update on multijet studiesindiacms/indiacms-meetings/december...update on multijet studies sunanda...

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


• 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


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


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


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


Effect of PileUp

• Use 2011A data

• Divide the events according to number of good primary vertex (<4, 4-6, 7-10, >10)


-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


-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


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


-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


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


0

0.01

0.02

0.03

0.04

0.05

0.06

0.07Entries 2.939321e+07

Mean 0.789

RMS 0.1099


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


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


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.


Resolution Studies for 3Jet Mass (0.0<|η|<0.5)


3Jet Mass Resolution in different η bins

Final choice of binnings is 50 GeV upto 1000 GeV and 100 GeV beyond that.


Resolution Studies for 4Jet CosθNR (0.0<|η|<0.5)


4Jet CosθNR Resolution in different η bins

Constant bin width of 0.1 is used.


Data/MC Comparison

Detector level measurements are compared with predictions from 3 different event generators: Pythia6 (TuneZ2), Pythia8 (Tune4C), MadGraph


-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




|<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 228.6Entries 1.43193e+07

Mean 549.6

RMS 221.6

Entries 1.43193e+07

Mean 549.6

RMS 221.6


Mean 548.6

RMS 222.3


Mean 548.6

RMS 222.3


Mean 546.7

RMS 224.3


Mean 546.7

RMS 224.3

|<0.500.00<|


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



Mean 577.7

RMS 233.5

Entries 4.21741e+07

Mean 577.7

RMS 233.5


Mean 578.4

RMS 234.5


Mean 578.4

RMS 234.5


Mean 572.3

RMS 236.8


Mean 572.3

RMS 236.8

All


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





|<1.501.00<|


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


0

0.02

0.04

0.06

0.08

0.1

0.12

0.14Entries 24457Mean 0.9308RMS 0.05747




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


Mean 0.9143

RMS 0.06537


Mean 0.9143

RMS 0.06537


Mean 0.9194

RMS 0.06322


Mean 0.9194

RMS 0.06322


Mean 0.9266

RMS 0.059


Mean 0.9266

RMS 0.059

|<0.500.00<|


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


Mean 0.9126

RMS 0.0662


Mean 0.9126

RMS 0.0662


Mean 0.9172

RMS 0.06436


Mean 0.9172

RMS 0.06436


Mean 0.926

RMS 0.05961


Mean 0.926

RMS 0.05961

All


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 3159376Mean 0.9285RMS 0.05809

|<1.501.00<|


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


-510

-410

-310

-210

-110Entries 4208Mean 700.3RMS 232.5




Entries 315679Mean 689RMS 253.3

Entries 315679Mean 689RMS 253.3

|<1.000.50<|


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 1881147Mean 723.6RMS 290

Entries 1881147Mean 723.6RMS 290

|<0.500.00<|


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





All


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






|<1.501.00<|


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


0

0.02

0.04

0.06

0.08

0.1

0.12

0.14 Entries 4173Mean 0.4935RMS 0.2637






|<1.000.50<|


)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 2543544Mean 0.4745RMS 0.2634



Entries 1881112Mean 0.4907RMS 0.2644

|<0.500.00<|


)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 6042303Mean 0.4971RMS 0.2646

All


)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






|<1.501.00<|


)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


0

0.01

0.02

0.03

0.04

0.05

0.06

0.07







|<1.000.50<|


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 1826979Mean 0.7362RMS 0.4242




|<0.500.00<|


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






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







|<1.501.00<|


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


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.


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)


-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<|


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


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<|


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


-110

1

10

210

310|<1.000.50<|


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<|


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


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<|


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


-110

1

10

210

310

410 |<1.000.50<|


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<|


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


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<|


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


-110

1

10

210

310 |<1.000.50<|


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<|


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


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<|


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


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


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


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