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How not to use the Monte Carlo

How not to use the Monte Carlo. Stan Bentvelsen Nordic LHC workshop. Remarks 1. Proton-proton collisions are extremely complex Detectors like Atlas and CMS are extremely complex Do not thrust too much your event generator Do not thrust too much your detector simulation

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How not to use the Monte Carlo

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  1. How not to use the Monte Carlo Stan Bentvelsen Nordic LHC workshop

  2. Remarks 1 Proton-proton collisions are extremely complex Detectors like Atlas and CMS are extremely complex Do not thrust too much your event generator Do not thrust too much your detector simulation without explicit and full checks with the ‘data’ itself ‘Data’ can be any of the following and more: • Other experiments (Tevatron) • Internal (sub-) detector consistency • Test-beam • Cosmic rays • Proton-proton collisions Stan Bentvelsen Nordic LHC

  3. Remarks 2 For reliable results the game to play is: reduce the dependency on MC as much as possible (best to eliminate any dependence) ‘Shake down’ of detector (simulation) in many ways • Redundancy between detectors • Straight tracks, energy clusters, etc, etc… Use Physics: available ‘candlelight’ signals • Mass of the J/ψ, W±, Z0, top-quark • Presence of b-jets Use constraints: e.g. energy-momentum • Difficult in pp collisions: Partonic cm not known • Balance in PT Stan Bentvelsen Nordic LHC

  4. Remarks 3 This does not mean to sit back and wait for data to come! Make clever use of MC to construct ‘MC correction free’ observables Realistically not always possible – find balance This talk: just warn against using MC and simulation ‘blindly’… Stan Bentvelsen Nordic LHC

  5. Use of MC’s Not so straightforward to talk for one hour about “how not to use the Monte Carlo” Instead, I like to discuss a few examples in which “awareness of Monte Carlo limitations” plays an important role It is also closely connected to “commissioning of the LHC detectors” Stan Bentvelsen Nordic LHC

  6. Preliminaries: Every generated physics process consists of two parts: Hard process: Obtained using perturbative (LO,NLO) calculation of the probability amplitude (matrix element) Soft(er) effects: Initial and final state radiation (DGLAP parton showers) Underlying event Fragmentation and hadronisation See talk by Leif Lonnblad Event generation Stan Bentvelsen Nordic LHC

  7. How not to use the event generator “Pythia tells me that W+multijets is negligible background for my top study” • Make conscious choice of event generator • Is process (with phase space) implemented in the Generator? • ttbar spin-correlations not implemented in Pythia or MC@NLO • Don’t take ‘Herwig’ or ‘Pythia’ as the absolute truth • Clarify what aspect you want to test with the generator • Sensitivity to underlying matrix element • E.g. multi-jet physics • Sensitivity to soft component • E.g. exclusive resonance study • Underlying events In most practical cases both! Stan Bentvelsen Nordic LHC

  8. Ignore new/better calculations? • A major step forward occurred with the introduction of NLO generator MC@NLO • Full NLO QCD calculations • Practicalities: Deal with negative weights: ±w • Many generators available for multi-parton final states • AlpGen, VecBos, AcerMC, Sherpa See Leif's talk! Stan Bentvelsen Nordic LHC

  9. ttbar system: MC@NLO, Herwig, Pythia • PT(tt system) • Herwig & MC@NLO agree at low PT, • At large PT MC@NLO ‘harder’ • PYTHIA completely off Example: distributions on top-anti-top characteristic – PT of the whole system PT of t-tbar system is balanced by ISR & FSR Huge difference in PT from ‘ISR’, MC@NLO coincides with NLO QCD calculations Stan Bentvelsen Nordic LHC

  10. Next step: the simulation • Detector response • Parameterization, smeared • Simple detector geometry (e.g. cells in grid eta-phi) • Smear 4 vector of final state particles • Photons: resolution by EM calorimeter • Electrons, muons: resolutions by EM calorimeter and Inner Detector • Hadrons: collected in cells – cell smearing – jet finding • E.g. b-tagging of jets implemented by overall tagging efficiency and truth information. • Detailed simulation of material interactions • Geant4 (C++) packages (Geant3 is currently phased out) • Detailed description of material interactions of the detector • Detailed detector geometry description • Definition of ‘sensitive materials’: energy lost accumulated • Creation and tracking of daughter particles: shower development Stan Bentvelsen Nordic LHC

  11. Digitization Transform accumulated energy deposits into detector output Energy deposit in Si wafer  readout channel Physics modeling quite involved Digitization / reconstruction Stan Bentvelsen Nordic LHC

  12. Simulation/data analysis flow Services: -Atlas geometry -Alignment dBase Event generation Event generation (Pythia…) Simulation response Fast simulation / parametrization Energy depositionsHits Raw Data Objects G4 Sim Digitization Reconstruction PileUp Physics analysis objects Bytestream ATLAS Postscript (publications) Atlas detector response Reconstruction and analysis Stan Bentvelsen Nordic LHC

  13. How not to use the simulation Fast / full simulation have both their merits and are useful • Judge for each problem what to use • CPU power available • Time • Status • Probably not efficient to: • Study cracks in the calorimeter with parameterized simulation • Asses signal of various models of black hole evaporation with full simulation (event generation should be enough!) • If time and CPU power permits – full simulation seems always ‘better’ • But life is not that straightforward • ‘back of envelope’ calculations often useful to test new ideas Rule of thumb: Detector simulation takes 1s for 1 GeV dumped energy on modern PC. I.e. ~1 hour to generate 1 ‘heavy’ event Stan Bentvelsen Nordic LHC

  14. How to check the Monte Carlo itself? • Tune the Monte Carlo response with test-beam data • Absolutely essential! • Few examples in last part of this talk • Internal consistency checks • Does the detector respond symmetrically in z? • Uniform in φ? Any other symmetry axis? • Inspection of the geometry – material distribution • All detectors-components installed that should be installed? • Compare the total ‘weight’ of a sub-detector with the weight in the simulation Stan Bentvelsen Nordic LHC

  15. Validate the full Monte Carlo simulation itself. Is geometry correct? Monte Carlo validation TRT C-wheels missing R (cm) Location of secondaries from truth. Z (cm) 2nd layer pixel missing Stan Bentvelsen Nordic LHC

  16. A few case studies • Using data to set the energy scales • Calorimeter scale: using Z0 • B-jet scale: using Z0 • Et-miss • Using data to check/study data • B-jet calibration • W-mass determination • Top physics • Underlying event • B-taggin efficiencies • Non W QCD background • Testbeam • We have already data! What do we learn from that? Stan Bentvelsen Nordic LHC

  17. Which physics in first year? Event rates in ATLAS or CMS at L = 1033 cm-2 s-1 Low lumi Already in first year, large statistics expected from: -- known SM processes  understand detector and physics at s = 14 TeV -- several New Physics scenarios Stan Bentvelsen Nordic LHC

  18. Energy scale calibration • Make use of physics signals to understand the detector • Abundance of Z and W particles being produced • Top quark • Various combinations of these with associated particles • Z-boson: • Properties extensively determined at LEP • Mass and width known up-to approx 2 MeV • Mass and couplings described by Standard Model • W-boson: • Current precision on mass approx 42 MeV • Ultimate goal at LHC to bring down to ~15 MeV • Top-quark • Current mass at 178±4 GeV • Ultimate goal at LHC aprox 1 GeV Stan Bentvelsen Nordic LHC

  19. Z0e+e- calibration Use the Z0 mass to calibrate the EM calorimeter Can we get non-uniformity and absolute energy scale from data? • Divide the calorimeter in regions i • Introduce bias for each region by • The Z0 invariant mass • Can be written as: • By giving all αia suitable variance, a likelihood fit can be constructed • Determine βij with lots of Z0 • Untangle the αi Stan Bentvelsen Nordic LHC

  20. Method works well From parametrized MC study a good correlation is observed between the fitted and injected values for α Test method on full simulation events Expect non-uniformity due to material distribution Ze+e- calibration Difference αinjand αfit Integrated over regions in φ as function of η Energy-loss due to material Stan Bentvelsen Nordic LHC

  21. Hadronic shower components • A hadronic shower consists of • EM energy (e.g. ), O(50%) • Visible non-EM energy (e.g. dE/dx from , , etc), O(25%) • Invisible energy (e.g. breakup of nuclei and nuclear excitation) O(25%) • Escaped energy (e.g. Ν) • Each fraction is E dependent and subject to large fluctuations Invisible energy is the main source of the non-compensating nature of hadron calorimeters Calibration has to take into account both visible and invisible energy fractions: delicate process Energy scale of jets can have miscalibration as large as 5-10% Stan Bentvelsen Nordic LHC

  22. Jet energy calibration Can we utilize the EM scale to say something on hadronic scale? • Use lepton balancing in PT to calibrate the jet energy • Again – use the MC to check if the method works in an unbiased way. • But method should be independent of MC as possible • Jets energy calibration not straightforward • Both hadronic and electromagnetic energy content. • No unambiguous assignment of energy-flow to a jet. • Which particles belong to the jet and which don’t? Stan Bentvelsen Nordic LHC

  23. B-jet response different from light quark jet fragmentation – invisible component … Rely on relatively rare process Process : g + b  b + Z0  b jet + +- Constraint : pT(b)  pT(Z0) First estimation of calibration constant :  = pT(2)/ pT(jet) Use precise muon tracking to study the scale of b-jets M(Z)=91.2 GeV (b-tagged) Jet Calibration of b-jets using Z or  • MC study: • g + q  q + Z0 signal • q + q  g + Z0 with g qq background Jet reconstruction with Cone algorithm R = 0.7 pTseed = 5 GeV; pT threshold = 15 GeV Stan Bentvelsen Nordic LHC

  24. Event selection: Z0+jet Statistics not great for Z0+jet – but enough to do the analysis -1 bjet + +- reproducing invariant mass of Z0 -no photons, no electrons Event generated : 30 000 000; events selected : 20 000 Expected number of events in 3 years of low luminosity runs Stan Bentvelsen Nordic LHC

  25. Imbalance in PT Due to Initial State Radiations : • the pT balance is not fully verified • the reconstructed b jet can come from ISR Stan Bentvelsen Nordic LHC

  26. How to deal with imbalance? • Use the MC to evaluate the balance between pT(b) and pT() • Introduce dependence on ISR of MC • Check the MC by evaluate distribution in φ • K= (pT(jet) + pT().cos(φ/2) is sensitive to ISR • φ is the angle between pT(jet) and pT() • RMS (K) can be used to evaluate ISR effects from real data side φ Stan Bentvelsen Nordic LHC

  27. Potential of  + jet The statistics is much better in this case Analysis: •  selection, optimized for jet rejection • Opposite hemisphere : most energetic jet + loose back-to-back requirement ±0.3 in azimuth Imbalance between photon and quarks, particles, EM jets and Had jets Example Cone jet R=0.7 Stan Bentvelsen Nordic LHC

  28. +jet or Z0+jet in-situ processes Which useful information can be extracted from these processes ? • Pro’s •  and Z0are “electromagnetic objects”, calibrated at a well-known scale • the selection of the jet can beindependent of the jet finding algorithm, jet fragmentation, etc... by selecting simply the highest ET jet in the opposite hemisphere to the  or Z0 • comparative jet algorithm studies can be done: difference in calibration between different jet algorithms, relative efficiencies, etc. • +jet with pT>20 GeV: ~ 10k events in 1 minute but  identification efficiency & trigger! • can be used to calibrate calorimeter region with dead material, uniformity scans, monitoring, etc. Stan Bentvelsen Nordic LHC

  29. +jet or Z0+jet in-situ processes • Con’s •  or Z0is not an unbiased estimator of the back-to-back parton because of ISR and FSR • Difficulty for absolute energy scale : this applies particularly in the low pT range up to ~ 40 GeV • the background to the  or Z0 may bring an additional bias • The background will be more severe for ’s that for Z0 • the pT range covered with good statistics may be limited • The effect of the trigger has also to be considered (standard menu or downscaled) Stan Bentvelsen Nordic LHC

  30. W-mass determination

  31. W-mass determination Stan Bentvelsen Nordic LHC

  32. Relevant quantities Hadronic recoil U PT of the muon I.e. for correct transverse mass MT the PT of the recoil U is needed Straightforward MC method: Construct MC ‘template’ for MT by taking into account: Initial State Radiation Angular distributions Recoil model Detector resolution Fit template to data to extract MW Not the best method! Heavily rely on details of event generation, Monte Carlo simulation. Can one be a bit clever and reduce dependency on MC? Measurement by comparing MC ν  W up anti-down Hadronic recoil Stan Bentvelsen Nordic LHC

  33. Use data from process Z Factor 10 lower cross section, but with abundant Z production no problem Substitute one muon by a neutrino Z-decays have almost identical topology Calculate MT(Z) from remaining muon and recoil Use the precise knowledge of MZ Transform MT(Z) distribution to MT(W) distribution and extract MW/MZ. Take difference of production mechanism into account Reduce MC dependency MC only needed to predict effect of topology difference (small effect) Determination of a ratio and MZ to determine MW MW by comparison to Z data   Z up anti-down Hadronic recoil Stan Bentvelsen Nordic LHC

  34. To compare W and Z samples: Use technique to create MT templates for arbitrary mass and width: Use both muons to reconstruct Z Boosts muons into Z rest frame Set Z mass to arbitrary new value MX, and width ΓX. Consider one muon to be a neutrino Calculate outgoing 4 vectors Boost back into LAB frame Calculate MTX from recoil and remaining muon Compare many MTX templates with the observed, measured MTW distribution, to extract MW as MX. Analysis Stan Bentvelsen Nordic LHC

  35. Analysis and worries.. • Now MW can be determined from fit to templates MT(W) • Very small uncertainties • With 106 events uncertainty ~10 MeV (stat) • Test method on fully simulated events • Use MC to assess Final State Radiation effects • Neutrino does not radiate • Muon does radiate Completely different use of the MC! Stan Bentvelsen Nordic LHC

  36. Top physics

  37. Top studies • Top physics at LHC • Top one of ‘easiest’ bread and butter • Cross section 830±100 pb • Used as calibration tool • What variations in predictions of t-tbar – which generator to use? • Underlying event parameterization • Background estimation from MC • Tuning b-tagging at startup • Jet energy scale • Try to be as independent from MC as possible. Semi-leptonic top channel detector tools involved: Lepton reconstruction Missing ET Jets + calibration B-tagging Stan Bentvelsen Nordic LHC

  38. Hadronic side W from jet pair with closest invariant mass to MW Require |MW-Mjj|<20 GeV Assign a b-jet to the W to reconstruct Mtop Kinematic fit Using remaining l+b-jet, the leptonic part is reconstructed |mlb -<mjjb>| < 35 GeV Kinematic fit to the tt hypothesis, using MW constraints j2 j1 b-jet t Lepton + jet: reconstruct top W-mass • Selection efficiency 5-10% Stan Bentvelsen Nordic LHC

  39. j2 j1 b-jet Mtop t High Pt sample • The high pT selected sample deserves independent analysis: • Hemisphere separation (bckgnd reduction, much less combinatorial) • Higher probability for jet overlapping • Use all clusters in a large cone R=[0.8-1.2] around the reconstructed top- direction • Less prone to QCD, FSR, calibration • UE can be subtracted Mtop Statistics seems OK but what about syst? R Stan Bentvelsen Nordic LHC

  40. Underlying event evolution • It is not only minimum bias event! • The underlying event is everything except the two outgoing hard scattered jets. • In a hard scattering process, the underlying event has a hardcomponent (initial + final-state radiation and particles from the outgoing hard scattered partons) and a softcomponent (beam-beam remnants). CDF analysis: • charged particles: pt>0.5 GeV and |η|<1 • cone jet finder: UE is defined as the Transverse Region Df = f - fljet Stan Bentvelsen Nordic LHC

  41. LHC predictions: UE LHC Transverse < Nchg > x 5 x 4 x 3 Tevatron Pt (leading jet in GeV) Stan Bentvelsen Nordic LHC

  42. Top-quark production UE • Charged particle density in pseudorapidity: Tevatron and LHC predictions. Widly different predictions! Stan Bentvelsen Nordic LHC

  43. 10 GeV Jimmy UE: Cells & Jets • Herwig vs Jimmy • LO t-tbar • At jet-level effectreduced Cell multiplicity Cluster multiplicity Jet multiplicity Stan Bentvelsen Nordic LHC

  44. Top peak for various reconstruction methods Difference in mass can be as large as 5 GeV Really need data to check data on UE Study effect better with data itself!! Reconstruct the top Stan Bentvelsen Nordic LHC

  45. Background events • Top physics background • Mistags or fake tags • Non-W (QCD) • W+jets, Wbbar, Wccbar • Wc • WW,WZ,ZZ • Z  tt • Single top • ~ 150 pb-1 W+4jet background • Not completely trivial to generate Can we observe the top without b-tagging? Largest background is W+4 jet. This background cannot be simulated by Pythia or Herwig shower process. Dedicated generator needed: e.g. AlpGen. Large uncertainties in rate Ultimately, get this rate from data itself. For example, measure Z+4 jets rate in data, and determine ratio (Z+4 jets)/(W+4 jets) from MC W+4 extra light jets Jet: Pt>10, ||<2.5, R>0.4 No lepton cuts Stan Bentvelsen Nordic LHC

  46. Signal plus background at initial phase of LHC Most important background for top: W+4 jets Leptonic decay of W, with 4 extra ‘light’ jets Non btag: top sample • Selection: • Isolated lepton with PT>20 GeV • Exactly 4 jets (R=0.4) with PT>40 GeV • Reconstruction: • Select 3 jets with maximal resulting PT L = 150 pb-1 (2/3 days low lumi) With extreme simple selection and reconstruction the top-peak should be visible at LHC Stan Bentvelsen Nordic LHC

  47. Extraction of top signal • Fit to signal and background • Gaussian signal • 4th order polynomal Chebechev background 150 pb-1 Extract cross section and Mtop? Stan Bentvelsen Nordic LHC

  48. Select the 2 jets with highest resulting PT W peak visible in signal No peak in background Better ideas well possible! E.g. utilizing 2 body decay in top rest frame. Select 2 jets with invariant mass closest to Mw (80.4 GeV) Large peak in background Enormous bias Not useable! Can we see the W? (4 jets sample) 150 pb-1 Stan Bentvelsen Nordic LHC

  49. Fit to W mass • Fit signal and background also possible for W-mass • Not easy to converge fit With W sample we can check the jet energy scales Stan Bentvelsen Nordic LHC

  50. Jet Energy scale / MC dependence • Variation of the jet energy scale to infer systematics • Bjet scale: 0.92 – 0.96 – 1.00 – 1.04 – 1.08 • Light scale: 0.94 – 0.98 – 1.00 – 1.02 – 1.04 (1) (2) (3) (4) (5) • Analysis with jet energyscaled • All with MC@NLO, Herwigand Pythia; • Redo analysis with doubled W+4jet background (stat indep) • Determine Mtop and σ(top) • ‘Raw’, i.e. no correction for jet scale • ‘Corrected’, i.e. apply percentage difference of W-peak to the reconstructed top • Dependence on top mass reduced by scaling with W: • Rms Raw: 6.2 GeV • Rms Scaled: 1.2 GeV ‘Raw’ Top mass Top mass rescaled using W constraint Stan Bentvelsen Nordic LHC

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