Several algorithms for tracking and for primary and secondary vertex
reconstruction have been developed by the ATLAS collaboration following
different approaches. This has allowed a thorough cross-check of the
performances of the algorithms and of the reconstruction software. The results
of the most recent studies on this topic are discussed and compared.Comment: Proceedings of the HCP 2006 conferenc

Ferrari, Pamela

English

arXiv

In the past years ATLAS has developed several algorithms for tracking and for primary and secondary vertex reconstruction by following different approaches. This has allowed a thorough cross-check of the performances and of the reconstruction software. The results of the most recent studies on this topic are discussed and compared

Ferrari, P

CERN Document Server

Hadron Collider Physics symposium 2006Pamela Ferrari 0Hadron Collider Physics symposium 2006Duke 26 May 2006Tracking and vertexing at ATLASP.Ferrari (CERN)Hadron Collider Physics symposium 2006Pamela Ferrari 1Magnet system ( 2Tesla)The ATLAS DetectorMuon SpectrometerCalorimeterInner DetectorHadron Collider Physics symposium 2006Pamela Ferrari 2Forward SCTTransition Radiation straw tube TrackerBarrel SCTPixel Detectors(3 layers + 6 disks)The ATLAS Inner DetectorHadron Collider Physics symposium 2006Pamela Ferrari 3pixel:Barrel: 3 layers of Silicon  detectors (average R=5.05,8.85,12.25 cm).endcap:3 disks of Silicon pixel detectors. For some simulation the intermediate layer is not present for historical reasons ( 2-layer layout)SCT:8 layers of semiconductor tracker SCT in the barrel and 9 disks per side in the EndCaps ( stereo strip detectors)TRT:Several layers of 4 mm straws in the barrel region ( arranged in 3 layers of modules). 14 Transition Radiation Tracker wheels in the endcap~30 hits per track The Inner Detector580174SCT-60σ (Rz) (μm)170 36TRT123pixelσ (Rφ) (μm)pointsHadron Collider Physics symposium 2006Pamela Ferrari 4ηMagnetic field and material knowledgeThe magnetic field ~2T in ID B= 1T at the end of solenoid B= 0.8 T at end of ID¾ more complicated tracking algs¾ track resolution degradation on μ mainly at high pT on 1/pT for |η| >1.5 on d0 small effect since B field  is uniform around Int. PointKnowledge of material in the detector¾ To reconstruct tracks one needs totake into account:• multiple scattering • energy loss in materialHadron Collider Physics symposium 2006Pamela Ferrari 5Number of tracks per eventAt high luminosity per each bunch crossing ( 25 ns)¾ more than 200 tracks¾ about 15-20 vertex candidatesComplex task for tracking andVertexing because of pile-up.Triggering algorithms have to be fast and robust to avoid to miss rare events1414√s (TeV)251034100LHC (high ℒ) 52x103310LHC ( low ℒ) Minimum bias/bcoℒ (cm2/s)ℒint/y (fb-1)Hadron Collider Physics symposium 2006Pamela Ferrari 6ATLAS trigger¾ LVL1 trigger:-hardware trigger (2.5μs latency)-calorimeter + muon chambers.-Defines Regions Of Interest (ROI)¾ LVL2:processing in parallel info from ROI, uses ID information (latency 10ms)¾ Event Filter:uses tools similar to “offline” code (thanks to longer latency ~1s)¾ Challenge:have traking and b-tagging at trigger level > speed!HLT:software triggersHadron Collider Physics symposium 2006Pamela Ferrari 7¾ Tracks seeds formed by fitting with a straight line pairs of space points in pixel B layer and in second logical layer (in a given RoI).- Tracks extrapolated back to beam line Ö impact parameter (IP)- Track retained if IP is small in transverse plane.¾ The Z coordinate of the primary vertex = maximum of the histogram filled with the z intersection of the seeds with the beam line.¾ Third space point is extracted in modules situated in positions where the hits may lay ( LookUpTables). Space points compatible with linear extrapolation of track extend the seed.30 μm at high pTTracking algorithm at LVL2¾ remove ambiguities due to overlapping space   points in triplets  with extrapolation quality¾ Triplets fitted and identified  with tracks- Efficiency for tracks in jets 80-90% depending on luminosity and event topology - 95% for single electrons.Hadron Collider Physics symposium 2006Pamela Ferrari 8B-tagging at LVL2¾ b-jet selection is performed by using impact parameters significance (S=d0/σ(d0), where σ(d0) dependence from pT is obtained from simulation) ¾ Secondary vertex algorithm similar to offline but faster¾ b-jet estimator uses likelihood ratio¾ WH (mH=120 GeV/c2), low luminosity¾ Timing:  3 ms per RoI (track  rec.) + < 2 ms Sec. Vtx rec.¾ R ~ 25 (15) for εb=50%(60%)Impact parameters d0 & z0Impact parameters + Sec. Vertex∑==trN1i iijet )u(S)b(SlnWHadron Collider Physics symposium 2006Pamela Ferrari 9Track extrapolation ingredients1.The first step is the geometrical transport of the track parameters and their covariance matrices to a given  detector  surface2. The second procedure is the update of the propagated parameters and errors, taking multiple Coulomb scattering and energy loss effects during the propagation process into account.Hadron Collider Physics symposium 2006Pamela Ferrari 10ATLAS offline tracking algorithms¾ The xKalman algorithm: finding space points defining primary trajectories in SCT & pixel. Kalman filter associates clusters to tracks.In TRT reconstruct track in a narrow region around the extrapolated trajectory retaining all hits in that region. Kalman fitter= track fitting with Gaussian noise and all measurements and material effects are approximately Gaussian¾ The iPatRec algorithm: form track-candidates using space-point combinatorials subject to criteria on maximum curvature and crude vertex region projectivity. Global χ2 fitter used to fit tracks and associate clusters.Only good tracks are retained for extrapolation in TRT, where TRT hits are added. Using global χ2 fitter = minimises track χ2 by considering all measurements simultaneouslyHadron Collider Physics symposium 2006Pamela Ferrari 11New Tracking¾ NewTracking algorithm: Logical reorganization of tracking code. Largely based on xKalman, but in the future it will combine as well some tools from iPatRec Ö optimised tracking algorithm.¾ Can be used at event filter level, offline and for the Combined testbeam and Cosmics runs. ¾ Uses better geometry description builtfrom full geomodel Ö easy development of new codeHadron Collider Physics symposium 2006Pamela Ferrari 12Comparison of results Using tt events95%/2.7%99%/1.3%98%/0.3%Forward eff/ fake rate96%/3.6%98%/0.5%98%/0.6%Transition eff/ fake rate96%/2.5%99%/0.799%/0.6%Barrel Track eff/ fake rate16.8817.0616.69Multiplicity (P>1 GeV)newTrackingiPatRecxkalmaniPatrecσ(z0)σ(d0) WH(400 GeV/c2) W(Æμν)H(Æuu)iPatrecHadron Collider Physics symposium 2006Pamela Ferrari 13Averaged over all ηη~0Momentum Resolution vs PTPT(GeV)Resolutions obtained here using iPatRec (xKalman gives same results)Single μ± ( DC1 / 2 pixel layer layout pT = 1, 5, 20,100, 1000 GeV/c)Hadron Collider Physics symposium 2006Pamela Ferrari 14Cosmics with SCT & TRT¾ the SCT & TRT barrel are integrated on the surface¾ We are having cosmics data taking since the 9th of May¾ We expect to collect 300K of cosmics until mid of June¾ remember that we have still a non-aligned detector.The alignment precision is given by the module placement precision on the barrelHadron Collider Physics symposium 2006Pamela Ferrari 15View from outside towards Side ACosmics data takingSCT:• Read 504 modules grouped in a sector at the top and another one at the bottom• The bottom sector is not fully cabled up will be ready the the22nd of MayTRT:• Read 2 sectors in top +2 sectors in bottomHadron Collider Physics symposium 2006Pamela Ferrari 16ATLAS Atlantis Event: JiveXML_2085_00097  Run: 2085  Event: 970 100X (cm)01000Y (cm)Cosmics with SCT & TRT¾ First Cosmics tracks in top sectorWe aim to use the cosmics to do a first exercise on the alignmentATLAS Atlantis Event: JiveXML_2085_00024  Run: 2085  Event: 240 100X (cm)01000Y (cm)Present alignment == module positioning precisionHadron Collider Physics symposium 2006Pamela Ferrari 17e/π separation using the TRTTypical TR photon energy depositions in TRT ~ 8-10 keVpions deposit ~ 2 keVElectron identification using large energy depositions due to transition radiation (X-rays) when they traverse radiators between TRT strawsResults from TB 2002 @20 GeV Results from CTB2004Hadron Collider Physics symposium 2006Pamela Ferrari 18Primary vertex reconstruction¾ Large multiplicity of tracks ( several hundreds as we have seen) > vertex reconstruction must be fast¾ Input needed consists of  3D trajectory & error matrix of tracks. Quality requirements on track are applied¾ Approximate primary vertex position in Z: sliding window of 0.7 cm is moved along all interaction region. The window with largest number of tracks weighted with pT is chosen. The <z> is the mean obtained by all the tracks in that window¾ Tracks belonging to primary vertex are taken away and the procedure is iterated to get other (pile-up) vertices.Hadron Collider Physics symposium 2006Pamela Ferrari 19Primary vertex reconstruction cont’d¾ All tracks at ± 5mm in z and ± 1mm in  transverse plane are accepted as coming from primary vertex¾ At this point the vertex fitting is performed using a Billoirmethod: if the χ2 is too high, the tracks that give too high χ2are rejected and everything is recalculated ( outliers are removed)¾ There are two different implementation of this method which are basically using the same strategy: • VxPrimary• VKalVrtHadron Collider Physics symposium 2006Pamela Ferrari 20Primary vertex with IPatRecWHμνbb events mH=120 GeV/c242.7 ± 0.410.8 ± 0.1VKalVrt50.0 ± 0.512.6 ± 0.1VxPrimaryz[μm]x[μm]Hadron Collider Physics symposium 2006Pamela Ferrari 21New Adaptive Vertex FitterThe “Adaptive Vertex Fitter” solves the problem of outlier tracks that spoil the fit, not by discarding them, but by down-weighting them.Minimises instead than residuals, weighted sum of squared residuals (weight depending on χ2)(10000 events of WH(120) with H->bb)45.43±0.0511.07±0.09VKalVrt46.76±0.0511.07±0.09AVFz ( μm)x ( μm)hxold_narEntries  10200Mean   -6.672e-05RMS    0.01132Underflow       0Overflow        0Integral  1.02e+04 / ndf 2χ  113.4 / 76Constant  5.3± 414.6 Mean      1.104e-04± -6.674e-05 Sigma     0.00009± 0.01107 mm-0.06 -0.04 -0.02 -0 0.02 0.04#0100200300400500x resolution of VKalVrtHadron Collider Physics symposium 2006Pamela Ferrari 22B-Tagging methodsBa0<0a0>0xySecondary VertexPrimary vertexJet axisSoft lepton 1. Based on lifetime of b-hadrons jets, high multiplicity of b-jets• Impact parameter of tracks• Secondary vertex2. Soft-lepton tagging: development ongoing• Low pT electron from B (D)• Low pT muon from B (D)Key ingredients:• tracking (IP resolution, PrimaryVertex)• jets (axis)Hadron Collider Physics symposium 2006Pamela Ferrari 23¾ Use normalised S= d0/σd0for each track¾ compare it to predefined calibration p.d.f. for the b and light q hypothesis: get probabilities b(S) and u(S)IP in transverse plane¾ sum over all tracks Î jet btag weight∑==trN1i iijet )u(S)b(SlnWHadron Collider Physics symposium 2006Pamela Ferrari 243D Impact Parameter¾Improvement can be obtained by combining the longitudinal   and the transverse significance.  W=Pb(Sd0,Sz0)/Pu(Sd0,Sz0)b jet u jetHadron Collider Physics symposium 2006Pamela Ferrari 25Secondary vertex search1. Track selection with quality cut:(Typically pT > 1 GeV/c, |η| < 2.5;  |d0| < 1 mm, |z0| < 1.5 mm;  NPixB > 0, NPix > 1, NSi > 6)2.  Search for good 2-track vertices in jet3. At this point one can remove V0s, identified interaction withmaterial,…4.  Common (inclusive) vertex for remaining tracksKs Λbeam-pipe,pixel layersHadron Collider Physics symposium 2006Pamela Ferrari 26Final jet tagging weightInput variables have to be independent from flight distanceE(SVX)/E(jet)SVX mass# 2-track vertices+ IP3D1 2D variable1D variableb jet u jetb jet u jetb jet u jetHadron Collider Physics symposium 2006Pamela Ferrari 27Taggers availablelhSig=Lifetime1D+Lifetime2D+SecVtxBUVKalVrt=“weight”: IP3D + SV1Pre-definedcombinationSecVtxTDSecVtxBULifetime3DLifetime2DLifetime1D2nd streamSV2SV1Inclusive Secondary VertexIP3DIP2D(trans. impact)IP (long. impact)1st streamDifferent taggers are used as cross-check since they are almost identical wrt discriminating variables:Lifetime2D ~ IP2D Lifetime3D ~ IP3DSlight differences:• refined track selection in IPxD, • one 2D vs one 1D pdf for IP3D    vs Lifetime3DHadron Collider Physics symposium 2006Pamela Ferrari 28PerformancesLabelling of jets: label a jet as a b-jet if there is a b-quark within ΔR<0.3.efficiency εb : (# b jets )/(#jets labelled as b with pT>15 GeV/c, |η|<2.5 )light-jet rejection: Ru= 1 / εuOverlapping jets and purification: Overlaps in jets  Æ mislabellingJet isolation very dependent on the type of events and physics processes (gluon jets) + jet algorithmPurification to factorize it from pure b-tagging issuesªdo not consider lights jets whenthere is  a b/c/quark/hadron within ΔR<0.8Using WH events mH = 120 GeV/c22 layer-layout, xKalman tracks, Cone 0.4 jetsIP2DIP3DSV1+IP3DSV2+IP3DHadron Collider Physics symposium 2006Pamela Ferrari 29Shared hits and bad tracksb-tagging is obviously very demanding for track quality.One might try to ‘clean them up’1) Tracks in jet may share some hits, resulting in lower quality tracks: special treatment, by either rejecting them, or using dedicated calibrations. Fraction in b-jets:• tt events                  3.5%• WH events (400 GeV)    8.5%2) Tracks may originate from V0 or interaction with material.They usually have “more lifetime” →reject them (bad tracks)Fraction of V0 tracks in b-jets: • tt 1.2%• WH(400 GeV) 3.6%in light jetsHadron Collider Physics symposium 2006Pamela Ferrari 30B-tagging performance157 ±11609 ±8675 ±4214 ±1855 ±2135±9Rej:just taggerVKalVrt147 ±10612 ±8773 ±4 205 ±1752 ±2130 ±9 Rej:just taggerAVFRej:bad tracks +AVFRej:bad tracks +VkalVrt164 ±12794 ±12998 ±6 327 ±3466 ±3199 ±16 192 ±15815 ±134101 ±6339 ±3569 ±3206±1760%50%60%50%60%50%efficiencyIP3D+SV1IP3DIP2DB-tagging performance using different primary vertex finders.• WH>uuμν, mH = 120 GeV/c xKalman• Geometry for this study: Final Layout for pixels (3 layers/disks)  Physics performance limited by gluon splittingHadron Collider Physics symposium 2006Pamela Ferrari 31b-tagging in ttbar events 190 K tt ttbar, cone ΔR=0.4, iPatrec tracks 60k ttH ( mH=120 GeV) cone ΔR=0.4, iPatrec tracks (2-layer layout)1133 ±56.6326 ±9.8ttbarSV1+IP3D + shared hits 1392 ±56.6313 ±9.4ttH events lhSig1326 ±66.3375 ±11.2ttbar eventsSV1+IP3D + shared hits +bad tracks858±42.9259 ±7.8ttbar SV1+IP3D Ru (εb=50%)Ru (εb=60%)Hadron Collider Physics symposium 2006Pamela Ferrari 32Conclusions¾There has been a lot of work/improvement on tracking and vertexing in the past year(s).¾Tracking algorithms are available at LVL2, Event filter, offline, for cosmic running, combined testbeam etc..¾Different parallel software developments for the tracking and vertexing algorithms have been produced, giving comparable results¾We are already reconstructing cosmic events with the SCT and TRT barrels¾We are looking forward to the commissioning of all those tools with the final detector.Hadron Collider Physics symposium 2006Pamela Ferrari 33Back-upHadron Collider Physics symposium 2006Pamela Ferrari 34Tracking the basics:1. Pattern recognition: finding hits in SCT and pixel and then make a fast fit to extrapolate tracks to TRT to find TRT hits. At anystage the effect of the magnetic field is taken into account2.Track fitting: uses the list of hits that the pattern recognition associates to a track, and fits the track. It needs as input thetrack parameters at the perigee ( point of closest approach to the z axis for the track). There are 5 parameters: f0,q0,d0, z0 and q/p. The track fit can correct for energy loss and multiple scattering for each scattering plane.3.Residuals: difference between the track prediction and the hit 4.Track parameters pulls: a measure of the reliability of the track fit are the pull distributions (rec-tru)/(error on rec) for the 5 track parameters.Hadron Collider Physics symposium 2006Pamela Ferrari 35Comparison of results Using 100 tt events3.2/8.4/15.63.2/8.5/15.23.3/8.8/15.2Forward# hits Pixel/SCT/TRT2.9/7.8/26.43.0/8.0/25.73.0/8.2/25.8Transition# hits Pixel/SCT/TRT2.9/7.9/28.92.9/8.0/27.52.9/8.1/28.1Barrel# hits Pixel/SCT/TRT95%/2.7%99%/1.3%98%/0.3%ForwardTrack eff/ fake rate96%/3.6%98%/0.5%98%/0.6%Transition Track eff/ fake rate96%/2.5%99%/0.7%99%/0.6%Barrel Track eff/ fake rate16.8817.0616.69Multiplicity (P>1 GeV)newTrackingiPatRecxkalmanHadron Collider Physics symposium 2006Pamela Ferrari 36Impact parameter resolutionsWH(400 GeV) W(Æμν)H(Æuu)iPatreciPatrec xKalmanxKalmanσ(z0)σ(d0)WH(400 GeV/c2) W(Æμν)H(Æuu)Hadron Collider Physics symposium 2006Pamela Ferrari 37Primary vertex with xKalman47.4 ± 0.511.4 ± 0.1VKalVrt51.4 ± 0.613.0 ± 0.2VxPrimaryz[μm]x[μm]Hadron Collider Physics symposium 2006Pamela Ferrari 38Shared hits: IP distributions Typical criteria:• ‘’Good’’ track: no shared pixel AND  < 2 shared SCT hits• ‘’Shared’’ track: the restb-jets light jets1.4%3.5%ttH, ttbb, tt(jj)4.4%8.5%WH(400 GeV)light jetsb-jetsFraction of tracks with shared hitsRome samplesHadron Collider Physics symposium 2006Pamela Ferrari 39Performances: ttH vs ttjjComplicated/busy events due to overlaps and mislabellings.Purification done by factorising these effects to disentagle them from b-tagging b-jets: ttH Pythia (samples 4867, 4868)  u-jets: tt(jj) MC@NLO cone ΔR=0.4, iPatrec tracks Statistics:  75k b-jets, 1.2M u-jetsas a function of|η| and pT@ 60% efficiency59297 ± 5882 ± 24SV1+IP3D2366 ± 1218 ± 3IP2DR@ εb 70%R@ εb 60%R @ εb 50%Hadron Collider Physics symposium 2006Pamela Ferrari 40Soft Electron Tagginguse Soft Electron identification variables to build a probability for each track in a jet     ⇒ the track with the highest probability is the “electron candidate”⇒ light jet rejection vs algorithm efficiency :@ 60% algorithm efficiency (i.e. 0.6*BR(b→eX) ~7.8% b-jet efficiency)Ru = 134 (WH mH=120 GeV events)PionsElectronsHadron Collider Physics symposium 2006Pamela Ferrari 41Impact of (mis)alignmentRandom misalignments:IP3D tagger , ttH events,realistic conditions⇒ will redo the exercise with misaligned detectors from simulation (more realistic than random misalignments)Hadron Collider Physics symposium 2006Pamela Ferrari 429 Use CDF experience : Map CDF commissioning misalignments from CDF run II to ATLASand propagate to b-tagging performances⇒ ~ 10% lossInfluence of alignment (II)Hadron Collider Physics symposium 2006Pamela Ferrari 43Performances versus jet pT, ηIP2DIP3D+SV1εb= 60%IP2DIP3D+SV1εb= 60%Non-uniform performances: tagging b-jets can bias kinematicsHow to improve bad regions ?:• large pseudo-rapidity (|η|>2): z-anolog clusters, matter descrip, interaction in disks• low pT (<50 GeV) [bbh,…]: better matter description• high pT (>200 GeV) [Susy, little Higgs,…]: tracks w/o hit in b-layer, ambiguitiesÎ A bit more subtle, being investigated nowHadron Collider Physics symposium 2006Pamela Ferrari 44Track ClassificationDefine track quality categories to:• reject/use dedicated calib for tracks w/ shared hits (≥1 Pix || ≥2 Sct)• reject tracks from V0 or interactions with materialTracks w/ shared hits:8.5%WH(400 GeV)3.5%ttFraction of sharedtracks in b-jetsSamplesTracks from V0,  interactions:3.6%WH(400 GeV)1.2%ttFraction of tracksfrom V0 in b-jetsSamplesLV, JBdV (CPPM)

Tracking and Vertexing at ATLAS

Several algorithms for tracking and for primary and secondary vertex reconstruction have been developed by the ATLAS collaboration following different approaches. This has allowed a thorough cross-check of the performances of the algorithms and of the reconstruction software. The results of the most recent studies on this topic are discussed and compared.Several algorithms for tracking and for primary and secondary vertex reconstruction have been developed by the ATLAS collaboration following different approaches. This has allowed a thorough cross-check of the performances of the algorithms and of the reconstruction software. The results of the most recent studies on this topic are discussed and compared

Tracking and vertexing at ATLAS

Abstract

Similar works

Full text

Available Versions

CERN Document Server

CERN Document Server