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Benchmark QCD Measurements and Tools at ATLAS

Benchmark QCD Measurements and Tools at ATLAS. Craig Buttar University of Glasgow. Outline. Soft physics: minimum bias and underlying event Measurements of PDFs for precision physics and BSM Jet algorithms and multijets. Low pt physics. Why measure min bias?.

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Benchmark QCD Measurements and Tools at ATLAS

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  1. Benchmark QCD Measurements and Tools at ATLAS Craig Buttar University of Glasgow

  2. Outline • Soft physics: minimum bias and underlying event • Measurements of PDFs for precision physics and BSM • Jet algorithms and multijets Craig Buttar, CTEQ07 Michigan May 2007

  3. Low pt physics

  4. Why measure min bias? Not exactly what the LHC was built for! But….. • Physics: measure dN/dh|h=0 • Compare to NSD data from SppS and Tevatron • MB samples for pile-up studies • Calorimeter • Physics analyses • Benchmark for sLHC • Overlap with underlying events • analyses eg VBF, Jets… • Demonstrate that ATLAS is operational • Inter-calibration of detector elements • Uniform events • Alignment • Baseline for heavy ions Craig Buttar, CTEQ07 Michigan May 2007

  5. η=2.0 interaction point η=3.8 +η pannel Beam-pipe MBTS MBTS • Trigger scintillation counters mounted on end of LAr calorimeter covering same radii as ID UA5 • To compare to UA5 and CDF data need to understand composition of the sample  trigger bias • Currently generate inelastic and diffractive parts using PYTHIA • Need to investigate other simulations-PHOJET Craig Buttar, CTEQ07 Michigan May 2007

  6. Minimum bias measurements Solve low pt tracking ie down to ~100MeV M.Leyton Craig Buttar, CTEQ07 Michigan May 2007

  7. MB measurements? • Can we measure such distributions over limited rapidity coverage |h|<2.5? • Charged multiplicities • N vs <pt> • dN/dpt • MC simulation to map physics -- > trigger • MBTS 2<|h|<4  single diff+double diff+non-diff • Required to compare to UA5 etc d-gaussian gaussian Uniform Craig Buttar, CTEQ07 Michigan May 2007

  8. High PT scatter Beam remnants ISR The underlying event LHC PYTHIA6.214 - tuned PHOJET1.12 Transverse < Nchg > x 3 x1.5 • Extrapolation of UE to LHC is unknown • Depends on • Multiple interactions • Radiation • PDFs • Striing formation • Lepton isolation • Top • Jet energy • VBF Craig Buttar, CTEQ07 Michigan May 2007

  9. Reconstructing the underlying event A.Moraes Njets > 1, |ηjet| < 2.5, ETjet >10 GeV, |ηtrack | < 2.5, pTtrack > 1.0 GeV/c Ratio <NTrackReco>/<NTrackMC> CDF Run 1 underlying event analysis Phys. Rev. D, 65 092002 (2002) Craig Buttar, CTEQ07 Michigan May 2007 Leading jet ET (GeV)

  10. Underlying event for different processes R.Field • The underlying event for electroweak processes needs to be studied • Critical for Higgs search in VBF Craig Buttar, CTEQ07 Michigan May 2007

  11. “Leading Jet” “Back-to-Back” Resolving hard and soft components • TransMAX and transMIN sensitive to radiation and soft UE respectively • Back-to-back sample suppresses radiation • difference between tranMAX region and transMIN in leading jet and b-2-b jet sample Craig Buttar, CTEQ07 Michigan May 2007

  12. Parton Level Calibration: Jet Algorithms in Pt Balance S.Jorgensen Too close to the generation cut • Cone 0.4 collects only the core of the jet • Leakage out of cone and UE compensate in cone 0.7 • Excess of energy in Kt jets (D=1) due to UE and noise cone 0.4 cone 0.7 (pTγ+pTparton)/2 (pTγ+pTparton)/2 Differences between recon and particle levels related to the standard H1 weighting (calibrated for cone 0.7) Biases on pT balance MOP for the different jet algorithms: Kt (pTγ+pTparton)/2 Craig Buttar, CTEQ07 Michigan May 2007

  13. A.Moraes Extrapolation to LHC energies Transverse < Nchg > LHC LHC PYTHIA6.214 - tuned PHOJET1.12 x5 Transverse < Nchg > x 3 x4 x3 x1.5 Pt (leading jet in GeV) Pt (leading jet in GeV) Tevatron No agreement amongst MC Energy extrapolation is a tunable parameter Craig Buttar, CTEQ07 Michigan May 2007

  14. Simulation of underlying event • MC tools for simulation of underlying event • PYTHIA (UE+min bias) • Herwig + Jimmy (UE only, pt-cut) • PHOJET (Min bias and UE) • All give a reasonable description of Tevatron data with tuning (pt-min, matter distributions) • Energy extrapolation is essentially a free parameter and uncertain  data required • SHERPA also has simulation of underlying event but has been studied less Craig Buttar, CTEQ07 Michigan May 2007

  15. PDFs

  16. SM 2XD 4XD 6XD Impact of PDF uncertainty on new physics • Extra-dimensions affect the di-jet cross section through the running of as. Parameterised by number of extra dimensions D and compactification scale Mc. • PDF uncertainties (mainly due to high-x gluon) reduce sensitivity to compactification scale from ~5 TeV to 2 TeV S.Ferrag Mc= 2 TeV Mc= 6 TeV Mc= 2 TeV PDF uncertainties • Similarly PDF uncertainties limits the sensitivity in inclusive xsect to BSM physics Craig Buttar, CTEQ07 Michigan May 2007

  17. Measure high-x gluon pdfs from inclusive jet cross-section • Measure inclusive xsect to get high-x gluons • Measure in different rapidity bins • New physics vs pdf • Theoretical uncertainties in QCD calculation • Scale dependence • PDF uncertainty • Use NLOJET++ and CTEQ via LHAPDF • Experimental errors • Jet energy scale Craig Buttar, CTEQ07 Michigan May 2007

  18. Uncertainty due to high-x gluon PDF NLOJET++/CTEQ6.1(29+30) Other pdfs contribute At low pt (NLO) At 1TeV in central region error is 10-15% D.Clements Craig Buttar, CTEQ07 Michigan May 2007

  19. Scale errors From changing scale µr=µf from 0.5pT jet to 2.0pT jet 5%-10% scale error. D.Clements Craig Buttar, CTEQ07 Michigan May 2007

  20. Experimental Errors 10% JES  6% on s 5% JES  30% on s 1% JES  6% on s JES can measued to ~1% using Wjj in top events, can also use g-j, Z-j etc But need to “bootstrap” from ~500GeV to ≥ 1TeV region D.Clements Craig Buttar, CTEQ07 Michigan May 2007

  21. Craig Buttar, CTEQ07 Michigan May 2007

  22. Checking JES uncertainty at high Et • Bootstrap JES from 1% measured at low Et with Wjj in top, g-jet to high Et using jet-balancing • Truth jets • Can identify 1% change in JES with increasing Et • Reconstruction • Harder to see 1% due to resolution effect Truth jets Reconstructed Craig Buttar, CTEQ07 Michigan May 2007

  23. Analysis – Constraining High x-Gluon Effect of adding simulated ATLAS collider data to gluon uncertainty in a global PDF fit (C. Gwenlan) Gluon uncertainty x x • A very good control (1%) of the Jet Energy Scale is needed in order to constrain PDFs using collider data. Craig Buttar, CTEQ07 Michigan May 2007

  24. At the LHC we will have dominantly sea-sea parton interactions at low-x And at Q2~M2W/Z the sea is driven by the gluon by the flavour blind g ->qq gluon is far less precisely determined for all x values Measurement of W and Z rapidity distributions can improve our knowledge of the gluon PDF  key to using W,Z as luminosity monitor _ Improving low-x gluon using rapidity distribution in W-decay |h|<2.5 Cooper-Sarkar, Tricoli Craig Buttar, CTEQ07 Michigan May 2007

  25. GOAL: syst. exp. error ~3-5% W and Z Rapidity Distributions for different PDFs Analytic calculations: Error bands are the full PDF Uncertainties CTEQ6.1M MRST02 ZEUS-S At y=0 the total W PDF uncertainty is ~ ±5.2% from ZEUS-S ~ ±3.6% from MRST01E ~ ±8.7% from CTEQ6.1M ZEUS to MRST01 central value difference ~5% ZEUS to CTEQ6.1 central value difference ~3.5% (From LHAPDF eigenvectors) CTEQ6.1M Cooper-Sarkar, Tricoli Craig Buttar, CTEQ07 Michigan May 2007

  26. PDF constraining potential of ATLAS ZEUS-PDFBEFORE including W data ZEUS-PDFAFTER including W data e+CTEQ6.1 pseudo-data e+CTEQ6.1 pseudo-data |h| Effect of including the ATLAS W Rapidity “pseudo-data” in global PDF Fits: how much can we reduce the PDF errors when LHC is up and running? Simulate real experimental conditions: Generate 1M “data” sample with CTEQ6.1 PDF with ATLFAST detector simulation include this pseudo-data (with imposed 4% error) in the global ZEUS PDF fit (with Det.->Gen. level correction). Central value of ZEUS-PDF prediction shifts and uncertainty is reduced: low-x gluon shape parameter λ, xg(x) ~ x –λ BEFORE λ = -0.199 ± 0.046 AFTER λ = -0.181 ± 0.030  35% improvement |h| Craig Buttar, CTEQ07 Michigan May 2007 Cooper-Sarkar, Tricoli

  27. jets

  28. Jet Finders in ATLAS: Implementations • General implementation • All jet finders can run on all navigable ATLAS data objects providing a 4-momentum through the standard interface • Tasks common to different jet finders are coded only once • Different jet finders use the same tools • Default full 4-momentum recombination • Following Tevatron recommendation • Cone jets • Seeded fixed cone finder • Iterative cone finder starting from seeds • Free parameters are: seed Et threshold (typically 1 GeV) and cone size R • Needs split and merge with overlap fraction threshold of 50% • Seedless cone finder • Theoretically ideal but practically prohibitive • Each input is a seed • New fast implementation in sight: G.P.Salam & Gregory Soyez, A practical seedless infrared safe cone jet algorithm,arXiv:0704.0292 • No split and merge needed • MidPoint cone • Seeded cone places seeds between two large signals • Still needs split and merge Craig Buttar, CTEQ07 Michigan May 2007

  29. P.A.Delsart, (U. Montreal) ATLAS T&P Week March 2006 CPU time (arb. units) Jet Finders in ATLAS: Implementations • Dynamic Angular Distance Jet Finders • Kt algorithm • Fast implementation available → no pre-clustering to reduce number of input objects needed anymore • “Aachen” algorithm • Similar to Kt, but only distance between objects considered (no use of Pt) • Optimal Jet Finder • Based on the idea of minimizing a test function sensitive to event shape • Uses unclustered energy in jet finding Craig Buttar, CTEQ07 Michigan May 2007

  30. Jet Finders in ATLAS: Algorithm Parameters • Adjust parameters to physics needs • Mass spectroscopy W →jj in ttbar needs narrow jet • Generally narrow jets preferred in busy final states like SUSY • QCD jet cross section measurement prefers wider jets • Important to capture all energy from the scattered parton • Common configuration • ATLAS, CMS, theory • J.Huston is driving this • Likely candidate two-pass mid-point mW N.Godbhane, JetRec Phone Conf. June 2006 P.-A. Delsart, JetRec Phone Conf. June 28, 2006 Craig Buttar, CTEQ07 Michigan May 2007

  31. Dfdijet= p 2p/3Dfdijetp Dfdijet~2p/3 p/2Dfdijet2p/3 A.Moraes Azimuthal dijet decorrelation Early measurement to benchmark generators particularly parton showers/higher orders Craig Buttar, CTEQ07 Michigan May 2007

  32. Reconstructed di-jet azimuthal decorrelations A.Moraes Selecting di-jet events: J5 Cone jet algorithm (R=0.7) Njets = 2, |ηjet| < 0.5, ETjet #2 > 80 GeV, Two analysis regions: 300 < ETMAX < 600 GeV 600 < ETMAX < 1200 GeV J6 Craig Buttar, CTEQ07 Michigan May 2007

  33. Spectra include tt Multijets in top events A.P.Colijn • MC@NLO and ALPGEN agree for hardest jet • HERWIG fails at high pt • Significant number of events have 3 additional jets there is a discrepancy between MC@NLO and HERWIG vs ALPGEN •  measure multijet spectra • Possible with early high energy running • Key for ttH Craig Buttar, CTEQ07 Michigan May 2007

  34. Summary and conclusions • QCD benchmarks (inc low-pt processes) • Underlying event • Fundamental part of hadronic environment that needs to be understood • Study soft and hard part • Measure for different processes – QCD vs EW • PDFs • New regime in PDFs • Need to measure for precision SM and high-pt BSM physics • Multijets • Measure azimuthal decorrelations to validate simulations • Many more jets in events tt6J+nJ • Need to understand multiplicities Craig Buttar, CTEQ07 Michigan May 2007

  35. Extra slides

  36. Tevatron  LHC stot Q2 (GeV) Craig Buttar, CTEQ07 Michigan May 2007

  37. Low pt tracking efficiency and fake rates M.Leyton Craig Buttar, CTEQ07 Michigan May 2007

  38. LHC prediction Tevatron Minimum bias and Underlying Event: LHC predictions dN/dh in minimum bias events Particle density LHC PYTHIA6.214 - tuned Tevatron ● CDF 1.8 TeV ● CDF 1.8 TeV ~80% ~200% Pt leading jet (GeV) PYTHIA6.214 - tuned Minimum bias = inelastic pp interaction Underlying event = hadronic environment not part of the hard scatter UE includes radiation and small impact parameter bias MB only Craig Buttar, CTEQ07 Michigan May 2007

  39. 1000 events dNch/d h Black = Generated (Pythia6.2) Blue = TrkTrack: iPatRec Red = TrkTrack: xKalman dNch/dpT Reconstruct tracks with: 1) pT>500MeV 2) |d0| < 1mm 3) # B-layer hits >= 1 4) # precision hits >= 8 pT (MeV) Tracking in MB events • Acceptance limited in rapidity and pt • Rapidity coverage • Tracking covers |h|<2.5 • pT problem • Need to extrapolate by ~x2 • Need to understand low pt charge track reconstruction Craig Buttar, CTEQ07 Michigan May 2007

  40. Minimum bias studies: Charged particle density at  = 0 Why? soft physics, pile-up at higher luminosities, calibration of experiment LHC? Large uncertainties in predicted particle density in minimum bias events ~x2  Measurement with central tracker at level of ~10% with ~10k events – first data Craig Buttar, CTEQ07 Michigan May 2007

  41. Use MB multiplicity distributions to tune fluctuations in number of events d-gaussian smooth Uniform abrupt gaussian Compare abrupt and smooth pt-cut-off: Abrupt cut-off generates a Poisson distribution with too few multi-parton interactions in a single event Compare matter distributions: uniform, gaussian, double gaussian  Use double gaussian Craig Buttar, CTEQ07 Michigan May 2007

  42. Herwig+Jimmy • Jimmy is multi-parton interaction model similar to PYTHIA • Main parameter is pt-min • Only for hard UE cannot model low-pt ie MB difficult to get energy dependence • Matter distribution is determined from em form factor • Gives a good description of CDF data with increased pt-min2.53.25GeV Craig Buttar, CTEQ07 Michigan May 2007

  43. Tagging jet W Z/W H Z/W W Tagging jet VBF Signal (HWWll) • forward tagging jets • correlated isolated leptons • low hadronic activity in central region • central Higgs production Important discovery channel For Higgs in mass range 120-200GeV Craig Buttar, CTEQ07 Michigan May 2007

  44. Uncertainty at the level of ~6% on CJV Pt>20GeV and ~3% on lepton Giving a total uncertaintly in the range ~8% Craig Buttar, CTEQ07 Michigan May 2007

  45. Lepton isolation in H->4m S.Abdullin et al (CMS) Les Houches 05 • Effect of UE on lepton efficiency • Vary pt-min by 3s • Determine from data • Good muons • Barrel h<1.1 Pt>7GeV • Endcap 1.1<h<2.4 P>9GeV • Isolation • SPt for charged tracks excluding ms with Pt>0.8GeV and DR<0.3 around m in h-f space Craig Buttar, CTEQ07 Michigan May 2007

  46. Extract effect of UE from data • Use inclusive Z-sample, high statistics • Similar dependence to ZZ sample but small systematic shift m m UE m m random Craig Buttar, CTEQ07 Michigan May 2007

  47. Impact of decreasing experimental systematic uncertainty-uncorrelated Craig Buttar, CTEQ07 Michigan May 2007

  48. JES extrapolation • Bootstrap JES to high Et using jet-balancing • Truth jets • Can identify 1% change in JES with increasing Et • Reconstruction • Harder to see 1% due to resolution effect Truth jets Reconstructed Craig Buttar, CTEQ07 Michigan May 2007

  49. Impact of decreasing experimental correlated systematic uncertainty Challenging! Can we decrease Jet Energy Scale systematic to 1%? Craig Buttar, CTEQ07 Michigan May 2007

  50. Initial considerations Jets define the hadronic final state of basically all physics channels Jet reconstruction essential for signal and background definition Applied algorithms not necessarily universal for all physics scenarios Which jet algorithms to use? Use theoretical and experimental guidelines collected by the Run II Tevatron Jet Physics Working Group J.Blazey et al., hep-ex/0005012v2 (2000) Theoretical requirements Infrared safety Artificial split due to absence of gluon radiation between two partons/particles Collinear safety Miss jet due to signal split into two towers below threshold Sensitivity due to Et ordering of seeds Invariance under boost Same jets in lab frame of reference as in collision frame Order independence Same jet from partons, particles, detector signals collinear sensitivity (1) (signal split into two towers below threshold) infrared sensitivity (artificial split in absence of soft gluon radiation) collinear sensitivity (2) (sensitive to Et ordering of seeds) Jet Algorithm Choices: Guidelines for ATLAS Craig Buttar, CTEQ07 Michigan May 2007

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