 or |Jet
=900 GeVs|<3!|Jet
Minimal EMF Cut Efficieny
(GeV)tJet P10 20 30 40 50 60
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|>2.6!EMF>0.01 or |Jet
=900 GeVs|<3!|Jet
Minimal EMF Cut Efficieny
Figure 14: Dijet Minimal EMF cut survival fraction as a function of corrected jet pT . Left: The cut is required forboth leading jets. Right: The cut is required jet-by-jet for the two leading jets.
(GeV)tJet P10 20 30 40 50 60
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fHPD<0.98
=900 GeVs|<3!|Jet
fRBX<0.98 & fHPD<0.98 Cut Efficieny
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fHPD<0.98
=900 GeVs|<3!|Jet
fRBX<0.98 & fHPD<0.98 Cut Efficieny
Figure 15: Dijet fHPD cut survival fraction as a function of corrected jet pT . Left: The cut is required for bothleading jets. Right: The cut is required jet-by-jet for the two leading jets.
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(GeV)tJet P10 20 30 40 50 60
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MCCMS Preliminary
|>2.6!EMF>0.01 or |Jet
=900 GeVs|<3!|Jet
Minimal EMF Cut Efficieny
Figure 14: Dijet Minimal EMF cut survival fraction as a function of corrected jet pT . Left: The cut is required forboth leading jets. Right: The cut is required jet-by-jet for the two leading jets.
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fHPD<0.98
=900 GeVs|<3!|Jet
fRBX<0.98 & fHPD<0.98 Cut Efficieny
Figure 15: Dijet fHPD cut survival fraction as a function of corrected jet pT . Left: The cut is required for bothleading jets. Right: The cut is required jet-by-jet for the two leading jets.
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(GeV)tJet P10 20 30 40 50 60
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Loose Cut
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Loose Cut
=900 GeVs|<3!|Jet
All Loose Cuts Efficieny
Figure 16: The loose cut survival fraction as a function of corrected jet pT . Left: The cut is required for bothleading jets. Right: The cut is required jet-by-jet for the two leading jets.
(GeV)tJet P10 20 30 40 50 60
Jets
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Without Cut
The Loose Cut
=900 GeVs|<3!|Jet
Jet Pt without Cut
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Without Cut
The Loose Cut
=900 GeVs|<3!|Jet
Jet Pt without Cut
Figure 17: The pT spectrum among jets comprising dijets where the loose cut is required Left: on both jets togetherand Right: on each jet independently.
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