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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 Defense March, 24 2014. Sung-Won Lee. 1. Outline. Motivation. Large Hadron Collier (LHC) and Compact Muon Solenoid (CMS).
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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 Defense March, 24 2014 Sung-Won Lee 1
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
Motivation • Measurements of the rapidity distributions and differential cross sections are one of the crucial test of the SM prediction • Provide good feedback to the theoretical physics community to improve the precision of perturbative QCD and to event generator experts • Major background processes for various new physics searches such as Higgs and Supersymmetry (SUSY) • For Z boson decays into μ+μ- , the trigger system is very efficient and nearly background free
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 TeV and 4 x 10-34 cm-2s-1 • In 2011, collision at = 14 TeV and 4 x 10-33 cm-2s-1 • In 2012, collision at = 8 TeV and 7.7 x 10-33 cm-2s-1
What Do We Measure? • Rapidity distributions of Z boson: |yz| • Rapidity distributions of leading jet: |yjet| • Rapidity difference: ydiff = 0.5|yz-yjet| • Related to the scattering angle at the center of momentum frame: tanh(ydiff) = β*cosθ* • Rapidity average: ysum = 0.5|yz+yjet| • Rapidity boost from the center of momentum frame to the lab frame • Rapidity is defined by
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 yjet • Other variable have unfolding correction consistent with one. (4) Evaluate Systematic uncertainties. (5) Compare shapes with MCFM, MADGRAPH, and SHERPA MC simulations. MCFM • Matrix element at NLO,without parton showering or hadronization • Scale set to the dilepton mass • CTEQ 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 pT(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)
Dataset and HLT • CMS data collected in 2011 for 5.1 ± 0.1 fb-1 JSON: Cert_160404-180252_7TeV_ReRecoNov08_Collisions11_JSON.txt • Monte Carlo Simulations • High Level Trigger
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
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
Basic Kinematic Properties • Well agreements for jets kinematics between data and MC
Muon ID Scale Factor and Efficiency • Re-weight the MC events that pass ID selections with the scale factors • 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 ID efficiency correction is the reciprocal if the ratio of weighted with ID selections and without ID selection • The scale is computed from the ratio of tag+passing probe and tag+failing probe • Obtain efficiency as a function of the four rapidity variables ID scale factors from Particle Object Group • Obtain the data-to-MC ID efficiency scale factors in bins of pT and η • Use Muon Particle Object Group recommendations
Unfolding • 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. • Using RooUnfold package • MADGRAPH+Pythia as source of response matrices • 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 Response matrices of rapidity: the comparison shows mostly diagonal elements
Unfolding Correction on Data • Unfolding is consistent at one for all but yjet distribution. • Thus, we will unfold yjet.
Systematic Uncertainties • Jet Energy Scale (JES) Uncertainties • 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 • Jet Energy Resolution (JER) • 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
JES Uncertainties • Uncertainty is < 1% for all distributions
JER Uncertainties • Uncertainty is < 2% for all distributions
Comparison to Theories Shape comparisons of CMS data, MADGRAPH, and SHERPA to MCFM are shown.
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 yz and yjet . • 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.
What Do We Measure? In this analysis, we measured the Z+jets differential cross sections of up to two jets associated with Z → μ+μ- . • The Z+jets production cross section as a function of the jet multiplicity : dσ/ dNJ • 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
Dataset • CMS data collected in 2012 for 19.8 ± 0.1 fb-1 • JSON: Cert_190456-208686_8TeV_22Jan2013ReReco_Collisions12_JSON.txt • Monte Carlo Simulations • High Level Trigger → HLT_Mu17_Mu8_v* with L1_DoubleMu3p5 seed
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
Basic Muon Selections • Using PF muon collection matched the trigger objects
The First Muon Candidate • First muon candidate kinematics are agreed between data and MC
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
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 pT and η
Z Reconstruction • Z bosons are reconstructed from opposite charged muons • Z mass window of 71 < MZ < 111 are used and agreed with MC
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
Measured Observables Exclusive Inclusive • Good agreement between data and MC up to 4 jets as expected
Measured Observables • pT distributions of the first and second leading jets agree at low pT
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
Unfolding • Using MADGRAPH+Pythia as source of response matrices • 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
Unfolding Response matrix
Systematic Uncertainties • Jet Energy Scale (JES) Uncertainties • 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 • Jet Energy Resolution (JER) • 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
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
Systematic Uncertainties • JER causes only 2-4% uncertainty