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Jets produced in association with Z-bosons in CMS at the LHC. Christos Lazaridis University of Wisconsin-Madison PhD Thesis Defense August 16, 2011. Outline. Theory Overview Experimental Apparati Detecting Particles Z+Jets Characteristics Monte Carlo Simulations Data Sample

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Jets produced in association with Z-bosons in CMS at the LHC


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  1. Jets produced in association with Z-bosons in CMS at the LHC Christos LazaridisUniversity of Wisconsin-Madison PhD Thesis DefenseAugust 16, 2011

  2. Outline • Theory Overview • Experimental Apparati • Detecting Particles • Z+Jets Characteristics • Monte Carlo Simulations • Data Sample • Event Selection • Unfolding • Results • Conclusions PhD Thesis Defense

  3. The Standard Model • Fundamental particles: • Fermions • Matter constituents • Quarks • up, down, charm, strange, top, bottom • Leptons • Electron, muon, tau, corresponding neutrinos • Bosons • Force carriers • Electromagnetism: Photon • Weak interactions: W±/Z • Strong interactions: Gluons • Higgs boson • Hypothesized • Explains how elementary particles become massive PhD Thesis Defense

  4. Particle Interactions 1 • Quarks bound in hadrons via strong interactions • Baryons • 3-quark states • Protons, neutrons • Mesons • quark-antiquarkstates • pions, kaons • Atoms bound together via electromagnetic interactions • Weak force responsible for radioactive decay 10-2 10-7 Proton Pion Relative forcestrength Lithiumatom PhD Thesis Defense

  5. Motivation for Z+Jets Z+0 jets Z+1 jet Backgrounds to new or other interesting physics processes Z+2 jets • Validation of theoretical predictions at a higher energy regime • Testing perturbative QCD calculations • Cross-check Monte Carlo generators with CMS collision data • High cross section,low-background Z mass peak • Useful as a detector calibration tool • Final states contain • Leptons • Jets • Goal: • Measure production rates of Ze+e- in association with jets • Performing ratio measurements where significant systematics will cancel out PhD Thesis Defense

  6. Perturbative QCD and Renormalization NLO: Next-to-leading order LO: Leading order • The Quantum ChromoDynamics theory describes strong interactions • Calculable at small distances and high momentum transfer (large Q2) • αs, the coupling constant of the strong force increases with distance (low energy scales) • An approximate solution is obtained by a perturbative expansion of αs terms: • dσ = A0αs0 + A1αs1+A2αs2 + … • αs 0 (high energy scale)  Asymptotic freedom • αs ∞ (low energy scale) Color confinement, pQCDnot valid PhD Thesis Defense

  7. From Partons to Jets Jet Parton showers Decays Hadronization Hard scatter Hadronization • From the hard scatter in a collision, outgoing partons (quarks and gluons) are created • Due to color confinement, these colored particles cannot exist in free form • Colored partons evolve into collinear jets of colorless hadrons (hadronization) • Cannot be described by perturbative QCD • Described by phenomenological models from experimental data PhD Thesis Defense

  8. The Large Hadron Collider • World’s most powerful particle accelerator and collider • Built in a tunnel 100 meters underground across the Swiss-French border near Geneva • 27 km in circumference • >1600 superconducting niobium-titanium magnets • Operating at 1.9 K • Currently accelerates proton beams at 3.5 TeV/beam • Nominal: 7 TeV/beam • 4 collision points • CMS • ATLAS • LHCb • ALICE PhD Thesis Defense

  9. The CERN Accelerator Complex 3.5/7 TeV 450 GeV 25 GeV 50 MeV 1.4 GeV PhD Thesis Defense

  10. Proton-Proton Collisions at the LHC • Luminosity • L= Particle Flux/Time • Interaction rate: • Cross sectionσ: “effective” area of interacting particles PhD Thesis Defense

  11. Compact Muon Solenoid • Weight: 12500 T • Diameter: 15 m • Length: 21.5 m • Solenoid: • Field: 4 Tesla • Diameter: 6 m • Length: 12.5 m • Tracker: • Full silicon • Electromagnetic calorimeter: • Lead tungstate crystals PhD Thesis Defense

  12. Detector Geometry Parallel to proton beam Barrel |η| < 1.44 η=0.0 φ=π/2 Endcap|η| > 1.52 Perpendicular to proton beam θ η=1.5 Collision point η=3.0 φ=0 η=inf. • Pseudorapidity • η= -ln(tan(θ/2)) • Separation in solid angle • ΔR = ((Δφ)2+ (Δη)2)1/2 PhD Thesis Defense

  13. Tracker • Records charged particle path and determines their transverse momentum (pT) • Helps reject photons and electrons originating from photon conversions 210 m2 of active silicon Coverage|η| < 2.5 Excellent resolution: • Silicon pixel detectors closest to interaction region • Higher granularity • Less occupancy per cell • Silicon strip detectors in barrel and endcaps PhD Thesis Defense

  14. Electromagnetic Calorimeter • ECAL measures electron/photon energy and position up to |η|<3 • ~76,000 lead tungstate crystals • Short radiation length (0.89 cm) • Total crystal length 230 mm  25.8 X0 • Small Moliere radius (22 mm) • Resolution: PhD Thesis Defense

  15. Hadronic Calorimeter • Forward region (3 < |η| < 5) • Steel plates/quartz fibers • Resolution: • Barrel/Endcap region (|η| < 3) • Brass/scintillator layers • Resolution: • HCAL samples showers to measure their energy/position • Sampling calorimeter • Assists in measuring energy and position of jets PhD Thesis Defense

  16. Detecting Particles • Electrons • Track in silicon tracker • Energy deposit in the electromagnetic calorimeter • Jets • Tracks in silicon tracker • Energy deposits in the electromagnetic & hadronic calorimeters Tracker ECAL HCAL Solenoidmagnet Muon detectors Collimated sprays of high-energy hadrons PhD Thesis Defense

  17. Reconstructing Electrons ET γ e+ Tracker Strips Pixel detector pT • Combining calorimetric and tracker information • Creating “superclusters” of energy deposits in the ECAL • In area identified by Level-1 trigger • ET > 4 GeV • Wider in φdue to bremsstrahlung • Tracks reconstructed from hits in the CMS silicon tracker • Matching superclusters with tracks • Within ΔR ≤ 0.15 PhD Thesis Defense

  18. Reconstructing Jets • The Particle Flow algorithm is used to make jets • Combines information from all CMS subdetectors • Creates a list of Particle Flow objects • Electrons, muons, charged & neutral hadrons • The list of particles is used as input to the anti-kT jet clustering algorithm • Sequential recombination algorithm • Combines particles based on some distance measure starting from the closest one  Jets PhD Thesis Defense

  19. Detecting Particles • Electrons • Track in silicon tracker • Energy deposit in the electromagnetic calorimeter • Jets • Tracks in silicon tracker • Energy deposits in the electromagnetic & hadronic calorimeters Tracker ECAL HCAL Solenoidmagnet Muon detectors Collimated sprays of high-energy hadrons PhD Thesis Defense

  20. Trigger 40 MHz crossing frequency • Each beam contains 1011 protons • Beams designed to collide every 25 ns • 20 proton-proton interactions per crossing • Trigger system selects events of interest for offline processing • Level-1 Trigger • High-speed custom-made electronics • High Level Trigger • Commercial processor farm • 4 x 105 total rejection factor HLT output ~100 Hz PhD Thesis Defense

  21. Z+Jets Characteristics • Ze+e- + Jets production has a very distinct signature: • Two isolated electrons originating from the decay of the vector boson • Clear invariant mass peak • NNLO cross section O(1 nb) • With the first 36 pb-1 of LHC collision data we expect ~4x104 Z events • Events with more jets are harder to produce • Due to the addition of an extra strong coupling • 2010 LHC recorded data allow us to study up to Z+4 jet events Ze+e- + 2 jets event PhD Thesis Defense

  22. Monte Carlo Simulations Hard scattering (Madgraph) Hadronization, showering etc. (Pythia) • MadGraph: Z + Jets process • Fixed order matrix element calculations of cross sections • Generates multi-parton processes in hadroniccollisions • Pythia : Subsequent generator level simulation • Hadronization, partonshowering, initial/final state radiation • Underlying event (“tune”) • GEANT4 : Detector simulation • Toolkit for the simulation of the passage of particles through matter • CMSSW : CMS software framework • Detector electronics response • Event reconstruction • Used MadGraph/Pythia and Pythia-onlyMonte Carlo samples to model signal and backgrounds • Tune Z2 used to model underlying event PhD Thesis Defense

  23. Data Sample • Analyzed 2010 CMS proton-proton collision data • Corresponding to 36.1 ± 1.4 pb-1 • Considering only runs approved by the CMS data quality group • Selected events single electron triggers • Requiring the presence of an electron above a pT threshold • Thresholds and quality requirements change as luminosity increases to keep a manageable trigger rate • pT> 10 GeVrequired for early running, increased progressively to 17 GeV • Electron Identification and Isolation requirements were also added PhD Thesis Defense

  24. Analysis Flow Fit distributions A L L E V EN T S Electron Collection ZCandidates Correct yields for reconstruction efficiency Signal yields vs. # jets Unfold jet multiplicity and leading jet pT Jet Collection • Ratio plots • σ(Ζ+n jets) / σ(Ζtotal) • σ(Ζ+n jets) /σ(Ζ+(n-1) jets) PhD Thesis Defense

  25. Event Selection • Events firing one of the selected HLT triggers • Asymmetric electron requirements • One “tight” electron (Working Point 80) • pT > 20 GeV • Matched to the HLT object (ΔR < 0.2) • “Looser” second leg (Working Point 95) • pT > 10GeV • |η|<2.5, excluding barrel-endcap transition region • Transverse impact parameter δxy < 0.035 • Jet requirements • pT > 30 GeV • |η| < 2.4 • Loose Jet Identification criteria (later) PhD Thesis Defense

  26. Electron Selection Electron Identification : σiηiη, Δφin Barrel Endcap Z+Jets W+Jets ttbar QCD Plots show consecutive cuts corresponding to the WP80 selection PhD Thesis Defense • σiηiη • Describes the shape width of the ECAL cluster along the ηdirection • Δφin • Electron-track direction matching in φ

  27. Electron Selection Electron Identification : Δηin, Η/Ε Barrel Endcap Z+Jets W+Jets ttbar QCD Plots show consecutive cuts corresponding to the WP80 selection PhD Thesis Defense • Δηin • Electron-track direction matching in η • H/E • Fraction of the electron energy deposited in the HCAL

  28. Electron Selection Isolation Definition Isolation cone Inner veto cone HCAL ECAL Tracker • Electrons originating from a Z boson expected to be well isolated • Powerful QCD background discriminator • Three isolation variables • Tracker isolation • Sum of pT of tracks around the electron • ECAL isolation • Sum of ECAL energy deposits around the electron • HCAL isolation • Sum of HCAL energy deposits around the electron • Quantities calculated in a ΔR = 0.4 cone • Inner veto cone to exclude electron contributions • Relative isolation is used • Divided by the electron ET PhD Thesis Defense

  29. Electron Selection Applying isolation requirement Relative tracker isolation Relative ECAL isolation Relative HCAL isolation Barrel Z+Jets W+Jets ttbar QCD Endcap PhD Thesis Defense

  30. Electron Selection Conversion rejection Missing hits dist Δcot(θ) Z+Jets W+Jets ttbar QCD • Electrons from photon conversions are a non-negligible background • Vetoed as coming from a conversion if • missing expected hits in front of the innermost valid track hit exist, OR • |dist| < 0.02 AND |Δcotθ| < 0.02 • dist: Closest distance between electron track and “partner” track • |Δcotθ|: Polar angle separation between electron and “partner” track PhD Thesis Defense

  31. Leading Electron after Selection DATA Z+Jets W+Jets ttbar QCD Leading electron pT Leading electron η Leading electronφ 36.1 pb-1 • Characteristic distributions of the leading electron • pT agreement up to ~70 GeV • data electrons more central in ηthan MC • φflat for both data & MC • QCD underestimated • Scaled by a factor of 1.15 PhD Thesis Defense

  32. Analysis Flow Fit distributions A L L E V EN T S Electron Collection ZCandidates Correct yields for reconstruction efficiency Signal yields vs. # jets Unfold jet multiplicity and leading jet pT Jet Collection • Ratio plots • σ(Ζ+n jets) / σ(Ζtotal) • σ(Ζ+n jets) /σ(Ζ+(n-1) jets) PhD Thesis Defense

  33. Dielectron Selection 36.1 pb-1 Mass pT Rapidity • The highest pT electron passing tight selection is combined with the highest pT electron passing loose selection to construct Z candidates • Mass window ( 60, 120 ) GeV • Almost zero background • Very good agreement with Monte Carlo PhD Thesis Defense

  34. Analysis Flow Fit distributions A L L E V EN T S Electron Collection ZCandidates Correct yields for reconstruction efficiency Signal yields vs. # jets Unfold jet multiplicity and leading jet pT Jet Collection • Ratio plots • σ(Ζ+n jets) / σ(Ζtotal) • σ(Ζ+n jets) /σ(Ζ+(n-1) jets) PhD Thesis Defense

  35. Jet selection MC without PU PU with corrections PU without corrections Exclusive jet rates 36.1 pb-1 DATA Z+Jets W+Jets ttbar QCD Z electrons removed from jet list using a ΔR=0.3 cone Exclusive jet multiplicity PhD Thesis Defense • Particle Flow jets • anti-kt 0.5 clustering algorithm • pT > 30 GeV, |η| < 2.4 • Corrected for event pile-up and non-uniform calorimeter response • Pile-up increases measured jet energy • Data-driven method to correct this effect • Loose jet identification applied • Noise removal

  36. Analysis Flow Fit distributions A L L E V EN T S Electron Collection ZCandidates Correct yields for reconstruction efficiency Signal yields vs. # jets Unfold jet multiplicity and leading jet pT Jet Collection • Ratio plots • σ(Ζ+n jets) / σ(Ζtotal) • σ(Ζ+n jets) /σ(Ζ+(n-1) jets) PhD Thesis Defense

  37. Z MassData - Monte Carlo comparison ≥0 jets ≥1 jets ≥2 jets ≥3 jets ≥4 jets DATA Z+Jets W+Jets ttbar QCD PhD Thesis Defense • Invariant mass of dielectron pair • Very low background • Monte Carlo scaled to data luminosity 36.1 pb-1 • Few statistics for 4-jet events

  38. Z pTData - Monte Carlo comparison ≥0 jets ≥1 jets ≥2 jets ≥3 jets ≥4 jets DATA Z+Jets W+Jets ttbar QCD PhD Thesis Defense • Transverse momentum of reconstructed Z boson • Monte Carlo scaled to data luminosity 36.1 pb-1 • Mass window( 60, 120 ) GeV • Low statistics for events with more than 3 jets

  39. Leading jet pTData - Monte Carlo comparison ≥1 jets ≥2 jets DATA Z+Jets W+Jets ttbar QCD ≥3 jets ≥4 jets PhD Thesis Defense • Transverse momentum of the most energetic jet in an event • Monte Carlo scaled to data luminosity 36.1 pb-1 • Mass window ( 60, 120 ) GeV • Low statistics for events with more than 3 jets

  40. Analysis Flow Fit distributions A L L E V EN T S Electron Collection ZCandidates Correct yields for reconstruction efficiency Signal yields vs. # jets Unfold jet multiplicity and leading jet pT Jet Collection • Ratio plots • σ(Ζ+n jets) / σ(Ζtotal) • σ(Ζ+n jets) /σ(Ζ+(n-1) jets) PhD Thesis Defense

  41. Fitting method 0 jets • Z mass peak fit: • Signal • Z Monte Carlo lineshape extracted from Pythia convolved with Crystal Ball + exponential + Gaussian • Same fit method used for the CMS inclusive Z(ee) results • Parameters for events with n≥2 jets fit fixed from 1-jet case • Background • Exponential function • Independent parameters for each jet multiplicity • Fits in exclusive number of jets for up to 3 jets • Plots for 4-jets are inclusive PhD Thesis Defense

  42. Fitting Z mass 1 jets 2 jets 3 jets ≥4 jets • Signal • Z lineshape convolved with Crystal Ball + exp. Gaussian • Background • Exponential • For n≥2 jets fit parameters fixed from 1-jet case PhD Thesis Defense

  43. Analysis Flow Fit distributions A L L E V EN T S Electron Collection ZCandidates Correct yields for reconstruction efficiency Signal yields vs. # jets Unfold jet multiplicity and leading jet pT Jet Collection • Ratio plots • σ(Ζ+n jets) / σ(Ζtotal) • σ(Ζ+n jets) /σ(Ζ+(n-1) jets) PhD Thesis Defense

  44. Event Selection Efficiency Tag and Probe Method • Data-driven Tag and Probe method used to extract electron selection efficiency • Requiring the presence of two electrons: • One passing tight selection requirements (“tag”) • The second (“probe”) is used to measure the selection efficiency of a studied quantity • The invariant mass of thepair should match the Zmass • Fits are performed to the passing and failingprobes • Efficiency calculated as: Sample fits Passing probes Failing probes All probes PhD Thesis Defense

  45. Event Selection Efficiency Results Efficiencies as a function of jet multiplicity • Full efficiency depends on five factors • Reconstruction efficiency for loose (WP95) & tight (WP80) electrons • Selection efficiency for loose & tight electrons • Trigger efficiency • ε = εtight,recx εtight,WP80 x εtight,triggerx εloose,recx εloose,WP95 • Corrected for Monte Carlo efficiency • Final numbers computed as • εΖ = ε(MCTruth) x ε(T&PDATA)/ε(T&PMC) PhD Thesis Defense

  46. Analysis Flow Fit distributions A L L E V EN T S Electron Collection ZCandidates Correct yields for reconstruction efficiency Signal yields vs. # jets Unfold jet multiplicity and leading jet pT Jet Collection • Ratio plots • σ(Ζ+n jets) / σ(Ζtotal) • σ(Ζ+n jets) /σ(Ζ+(n-1) jets) PhD Thesis Defense

  47. Unfolding Responsematrices # jets Leading jet pT • The measured jet distributions get “smeared” • Due to detector resolution and efficiency effects • The “true” (particle-level) distribution differs from the measured • Jet distributions are unfolded • “Response matrix” is created based on Monte Carlo • Correlates generated with reconstructed quantities • Number of jets • Leading jet pT • In principle, matrix is inverted and applied to data • The Singular Value Decomposition (SVD) method is used to unfold data PhD Thesis Defense

  48. Unfolding DataExclusive Jet Multiplicity Exclusive jet multiplicity Generated MC Reconstructed Data Unfolded Data • Response matrix from the Z+Jets, Z2 Tune MadGraph Monte Carlo • Unfolding performed on exclusive jet bins • Data yields corrected for selection efficiency • Sources of systematic uncertainty • Different unfolding algorithm • SVD vs. Bayes • Different Tune • Z2 vs. D6T • Different Generator • MadGraph vs. Pythia Ratio with MadGraph Z2 MC Reconstructed/Generated • Unfolded/Generated Exclusive jet multiplicity PhD Thesis Defense

  49. Unfolding DataLeading Jet pT Leading jet pT Generated MC Reconstructed Data Unfolded Data • Corrected leading jet pT • Response matrix from the Z+Jets, Z2 Tune MadGraph Monte Carlo • Jet resolution was studied to select optimal bin width • Bin sizes correspond ~2σ of the jet resolution in that pT region • Minimizing bin-to-bin migrations • Sources of systematic uncertainty • Different unfolding algorithm • SVD vs. Bayes • Different Tune • Z2 vs. D6T • Different Generator • MadGraph vs. Pythia Ratio with MadGraph Z2 MC Reconstructed/Generated • Unfolded/Generated Leading Jet pT PhD Thesis Defense

  50. Analysis Flow Fit distributions A L L E V EN T S Electron Collection ZCandidates Correct yields for reconstruction efficiency Signal yields vs. # jets Unfold jet multiplicity and leading jet pT Jet Collection • Ratio plots • σ(Ζ+n jets) / σ(Ζtotal) • σ(Ζ+n jets) /σ(Ζ+(n-1) jets) PhD Thesis Defense