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 Defense March, 24 2014 Sung-Won Lee 1

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 • 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

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 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

5. Compact Muon Solenoid CMS

6. Compact Muon Solinoid CMS

7. Standard Model (SM)

8. Z + Jet Angular Distribution

9. 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

10. 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)

11. 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

12. Basic Kinematic Selections

13. 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

14. 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

15. Basic Kinematic Properties • Well agreements for jets kinematics between data and MC

16. 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

17. Muon ID Efficiency

18. 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

19. Unfolding Correction on Data • Unfolding is consistent at one for all but yjet distribution. • Thus, we will unfold yjet.

20. 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

21. JES Uncertainties • Uncertainty is < 1% for all distributions

22. JER Uncertainties • Uncertainty is < 2% for all distributions

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

24. Comparison to Theories

25. Combined Results

26. Combined Results

27. 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.

28. Z + Jets Differential Cross Sections

29. Z+jets

30. 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

31. 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

32. 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

33. Basic Muon Selections • Using PF muon collection matched the trigger objects

34. The First Muon Candidate • First muon candidate kinematics are agreed between data and MC

35. 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

36. 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

37. Z Reconstruction • Z bosons are reconstructed from opposite charged muons • Z mass window of 71 < MZ < 111 are used and agreed with MC

38. Z Reconstruction

39. 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

40. Z+Jets Control Plots

41. Measured Observables Exclusive Inclusive • Good agreement between data and MC up to 4 jets as expected

42. Measured Observables • pT distributions of the first and second leading jets agree at low pT

43. 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

44. 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

45. Unfolding Response matrix

46. Unfolding

47. Unfolding

48. 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

49. 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

50. Systematic Uncertainties • JER causes only 2-4% uncertainty