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- Calibration for jets

- Calibration for jets. Reminder on the DØ detector Jet Identification and Reconstruction Jet Energy Scale: results from Run 1 b-jet calibration. Run II Detector. - Calorimeter. Fine segmentation: semi-projective towers in 0.1 x0.1 4 em layers: 2, 2, 7, 10 X 0

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- Calibration for jets

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  1. - Calibration for jets • Reminder on the DØ detector • Jet Identification and Reconstruction • Jet Energy Scale: • results from Run 1 • b-jet calibration

  2. Run II Detector

  3. - Calorimeter • Fine segmentation: • semi-projective towers in 0.1 x0.1 • 4 em layers: 2, 2, 7, 10 X0 • shower-max (EM3): 0.05 x 0.05 • 4/5 hadronic (FH + CH) • hermetic with full coverage • || < 4.2 (  2o) • int > 7.2 (total) • Uranium absorber (Cu (CC) or Steel (EC) for coarse hadronic) • compensating e/  1 • to be studied with shorter shaping from test beam data e: sE/E= 15% /ÖE+ 0.3% p: sE/E= 45% /ÖE + 4% Beam Tests of the D0 Uranium Liquid Argon Calorimeter.NIM A324, 53 (1993) NIM A 338 185 (1994)

  4. 250 MeV 0.25 ADC count/MeV Gain 8 1 GeV Gain  1 0 10 20 30 40 50 GeV 0 4 ~Energy/GeV SCA non-linearity • functional form of SCA non-linearity correction function • correction important at low energies • electronic noise translates into higher energy • jet become more narrow for energies > 200MeV non-linearity introduces an offset of ~250 MeV for the gain 8 measurements

  5. Central Jet Triggers • L2 jet • Cluster 3x3 or 5x5 trigger towers around L1 seed towers • L3 jet • Simple cone or tower NN algo’s 0.1x0.1 towers • 3 single jet (tower) triggers: • JT_LO L1: 5 GeV, L3:10 GeV • JT_HI L1:10 GeV, L3:15 GeV • CJT40: L1:40 GeV • Efficiency • standard jet selection, offline pT > 8 GeV • very sharp turn on Efficiency vs jet pT CJT(1,3) CJT(1,5) CJT(1,7) CJT(1,10) L1 Trigger efficiency CJT(1,x) L1 Trigger efficiency CJT(2,x) • L1 single jet efficiencies • ask for one or two hadronic trigger towers (0.2x0.2) above threshold • use -trigger as unbiased reference to measure turn-on • ask for one and only one reconstructed jet in ||<0.7 • L1 hadronic response about 40% low for current data set

  6. ETthresold ETneighbour> 100 MeV or 0.02Ecell NADA: noise reduction • NADA = New Anomalous Deposit Algorithm • identify isolated energy deposits in the calorimeter = “Hot Cells” • Source: electronics, Ur noise, beam splash, cosmics etc • Improve object resolution and ETmiss • Run 1: AIDA • Only examine neighbors in the same tower for Ecell > 10 GeV • 99% efficient, BUT 5-10% misidentification rate • examine all cells with > 1 GeV • remove cells < -1 GeV & > 500 GeV • ET < 5 GeV removed if no neighbor with E > 100 MeV • ET < 500 GeV removed if no neighbor with E > 2% Ecell • high efficiency (90%) and low misidentification • ET > 1 GeV : ~0.5% • ET > 10 GeV : ~0% • on average about 0.8 cells / event

  7. Calorimeter jet (cone) • jet is a collection of energy deposits with a given cone R: • cone direction maximizes the total ET of the jet • various clustering algorithms Jet Finding • correct for finite energy resolution • subtract underlying event • add out of cone energy • Particle jet • a spread of particles running roughly in the same direction as the parton after hadronization

  8. Jet Algorithms: Cone • Run 1 Legacy Cone • draw a cone of fixed size around a seed • compute jet axis from ET-weighted mean and jet ET from ET’s • draw a new cone around the new jet axis and recalculate axis and new ET • iterate until stable • algorithm is sensitive to soft radiation (split & merge) • Improved Run 2 cone • use 4-vectors instead of ET • add additional midpoint seeds between pairs of close jets • split/merge after stable proto-jets found • algorithm is infrared safe

  9. For each object and pair of objects: order all dii and dij: Ifdmin=dij  merge particles Collinear (if R<<1 ) If dmin=dii  jet Resolution parameter (D=1) Soft Jet Algorithms: kT • theoretically favored, no split-merge • to reduce computation time, start with 0.2 x 0.2 pre-clusters • x-section measurement differ from cone-jet (JETRAD) DØ Subjet multiplicity of gluon and quark jets reconstructed using the kT algorithm in pbarp collisions Phys. Rev. D65 052008 (2002) hep-ex/0108054The inclusive jet cross section in pbarp collisions at sqrt(s)=1.8 TeV using the kT algorithm Phys. Lett. B {525}, 211 (2002) hep-ex/0109041

  10. Hadronization effects? • particle jets are more (less) energetic than parton jets with kT (cone) • kT collects more energy • cone looses energy • kT jets are 7 (3)% more energetic at 60 (200) GeV than cone jets: • consistent with HERWIG at high pT, at 2 at low pT applying correction to cone-jets improves agreement between the 2 algorithms

  11. Jet Algorithms: CellNN & Flow • Cell Nearest Neighbor • layer-by-layer clustering starting with EM3 • each local maximum starts a layer-cluster then add in neighbors • energy sharing according to transverse shape parameterization • angular matching of floor clusters • search for minima in longitudinal energy distribution to separate EM and hadronic showers • Energy Flow algorithm • use tracking information to better characterize the contributions from charged particles • in development

  12. CHF EMF HotF n90  Data — MC Jet Selection DØ Run 2 Preliminary • central jets (Run 2 cone, R=0.7) • event quality cuts • number of jets  1 • Etotal in the calorimeter  2 TeV • missing ET  70% of leading jet pT • Zvtx < 50 cm • leading Jet Cuts • Jet pT > 8 GeV (offline cut) • 0.05  EMF  0.95 • CHF  0.4 (0.25 tight) • HotF  10 (5 tight) (HotF = ET1st cell / ET2nd cell ) • n90 > 1 (number of towers that contain 90% of jet ET) • efficiencies from MC • loose: ~100% tight: ~ 98% • ~Flat in  Non-linearity of SCA included in MC

  13. jet Jet Energy Scale correct Jet Energy to the particle level • Eoffsetenergy offset from underlying event, pile-up, noise determined from Min. Bias Events • Rcalocalorimeter response using -jet events: Missing ET Projection Fraction Method • Rshowerenergy contained in jet corrections from MC - energy in cones around the jet axis • depending on jet algorithm! Determination of the Absolute Jet Energy Scale in the D0 Calorimeters. NIM A424, 352 (1999), hep-ex/9805009

  14. E EOFF= EUE +NZB EUE +Enoise+Epile-up Run I: Offset corrections subtract contributions not associated to the high pt interaction: Ur noise, pile-up, multiple interaction, underlying even measured as ET densities D, to be multiplied by the area of a jet in  • measurement of the ET density D in zero bias event • measurement of DUEfrom minimum bias events DUE=DMB-DZBno HC

  15. ICR Run I: Offset corrections Ur noise, pile-up, multiple events underlying event contribution measured for different luminosities dominant error from occupancy dependence depends on s and process associated to a single event  independent of luminosity

  16. Run I: response correction using -jet events ideal calorimeter : jet response (with calibrated ): Ejetmeas: dependent on energy response and resolution, threshold effects and smearing better: E= ET cosh jet

  17. Run I: jet response • comparison of jet response in different cryostat regions • CC ||<0.7 • ICR 0.7<||<1.8 • EC 1.8 <||<2.5 • effect of finite jet resolution at E = 10GeV • lowest response in ICR: int < 6

  18. Run I: EC/CC correction independent of E as EC/CC similar in construction derived from overlap region of CC and EC response at 60<E<180 GeV Fncry/Fscry=0.997 0.003 EC response 2% below CC compared to the ratio of a fit to the 2 response functions

  19. Run I: ICR correction inhomogeneous detector material: correction as function of and ET high ET: jet-jet events low ET: -jet events  or leading jet required to be central (||< 0.5) fit of response as Rjet =  + b ln ET+ b ln (cosh ) correction derived from difference between measurement and the expectation for an ideal detector, extrapolated from fit at ||<0.5 and 2 < ||<2.5

  20. Run I: low ET bias • Etjet > 8 GeV • jet resolution ~50% • migration of low ET jets • jets fluctuating below Etmin are not reconstructed • bias of the response towards higher values • as response determined from Etmiss and , bias correction determined from:

  21. Run I: Response function • fit of the measured response function Rjet(E)=a+b ln E+c (ln E)2 • logarithmic terms justified by non compensation at low E • fit of CC and EC measurement for ET>30 GeV • at highest energy prediction from MC after tuning response on data in measured region • error band derived taking into account correlations

  22. Run I: Showering correction corrects for out-of-cone energy belonging to the jet scales reconstructed jet to particle level: S=Ejet/(Ejet+EshoMC) parameterizations for different cone sizes errors at low energy: offset subtraction; at high energy: stat shower correction depend on jet profiles, but not on s 1% 4% 10% 2.5% 5% 10%

  23. signal:152 M evts bkgd: 47.1 M evts signal: 64.8k evts bkgd: 650 evts /Z+jet QCD (udsg) QCD (cbt) W+jet, Z+X,  Run II: +jet / Z+jet • +jet: Run I method – jet calibration possible up to 250 GeV • Z+jet: lower statistics, but clean sample, useful at low energies, x-check!

  24. peak: 82.6 peak: 86.8 b-jet calibration • naïve reconstruction of Z-mass shows a lower mass for selected b-jets than light quark jets. • energy losses from semi-leptonic b decays (, ) • wider b-jets (due to the large b-mass)

  25. Z bb vs  + b-jet  + b-jet : • high statistics, allows for a tight b-jet selection (b-tagging). • expected number of tagged events: 1.2 M but: sensitive fractional imbalance I= (pT() - ET(jet))/ pT() Zbb: systematics closer to physics processes (H or Top) at high pT resonance mass independent of multiple interactions. but: signal/noise~10-3 requires special trigger (Silicon Track Trigger – operational end 2002)

  26. CDF Run 1: Z bb selection about 120 000 Z  bb events produced in Run 1 expected to be observed ~ 50-100 Trigger: central muon (pT> 7.5 GeV)  5.5 M evts Offline: request 2 tagged (0.7 cone) jets  5479 evts QCD background rejection based on event topology: > Z is produced by a time-like q-qbar anihilation, > QCD produced color flow between initial and final partons > Z is expected to have soft radiation between the jets > background will also have strong radiation between IS and FS partons. http://www-cdf.fnal.gov/physics/ewk/zbb_new.html

  27. CDF Run 1:3ET and 12 Use 2 kinematic variables to discriminate: 3ET : sum of ET of the clusters outside the 2 leading jets 12 : azimuthal angle difference between the 2 jets cuts derived: 3ET < 10 GeV, 12>3 rad

  28. CDF Run 1: Z bb Signal after cuts: S/N=1/6 at the Z mass peak select/antiselect w.r.t. the 2 variables to determine the tagging probability 3.2  exces

  29. CDF Run 1:Likelihood fit Results: MZ=90.0 2.4 GeV Z = 9.4  3.5 GeV NZ=91  30(stat) 19(sys.) Pythia: expect 12414

  30. First Run 2 QCD Physics Dijet mass spectrum at 1.96 TeV Inclusive jet pT spectrum at 1.96 TeV Ldt = 1.9 ± 0.2 pb-1 Ldt = 1.9 ± 0.2 pb-1 Highest 3-jet event ETjet1 : 310 GeV Etjet2 : 240 GeV ETjet3 : 110 GeVEtmiss : 8 GeV Only statistical errors Only statistical errors • not fully corrected distributions: • preliminary correction for jet energy scale(but no unsmearing or resolution effects) • 30-50% systematic error in cross-section • no trigger selection efficiency corrections

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