Update on MultiJet Studies
Sunanda BanerjeeManjit KaurRuchi Gupta
1Tuesday, December 20, 2011
Outline
• Introduction
• DataSets Used
• Event Selection
• PileUp Studies
• Choice Of Binning
• Data/MC Comparison
• Detector Unfolding
• Summary and Outlook
2Tuesday, December 20, 2011
• 3-Jet variables:‣ Invariant mass of 3-jet system‣ Scaled energies: ordered in jet c.m. frame:
Variable definitions1+2→ 3+4+5
• 4-Jet variables:‣ Invariant mass of 4-jet system‣ Scaled Energies: ‣ Bengtsson-Zerwas angle: Angle between
planes containing the two leading jets and the two non leading jets.
‣ Nachtmann-Reiter angle: Angle between the momentum vector difference of the two leading jets and the two non-leading jets:
1+2→ 3+4+5+6
3Tuesday, December 20, 2011
Datasets Used
• Data: ‣ /Jet/Run2011A-05Aug2011-v1/AOD ‣ /Jet/Run2011A-May10ReReco-v1/AOD ‣ /Jet/Run2011B-PromptReco-v1/AOD ‣ /Jet/Run2011A-PromptReco-v4/AOD ‣ /Jet/Run2011A-PromptReco-v6/AOD
• JSON Files Used:‣ Cert_160404-163869_7TeV_May10ReReco_Collisions11_JSON_v3.txt 216.149 pb-1
‣ Cert_170249-172619_7TeV_ReReco5Aug_Collisions11_JSON_v3.txt 368.037 pb-1
‣ Cert_160404-180252_7TeV_PromptReco_Collisions11_JSON.txt 4.638 fb-1
4Tuesday, December 20, 2011
MC Datasets• Pythia6TuneZ2 ‣ /QCD_Pt-*_TuneZ2_7TeV_pythia6/Summer11-PU_S3_START42_V11-v2/AODSIM
• Pythia8Tune4C‣ /QCD_Pt-*_Tune4C_7TeV_pythia8/Summer11-PU_S3_START42_V11-v2/AODSIM
•MadGraph‣ /QCD_TuneZ2_HT-100To250_7TeV-madgraph/Summer11-PU_S4_START42_V11-v1/
AODSIM‣ /QCD_TuneZ2_HT-250To500_7TeV-madgraph/Summer11-PU_S4_START42_V11-v3/
AODSIM‣ /QCD_TuneZ2_HT-500To1000_7TeV-madgraph/Summer11-PU_S4_START42_V11-v1/
AODSIM‣ /QCD_TuneZ2_HT-1000_7TeV-madgraph/Summer11-PU_S4_START42_V11-v1/
AODSIM
5Tuesday, December 20, 2011
Select inclusive 3(4)-Jet samples• Use HLT- Jet110 sequence• Use the following filters:‣HLT physics declared ‣Beam scraping‣Number of Good Primary Vertices (non-fake & ndof>4) > 0
• MET/SumET < 0.3• Leading Jet pT > 140 GeV• Other Jets pT > 50 GeV• Binning in η done on the basis of highest η jet‣Five η bins of width 0.5 from η=0.0 to η=2.5
Selection Criteria
• Boost the leading (pT) 3(4)-Jets to their CM frame
• Order them according to their energies• Compute the 3(4)-jet variables
6Tuesday, December 20, 2011
Effect of PileUp
• Use 2011A data
• Divide the events according to number of good primary vertex (<4, 4-6, 7-10, >10)
7Tuesday, December 20, 2011
-310
-210
-110
Entries 7533Mean 509.1RMS 210.9Entries 16125Mean 507.6RMS 214.3Entries 7409Mean 505.1RMS 211.8Entries 31564Mean 507.5RMS 213.1
nGoodPV<4
3<nGoodPV<7
6<nGoodPV<11
10<nGoodPV
3Jet Mass0 500 1000 1500 2000 2500 30000
0.51
1.520
0.51
1.520
0.51
1.52 0
0.02
0.04
0.06
0.08
0.1Entries 7508Mean 0.6902RMS 0.09441Entries 16100Mean 0.6894RMS 0.09468Entries 7384Mean 0.6907RMS 0.09426Entries 31539Mean 0.69RMS 0.09456
nGoodPV<4
3<nGoodPV<7
6<nGoodPV<11
10<nGoodPV
4x0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 10
0.51
1.520
0.51
1.520
0.51
1.52
3-Jet Variables
Difference among the distributions are within statistical uncertainty
Mass x4
8Tuesday, December 20, 2011
-410
-310
-210
-110Entries 1087Mean 706.9RMS 276.8Entries 2429Mean 703.8RMS 278.5Entries 1164Mean 696.8RMS 270.8Entries 4691Mean 702.2RMS 275.7
nGoodPV<4
3<nGoodPV<7
6<nGoodPV<11
10<nGoodPV
4Jet Mass0 500 1000 1500 2000 2500 30000
0.51
1.520
0.51
1.520
0.51
1.52
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Entries 694Mean 0.7116RMS 0.4313Entries 1573Mean 0.6881RMS 0.4231Entries 717Mean 0.6987RMS 0.418Entries 3008Mean 0.6967RMS 0.4246
nGoodPV<4
3<nGoodPV<7
6<nGoodPV<11
10<nGoodPV
BZ
0 0.2 0.4 0.6 0.8 1 1.2 1.400.5
11.5
200.5
11.5
200.5
11.5
2
4-Jet Variables
No significant effect due to pileup in the inclusive 3- or 4-jet variables
Mass χBZ
9Tuesday, December 20, 2011
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24 Mean 0.8954
RMS 0.07444
Mean 0.8953
RMS 0.07461
PUreWtNoPUreWt
3x0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
PUre
Wt/N
oPU
reW
t
0.50.60.70.80.9
11.11.21.31.41.5
HLT80
0 06
0.07
0.08
0.09
0.1
0.11
0.12
0.13Mean 0.5345
RMS 0.2894
Mean 0.5362
RMS 0.2895
PUreWtNoPUreWt
)NRcos(0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
PUre
Wt/N
oPU
reW
t
0.50.60.70.80.9
11.11.21.31.41.5
HLT80
Cos(θNR)
No appreciable effect observed
x3
Monte Carlo with and without PU re-weighting
Use PYTHIA6 Z2 tuned sample with and without pileup re-weighting relevant for the EPS data set on 3- and 4-Jet variables
Leading Jet pT>110GeV
10Tuesday, December 20, 2011
-610
-510
-410
-310
-210
-110Entries 1.774797e+08
Mean 512.5
RMS 217.2
Entries 1.774797e+08
Mean 512.4
RMS 217.1
PU (nPV)PU (truth)
3Jet Mass0 500 1000 1500 2000 2500 3000
nPV/
trut
h
0.80.85
0.90.95
11.05
1.11.15
1.2 0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18 Entries 1.774797e+08
Mean 0.4133
RMS 0.119
Entries 1.774797e+08
Mean 0.4134
RMS 0.119
PU (nPV)PU (truth)
5x0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
nPV/
trut
h
0.80.85
0.90.95
11.05
1.11.15
1.2
3-Jet Variables
• Monte Carlo PileUp re-weighting using the number of good reconstructed Primary Vertices from both data and MC
• Also use the true number of pileup events in Monte Carlo and luminosity in the data to define alternate way of reweighting
• Both methods yield identical results
Mass x5
11Tuesday, December 20, 2011
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07Entries 2.939321e+07
Mean 0.789
RMS 0.1099
Entries 2.939321e+07
Mean 0.789
RMS 0.1099
PU (nPV)PU (truth)
3x0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
nPV/
trut
h
0.80.85
0.90.95
11.05
1.11.15
1.2 0
0.02
0.04
0.06
0.08
0.1
0.12
0.14 Entries 2.939321e+07
Mean 0.2471
RMS 0.07538
Entries 2.939321e+07
Mean 0.2471
RMS 0.07538
PU (nPV)PU (truth)
6x0 0.1 0.2 0.3 0.4 0.5nP
V/tr
uth
0.80.85
0.90.95
11.05
1.11.15
1.2
4-Jet Variables
Two reweighting methods lead the same results
x6x3
12Tuesday, December 20, 2011
Detector Resolution
• Study using MC sample by comparing detector level with generator level distribution.
• Each detector level distribution is divided into 10 bins and in each bin, find the difference between detector and generator level quantities.
• Fit each histogram to Gaussian and the width of the fit is plotted as a function of the variable.
• Study all 3- and 4-jet variables.
• The final binning is decided such that the bin width is greater than the resolution and there is enough statistics in each bin.
13Tuesday, December 20, 2011
Resolution Studies for 3Jet Mass (0.0<|η|<0.5)
14Tuesday, December 20, 2011
3Jet Mass Resolution in different η bins
Final choice of binnings is 50 GeV upto 1000 GeV and 100 GeV beyond that.
15Tuesday, December 20, 2011
Resolution Studies for 4Jet CosθNR (0.0<|η|<0.5)
16Tuesday, December 20, 2011
4Jet CosθNR Resolution in different η bins
Constant bin width of 0.1 is used.
17Tuesday, December 20, 2011
Data/MC Comparison
Detector level measurements are compared with predictions from 3 different event generators: Pythia6 (TuneZ2), Pythia8 (Tune4C), MadGraph
18Tuesday, December 20, 2011
-510
-410
-310
-210
-110
Entries 24476Mean 540.2RMS 183.8
Entries 24476Mean 540.2RMS 183.8Entries 2615457Mean 516.4RMS 181
Entries 2615457Mean 516.4RMS 181Entries 3093622Mean 514.3RMS 177.8
Entries 3093622Mean 514.3RMS 177.8
Entries 1889664Mean 509.7RMS 183.8
Entries 1889664Mean 509.7RMS 183.8
|<1.000.50<|
DataPythia6Pythia8Madgraph
3Jet Mass0 500 1000 1500 2000 2500 3000
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
-510
-410
-310
-210
-110
Entries 94836Mean 569.5RMS 228.6
Entries 94836Mean 569.5RMS 228.6Entries 1.43193e+07
Mean 549.6
RMS 221.6
Entries 1.43193e+07
Mean 549.6
RMS 221.6
Entries 1.726803e+07
Mean 548.6
RMS 222.3
Entries 1.726803e+07
Mean 548.6
RMS 222.3
Entries 1.069183e+07
Mean 546.7
RMS 224.3
Entries 1.069183e+07
Mean 546.7
RMS 224.3
|<0.500.00<|
DataPythia6Pythia8Madgraph
3Jet Mass0 500 1000 1500 2000 2500 3000
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
-510
-410
-310
-210
-110
Entries 232623Mean 600.5RMS 241.4
Entries 232623Mean 600.5RMS 241.4Entries 4.21741e+07
Mean 577.7
RMS 233.5
Entries 4.21741e+07
Mean 577.7
RMS 233.5
Entries 5.075746e+07
Mean 578.4
RMS 234.5
Entries 5.075746e+07
Mean 578.4
RMS 234.5
Entries 3.049151e+07
Mean 572.3
RMS 236.8
Entries 3.049151e+07
Mean 572.3
RMS 236.8
All
DataPythia6Pythia8Madgraph
3Jet Mass0 500 1000 1500 2000 2500 3000
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
-610
-510
-410
-310
-210
-110Entries 33715Mean 573.5RMS 194.6
Entries 33715Mean 573.5RMS 194.6Entries 4242529Mean 520.6RMS 183.2
Entries 4242529Mean 520.6RMS 183.2Entries 5024893Mean 521.4RMS 184.1
Entries 5024893Mean 521.4RMS 184.1
Entries 3159395Mean 514.3RMS 183.3
Entries 3159395Mean 514.3RMS 183.3
|<1.501.00<|
DataPythia6Pythia8Madgraph
3Jet Mass0 500 1000 1500 2000 2500 3000
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
3 Jet Mass
0.1280.1440.153
0.1460.1860.200
0.3260.3380.358
0.1200.1300.156
19Tuesday, December 20, 2011
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14Entries 24457Mean 0.9308RMS 0.05747
Entries 24457Mean 0.9308RMS 0.05747Entries 2615438Mean 0.9118RMS 0.06547
Entries 2615438Mean 0.9118RMS 0.06547Entries 3093603Mean 0.9171RMS 0.06318
Entries 3093603Mean 0.9171RMS 0.06318Entries 1889645Mean 0.9247RMS 0.05917
Entries 1889645Mean 0.9247RMS 0.05917
|<1.000.50<|DataPythia6Pythia8Madgraph
3x0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14 Entries 94817Mean 0.9286RMS 0.05829
Entries 94817Mean 0.9286RMS 0.05829Entries 1.431928e+07
Mean 0.9143
RMS 0.06537
Entries 1.431928e+07
Mean 0.9143
RMS 0.06537
Entries 1.726801e+07
Mean 0.9194
RMS 0.06322
Entries 1.726801e+07
Mean 0.9194
RMS 0.06322
Entries 1.069181e+07
Mean 0.9266
RMS 0.059
Entries 1.069181e+07
Mean 0.9266
RMS 0.059
|<0.500.00<|
DataPythia6Pythia8Madgraph
3x0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
20
0.02
0.04
0.06
0.08
0.1
0.12
Entries 232604Mean 0.9276RMS 0.05936
Entries 232604Mean 0.9276RMS 0.05936Entries 4.217408e+07
Mean 0.9126
RMS 0.0662
Entries 4.217408e+07
Mean 0.9126
RMS 0.0662
Entries 5.075744e+07
Mean 0.9172
RMS 0.06436
Entries 5.075744e+07
Mean 0.9172
RMS 0.06436
Entries 3.049149e+07
Mean 0.926
RMS 0.05961
Entries 3.049149e+07
Mean 0.926
RMS 0.05961
All
DataPythia6Pythia8Madgraph
3x0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14 Entries 33696Mean 0.9273RMS 0.06053
Entries 33696Mean 0.9273RMS 0.06053Entries 4242510Mean 0.916RMS 0.06462
Entries 4242510Mean 0.916RMS 0.06462Entries 5024874Mean 0.9205RMS 0.06339
Entries 5024874Mean 0.9205RMS 0.06339Entries 3159376Mean 0.9285RMS 0.05809
Entries 3159376Mean 0.9285RMS 0.05809
|<1.501.00<|
DataPythia6Pythia8Madgraph
3x0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
3 Jet x3
0.2410.1570.038
0.3180.2450.127
0.1870.1110.055
0.2460.1760.035
20Tuesday, December 20, 2011
-510
-410
-310
-210
-110Entries 4208Mean 700.3RMS 232.5
Entries 4208Mean 700.3RMS 232.5Entries 424011Mean 685.9RMS 235.9
Entries 424011Mean 685.9RMS 235.9Entries 494811Mean 693.4RMS 235.3
Entries 494811Mean 693.4RMS 235.3
Entries 315679Mean 689RMS 253.3
Entries 315679Mean 689RMS 253.3
|<1.000.50<|
DataPythia6Pythia8Madgraph
4Jet Mass0 500 1000 1500 2000 2500 3000
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
-410
-310
-210
-110Entries 17084Mean 744RMS 273.9
Entries 17084Mean 744RMS 273.9Entries 2543579Mean 725.8RMS 282.3
Entries 2543579Mean 725.8RMS 282.3Entries 2991439Mean 735.5RMS 287.6
Entries 2991439Mean 735.5RMS 287.6
Entries 1881147Mean 723.6RMS 290
Entries 1881147Mean 723.6RMS 290
|<0.500.00<|
DataPythia6Pythia8Madgraph
4Jet Mass0 500 1000 1500 2000 2500 3000
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2-510
-410
-310
-210
-110Entries 47301Mean 780.2RMS 292
Entries 47301Mean 780.2RMS 292Entries 8420464Mean 761.6RMS 299.3
Entries 8420464Mean 761.6RMS 299.3Entries 9769959Mean 771.6RMS 309.2
Entries 9769959Mean 771.6RMS 309.2
Entries 6042338Mean 758.9RMS 309.5
Entries 6042338Mean 758.9RMS 309.5
All
DataPythia6Pythia8Madgraph
4Jet Mass0 500 1000 1500 2000 2500 3000
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
-510
-410
-310
-210
-110Entries 6632Mean 725.5RMS 236.2
Entries 6632Mean 725.5RMS 236.2Entries 712538Mean 685.5RMS 233.8
Entries 712538Mean 685.5RMS 233.8Entries 792705Mean 691.9RMS 237.7
Entries 792705Mean 691.9RMS 237.7
Entries 525262Mean 681.9RMS 244.6
Entries 525262Mean 681.9RMS 244.6
|<1.501.00<|
DataPythia6Pythia8Madgraph
4Jet Mass0 500 1000 1500 2000 2500 3000
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
4 Jet Mass
0.1080.0890.123
0.1100.1110.112
0.2160.209
0.256
0.0830.065
0.114
21Tuesday, December 20, 2011
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14 Entries 4173Mean 0.4935RMS 0.2637
Entries 4173Mean 0.4935RMS 0.2637
Entries 423976Mean 0.4678RMS 0.2605
Entries 423976Mean 0.4678RMS 0.2605Entries 494776Mean 0.4677RMS 0.2621
Entries 494776Mean 0.4677RMS 0.2621Entries 315644Mean 0.4893RMS 0.265
Entries 315644Mean 0.4893RMS 0.265
|<1.000.50<|
DataPythia6Pythia8Madgraph
)NRcos(0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14 Entries 17049Mean 0.494RMS 0.263
Entries 17049Mean 0.494RMS 0.263
Entries 2543544Mean 0.4745RMS 0.2634
Entries 2543544Mean 0.4745RMS 0.2634Entries 2991404Mean 0.4876RMS 0.2633
Entries 2991404Mean 0.4876RMS 0.2633Entries 1881112Mean 0.4907RMS 0.2644
Entries 1881112Mean 0.4907RMS 0.2644
|<0.500.00<|
DataPythia6Pythia8Madgraph
)NRcos(0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
20
0.02
0.04
0.06
0.08
0.1
0.12
0.14Entries 47266Mean 0.4985RMS 0.2639
Entries 47266Mean 0.4985RMS 0.2639
Entries 8420429Mean 0.481RMS 0.2633
Entries 8420429Mean 0.481RMS 0.2633Entries 9769924Mean 0.4935RMS 0.264
Entries 9769924Mean 0.4935RMS 0.264Entries 6042303Mean 0.4971RMS 0.2646
Entries 6042303Mean 0.4971RMS 0.2646
All
DataPythia6Pythia8Madgraph
)NRcos(0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14 Entries 6597Mean 0.4862RMS 0.2653
Entries 6597Mean 0.4862RMS 0.2653
Entries 712503Mean 0.4695RMS 0.2614
Entries 712503Mean 0.4695RMS 0.2614Entries 792670Mean 0.4918RMS 0.2578
Entries 792670Mean 0.4918RMS 0.2578Entries 525227Mean 0.4859RMS 0.2631
Entries 525227Mean 0.4859RMS 0.2631
|<1.501.00<|
DataPythia6Pythia8Madgraph
)NRcos(0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Dat
a/M
C
00.20.40.60.8
11.21.41.61.8
2
cosθNR
0.0620.0480.017
0.0890.0890.028
0.0730.060 0.024
0.0580.0310.011
22Tuesday, December 20, 2011
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
Entries 2837Mean 0.6969RMS 0.414
Entries 2837Mean 0.6969RMS 0.414Entries 310685Mean 0.7605RMS 0.4241
Entries 310685Mean 0.7605RMS 0.4241
Entries 354397Mean 0.7454RMS 0.4169
Entries 354397Mean 0.7454RMS 0.4169Entries 217819Mean 0.7184RMS 0.4193
Entries 217819Mean 0.7184RMS 0.4193
|<1.000.50<|
DataPythia6Pythia8Madgraph
BZ0 0.2 0.4 0.6 0.8 1 1.2 1.4
Dat
a/M
C
0.6
0.8
1
1.2
1.4
0
0.01
0.02
0.03
0.04
0.05
0.06Entries 11392Mean 0.6969RMS 0.4181
Entries 11392Mean 0.6969RMS 0.4181
Entries 1826979Mean 0.7362RMS 0.4242
Entries 1826979Mean 0.7362RMS 0.4242Entries 2091690Mean 0.7089RMS 0.4254
Entries 2091690Mean 0.7089RMS 0.4254Entries 1266831Mean 0.709RMS 0.4203
Entries 1266831Mean 0.709RMS 0.4203
|<0.500.00<|
DataPythia6Pythia8Madgraph
BZ0 0.2 0.4 0.6 0.8 1 1.2 1.4
Dat
a/M
C
0.60.70.80.9
11.11.21.31.40
0.01
0.02
0.03
0.04
0.05
0.06
Entries 30797Mean 0.6933RMS 0.4202
Entries 30797Mean 0.6933RMS 0.4202Entries 5951988Mean 0.7344RMS 0.4233
Entries 5951988Mean 0.7344RMS 0.4233Entries 6715509Mean 0.7029RMS 0.4213
Entries 6715509Mean 0.7029RMS 0.4213Entries 3957707Mean 0.704RMS 0.4207
Entries 3957707Mean 0.704RMS 0.4207
All DataPythia6Pythia8Madgraph
BZ0 0.2 0.4 0.6 0.8 1 1.2 1.4
Dat
a/M
C
0.6
0.8
1
1.2
1.4
0
0.01
0.02
0.03
0.04
0.05
0.06
Entries 4519Mean 0.7004RMS 0.4235
Entries 4519Mean 0.7004RMS 0.4235Entries 523290Mean 0.7388RMS 0.4213
Entries 523290Mean 0.7388RMS 0.4213
Entries 564139Mean 0.7037RMS 0.4088
Entries 564139Mean 0.7037RMS 0.4088Entries 364830Mean 0.7074RMS 0.4185
Entries 364830Mean 0.7074RMS 0.4185
|<1.501.00<|
DataPythia6Pythia8Madgraph
BZ0 0.2 0.4 0.6 0.8 1 1.2 1.4
Dat
a/M
C
0.6
0.8
1
1.2
1.4
χBZ
0.089 0.067 0.050
0.1510.1640.078
0.1010.097
0.060
0.0890.0380.034
23Tuesday, December 20, 2011
Observation from Data/MC Comparison
• Different generators give similar predictions for 3-, 4-jet mass distributions
• For other distributions, there is a difference among the generators: Pythia6 is deviating more from the data and MadGraph is closest to the data.
• For inclusive distributions and for distributions where all jets are in the barrel, agreement between data and MC is reasonable.
24Tuesday, December 20, 2011
Closure Test for UnfoldingLatest RooUnfold version 1.1.1 is used which is
supposed to have a more realistic error estimation.Unfold MC data set at detector level and compare the
unfolded distribution at generator levelI. Use measured distribution from the same generator
(Pythia6) as used for getting the response matrix II. Use measured distribution from a different generator
(Pythia8) as used for the response matrix (Pythia6)
25Tuesday, December 20, 2011
-210
-110
1
10
210
310
410
|<1.000.50<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
3Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
10
210
310
410 |<0.500.00<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
3Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
10
210
310
410
510 All
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
3Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
-110
1
10
210
310
410 |<1.501.00<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
3Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
3 Jet Mass (Case I)
0.0000.00050.082
0.0000.00020.093
0.0000.0010.087
0.0000.00040.079
26Tuesday, December 20, 2011
-110
1
10
210
310|<1.000.50<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
4Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
-110
1
10
210
310
410|<0.500.00<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
4Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
10
210
310
410All
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
4Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
-110
1
10
210
310|<1.501.00<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
4Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
4 Jet Mass (Case I)
0.007 0.000
0.105
0.258 0.008 0.000
0.000
0.136 0.107
0.000
0.010 0.098
27Tuesday, December 20, 2011
-110
1
10
210
310
410 |<1.000.50<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
3Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
10
210
310
410
510
|<0.500.00<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
3Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
10
210
310
410
510 All
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
3Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
-110
1
10
210
310
410
510
|<1.501.00<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
3Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
3 Jet Mass (Case II)
0.0340.0700.116
0.0910.1030.375
0.0430.101 0.108
0.014 0.0300.084
28Tuesday, December 20, 2011
-110
1
10
210
310 |<1.000.50<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
4Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
1
10
210
310
410
|<0.500.00<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
4Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
10
210
310
410
510
All
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
4Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
1
10
210
310|<1.501.00<|
GenUnfolded(binBybin)
Unfolded(svd)
Unfolded(bayes)
4Jet Mass500 1000 1500 2000 2500 3000
Gen
/Unf
olde
d
0.50.60.70.80.9
11.11.21.31.41.5
4 Jet Mass (Case II)
0.0610.1520.133
0.4020.2070.418
0.1830.2510.919
0.0310.0570.119
29Tuesday, December 20, 2011
Observation from Closure Tests
• One can get reasonable closure for bin-by-bin and SVD method
• Convergence for Bayesian method depends critically on the number of iterations (to be optimized)‣ Number of iterations for mass distributions need
to be large‣ Angular distributions need fewer iterations
• Uncertainty calculation still doubtful and needs to be studied in detail
30Tuesday, December 20, 2011
Summary & Outlook• Studied inclusive 3- and 4-jet variables using the entire 2011 data set.
• FInalize on HLT trigger path and event selection criteria.
• Effect of pileup is studied and the effect is found to be small.
• Binning in the variable is finalized on the basis of detector resolution.
• Data are compared with differenet leading order Monte Carlo's and MadGraph is providing the best description of the data.
• Studying the unfolding procedure: closure test is done using Monte Carlo sample
• Things to be done:
‣ Investigate systematic effects due to Jet ID’s, use of event filters (beam halo etc), Jet energy scale
‣ Study the uncertainties due to ISR/FSR, fragmentation models,
‣ Compare unfolded distributions with predictions from different NLO Monte Carlos
‣ Make the first draft of documentation
31Tuesday, December 20, 2011
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