sung-won lee

Sung-Won Lee 1

Study of Jets Production Association with a Z boson in pp Collision

at 7 and 8 TeV with the CMS Detector

Kittikul Kovitanggoon

Ph. D. Thesis DefenseMarch, 24 2014

2

Outline

Motivation

Large Hadron Collier (LHC) and Compact Muon Solenoid (CMS)

Overview of Standard Model (SM)

Measurements of Angular Distributions for Z+jet events at 7 TeV Theory Data Samples and Event Reconstructions Unfolded Results with Uncertainties

Differential Cross Section of Jets Associated to Z boson at 8 TeV Theory Data Samples and Event Reconstructions Unfolded Results with Uncertainties

Conclusions

3

Motivation

For Z boson decays into μ+μ- , the trigger system is very efficient and nearly background free

Provide good feedback to the theoretical physics community to improve the precision of perturbative QCD and to event generator experts

Measurements of the rapidity distributions and differential cross sections are one of the crucial test of the SM prediction

Major background processes for various new physics searches such as Higgs and Supersymmetry (SUSY)

4

Large Hadron Collider (LHC)

A 27 km in circumference

To collide rotating beams of protons or heavy ions

Maximum energy of proton-proton collisions at = 14 TeVand 4 x 10-34 cm-2s-1

√s

In 2011, collision at = 14 TeV and 4 x 10-33 cm-2s-1

√s

In 2012, collision at = 8 TeV and 7.7 x 10-33 cm-2s-1

√s

5

Compact Muon Solenoid CMS

6

Compact Muon Solinoid CMS

7

Standard Model (SM)

8

Z + Jet Angular Distribution

10

What Do We Measure? Rapidity distributions of Z boson: |y

z|

Rapidity distributions of leading jet: |yjet

|

Rapidity difference: ydiff

= 0.5|yz-y

jet|

Related to the scattering angle at the center of momentum frame: tanh(y

diff) = β*cosθ*

Rapidity average: ysum

= 0.5|yz+y

jet|

Rapidity boost from the center of momentum frame to the lab frame

Rapidity is defined by y=12

ln ( E+pzE− pz )

11

Analysis Procedure(1) Selects events containing a Z(→μμ) and a jet that satisfy kinematic and ID selections.(2) Derive efficiency from MC and correct it with data-to-MC scale factors via tag and probe method. (3) Unfold the distribution of y

jet

Other variable have unfolding correction consistent with one.

(4) Evaluate Systematic uncertainties.

(5) Compare shapes with MCFM, MADGRAPH, and SHERPA MC simulations.

MCFMMatrix element at NLO,without parton showering or hadronizationScale set to the dilepton massCTEQ 6.1 m (NLO PDFs)

MADGRAPH+PYTHIA Matrix element at LO with MLM matching Scale set to the square root sum of dilepton mass and p

T(jet)

CTEQ 6L1 m (LO PDFs)

SHERPA Matrix element at LO with CKKW matching Scale set to the dilepton mass CTEQ 6.6M (NLO PDFs)

12

Dataset and HLT CMS data collected in 2011 for 5.1 ± 0.1 fb-1

Monte Carlo Simulations

JSON: Cert_160404-180252_7TeV_ReRecoNov08_Collisions11_JSON.txt

High Level Trigger

13

Basic Kinematic Selections

14

Basic Kinematic Properties Well agreements for Z kinematics between data and MC

Z mass distribution was created before Z mass selections

Discrepancy of Z mass < 50 GeV comes from the generator-level mass selection

15

Basic Kinematic Properties

The number of jets accompanying a Z drops by ~αS

Non-zero jet mass is attributed to the finite angular spread of the jet in calorimeter

16

Basic Kinematic Properties

Well agreements for jets kinematics between data and MC

17

Muon ID Scale Factor and Efficiency

ID scale factors from Particle Object Group

Use Tag & Probe with Data & MC

Select a pair of muons: one passing tight selections (tag) and the other passing or failing loose selections (probe)

The scale is computed from the ratio of tag+passing probe and tag+failing probe

Use Muon Particle Object Group recommendations

Obtain the data-to-MC ID efficiency scale factors in bins of p

T and η

Re-weight the MC events that pass IDselections with the scale factors

Obtain efficiency as a function of the four rapidity variables

The ID efficiency correction is the reciprocal if the ratio of weighted with ID selections and without ID selection

18

Muon ID Efficiency

19

Unfolding

Unfolding methods 1. Bayesian with 3 iterations 2. Bin-by-Bin 3. Singular Value Decomposition with kreg=10

Criteria: if unfolding correction is consistent with zero within MC statistical uncertainty, do not unfold

In order to compare experimental result with theoretical prediction, the experimental need to be corrected due to the detector effects.==> The method is called unfolding.

Response matrices of rapidity: the comparison shows mostly

diagonal elements

Using RooUnfold package

MADGRAPH+Pythia as source of response matrices

20

Unfolding Correction on Data

Unfolding is consistent at one for all but yjet

distribution. Thus, we will unfold y

jet.

21

Systematic Uncertainties

Jet Energy Scale (JES) Uncertainties

Jet Energy Resolution (JER)

Jets are corrected due to the non-uniform and non-linear response of calorimeters Can cause the bin migration i.e. Z+0jet can fake as Z+1jet etc. Shifted jet corrections up and down by 1σ σ is provided by JetMET POG Re-performing measurements after shifting jet

Finite jet energy resolution can be the threshold effects Modified the reconstructed jet pT with the pT difference between matched reconstruction-level jets and generator-level jets

c is a factor provided by JetMET POG

22

JES Uncertainties

Uncertainty is < 1% for all distributions

23

JER Uncertainties

Uncertainty is < 2% for all distributions

24

Comparison to Theories Shape comparisons of CMS data, MADGRAPH, and SHERPA to MCFM are shown.

25

Comparison to Theories

26

Combined Results

27

Combined Results

28

Summary CMS detector was used to measure the angular distributions of

the products from Z+1jet events

• Madgraph+Pythia, Sherpa, and MCFM have similar agreement with data for y

z and y

jet .

• For Z + 1jet, Sherpa agrees better with data for ydiff

and ysum

.

Parton showering and matching scheme give the difference.

Provide feedback to theory community for improving theoretical predictions.

29

Z + Jets Differential Cross Sections

30

Z+jets

31

What Do We Measure?

In this analysis, we measured the Z+jets differential cross sections ofup to two jets associated with Z → μ+μ- .

The Z+jets production cross section as a function of the jet multiplicity : dσ/ dN

J

The Z+jets cross section as a function of the jet pT : dσ/ dpT

The Z+jets cross section as a function of the jet η : dσ/ dη

32

Dataset CMS data collected in 2012 for 19.8 ± 0.1 fb-1

Monte Carlo Simulations

JSON: Cert_190456-208686_8TeV_22Jan2013ReReco_Collisions12_JSON.txt

High Level Trigger → HLT_Mu17_Mu8_v* with L1_DoubleMu3p5 seed

33

PU Re-Weighting

MC productions use an approximate number of pileup interactions Pileup interactions in MC are re-weighted by the data pileup distribution using the entire data-taking period

34

Basic Muon Selections Using PF muon collection matched the trigger objects

35

The First Muon Candidate

First muon candidate kinematics are agreed between data and MC

36

The Second Muon Candidate

Second muon candidate kinematics are agreed between data and MC

The pT plots show good agreement at the kinematic region up to 60 GeV where we

expect to find most muons coming from Z decays

37

Efficiency Scale Factor

Scale factors of HLT, ID, and isolation from Tag and Probe

Provided by Muon POG

Obtain the data-to-MC scale factors in bins of p

T and η

38

Z Reconstruction Z bosons are reconstructed from opposite charged muons

Z mass window of 71 < MZ < 111 are used and agreed with MC

39

Z Reconstruction

40

Basic Jet Selections Jets are AK5 PF after Charged Hadron subtraction

Data are using L1FastJet + L2Relative + L3Absolute + L2L3Residual

MC are using L1FastJet + L2Relative + L3Absolute

Leptons are vetoed from the jet collection by a simple ∆ R cut of 0.5

41

Z+Jets Control Plots

42

Measured Observables

Good agreement between data and MC up to 4 jets as expected

Exclusive Inclusive

43

Measured Observables

pT distributions of the first and second leading jets agree at low pT

44

Measured Observables

η distributions of the first and second leading jets also agree in barrel region and show some discrepancy in endcap region as expected from detector performance

45

Unfolding Using MADGRAPH+Pythia as source of response matrices

Unfolding methods 1. Bayesian with 3 iterations → used for the final results 2. Bin-by-Bin 3. Singular Value Decomposition with kreg=10

Generator level phase space Muons are dressed with all the photons that are within

the cone of radius 0.1 Stable muons from Z (status =1) Cuts on muons pt > 20,η < 2.4 after adding photons

Background subtraction from data

Using MADGRAPH+Pythia as source of response matrices

46

Unfolding

Response matrix

47

Unfolding

48

Unfolding

49

Systematic Uncertainties

Jet Energy Scale (JES) Uncertainties

Jet Energy Resolution (JER)

Jets are corrected due to the non-uniform and non-linear response of calorimeters Can cause the bin migration i.e. Z+0jet can fake as Z+1jet etc. Shifted jet corrections up and down by 1σ σ is provided by JetMET POG Re-performing measurements after shifting jet

Finite jet energy resolution can be the threshold effects Modified the reconstructed jet pT with the pT difference between matched reconstruction-level jets and generator-level jets

c is a factor provided by JetMET POG

50

Systematic Uncertainties

Smearing jet pT can change

Z+0jet to Z+1jet etc

Higher the jet mutiplicity, more bin migration

JES causes up to 10% uncertainty

51

Systematic Uncertainties

JER causes only 2-4% uncertainty

52

Systematic Uncertainties

Summary

53

Results

54

Results

55

Z+Jets Summary

The differential cross-sections of Z+n jets (n up to 2) are measured as functions of

Jet multiplicity Jet transverse momentum Jet rapidity

The measurements are done on 8 TeV with integrated luminosity of 19.8 fb−1

Detector effects are corrected by unfolding with Bayes method

Results are compared to the following MCFM+Pythia generator prediction

56

Conclusions Jets productions in association with a Z boson in p-p collision

provides a good opportunity to test perturbative QCD and

important background for new physics

Angular distributions for the Z boson and a single jet of 4.7 fb− 1

at 7 TeV have been analyzed

|y

z| and |y

jet| are found to agree with predictions from SHERPA,

MADGRAPH, and MCFM y

sum described by all predictions up to 5% precision for y

sum < 1.0

At ysum

> 1.0, SHERPA is the best described due to the hybrid

calculations that employ NLO PDFY

diff is best described MCFM

57

Conclusions

Differential cross sections for the Z boson and jets of 19.8 fb− 1 at 8 TeV have been calculated

The measurements of Z+jets production deferential cross section up to two jets as a function of the

Jet multiplicity: dσ/ dNJ

Transverse momentum: dσ/ dpT

Rapidity: dσ/ dη

J

Results after unfolding and efficiency corrected, compared with different theoretical pQCD predictions in MADGRAPH

JES and JER are studied as the main systematic uncertainties

Comparisons are agreed with data and MC

58

Back Up

59

Unfolding Correction with SHERPA

Use the response matrices of MEDGRAPH to unfold the independent MC prediction of Z+jets, SHERPA

60

PU Systematic Uncertainty for Z + Jet Angular

61

Background Systematic Uncertainty for Z + Jet Angular

62

PU Systematic Uncertainty for Z + Jet Angular

63

Combination of Electron and Muon Best Linear Unbiased Esttimator

Andrea Valassi, NIM, A500, 391 Louis Lyons, Duncan Gibaut, and Peter Clifford, NIM, A207, 110

JES and PU uncertainties are 100% correlated between electron and muon channel

The covariance matrix has 2N dimension N is the number of bins with non-zero contents For each channel of y

jet, the bin-by-bin correlation is obtained

from the covariance matrix of RooUnfold after unfolding

For every bin of the observable, the uncorrelated uncertainty is at least 3 times of the correlated uncertainty

64

Breakdown Differential Cross Section

65

Breakdown Differential Cross Section