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Vertex and Track Reconstruction in CMS

Vertex and Track Reconstruction in CMS. W. Adam Institute of High Energy Physics, Austrian Academy of Sciences, Vienna CMS Collaboration. Perugia, Italy. Overview. The challenges The detector Track reconstruction Baseline: track finding and fitting Advanced algorithms

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Vertex and Track Reconstruction in CMS

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  1. Vertex and TrackReconstruction in CMS W. Adam Institute of High Energy Physics,Austrian Academy of Sciences,Vienna CMS Collaboration Perugia, Italy

  2. Overview • The challenges • The detector • Track reconstruction • Baseline: track finding and fitting • Advanced algorithms • Special applications • Vertex reconstruction • Vertex fitting • Primary & secondary vertex finding • Conclusions & Outlook WA, Vertex and Track Reco in CMS - Vertex06

  3. The challenges • pp-collisions at design luminosity (1034cm-2s-2, 14TeV) • 40 MHz crossing rate • O(20) superimposed pileup events / crossing • O(2000) charged tracks / crossing • Charged track density • 2.5 / cm2 / 25ns at r = 4cm • 0.15 / cm2 / 25ns at r = 10cm • 0.01 / cm2 / 25ns at r = 110cm • Trigger • Level 1 • Design rate 100kHz, no tracker • Levels 2-3: HLT • Reduction to 100HzIncludes (partial) track reconstruction WA, Vertex and Track Reco in CMS - Vertex06

  4. The challenges • Physics requirements • Highly efficient track reconstruction & low ghost rate • Excellent momentum resolution • Mass reconstruction • Energy flow • Charge separation • Excellent impact parameter resolution • Primary vertex reconstruction &separation of pileup • Secondary vertex reconstruction • Heavy flavour tagging • Combined reconstruction • Link to ECAL and muon system WA, Vertex and Track Reco in CMS - Vertex06

  5. Pixel R=1.2m L=5.4 m The detector • Full-Silicon solution for the inner tracking • Excellent hit resolution, high granularity: • Good two-track separation • Low occupancy • > 220 m2 of silicon sensors • Classical layout • cylindrical barrel • planar end cap disks WA, Vertex and Track Reco in CMS - Vertex06

  6. The detector • Pixel detector close to interaction region • Endcaps • 2 x 2 layers • 672 modules • |z| = 34.5 / 46.5 cm • Pixel size 100m x 150m • 18 M pixels • Tilted for Lorentz angle • Barrel • 3 layers • 768 modules • R = 4.4 / 7.3 / 10.2 cm • Pixel size 100m x 150m • 48 M pixels • Lorentz angle 23 WA, Vertex and Track Reco in CMS - Vertex06

  7. R=1.2m L=5.4 m The detector • Strip detectors enclosing the pixel part • 4 TIB layers • 6 TOB layers • 2 x 9 endcap disks • 2 x 3 inner disks End cap –TEC- Outer Barrel –TOB- Inner Disks –TID- Inner Barrel –TIB- WA, Vertex and Track Reco in CMS - Vertex06

  8. Outer Barrel • 6 layers (2 stereo) • Rectangular sensors(d=500m) • Pitch 122m & 183m R (mm)  • Inner & Endcap Disks • 2 x (3+9) disks • Up to 7 rings (3 stereo) • Trapezoidal sensors(d = 320m & 500m) • Mean pitch ~95m to ~185m • Inner Barrel • 4 layers (2 stereo) • Rectangular sensors(d=320m) • Pitch 80m & 120m Z (mm) The detector WA, Vertex and Track Reco in CMS - Vertex06

  9. Track reconstruction Selection of first hits & initialparameters Seeds Pattern Recognition Selection of full set of hits &ambiguity resolution TrackCandidates Parameter estimation,track quality,cleaning Track Fit Tracks WA, Vertex and Track Reco in CMS - Vertex06

  10. Baseline track reconstruction • Seeding from pixel hit pairs • Why pixels? Lowest occupancy & 2dim hits! • Start with one reference hit, add inner layer • Compatible with vertex region, first hit and pT limit • Full algorithmic efficiency • Fast ~ 30ms @ 2.8GHz forglobal reconstruction • For commissioning and extended acceptance • “mixed” and “pixelless” seeding • Applies same algorithm to (inner) strip layers WA, Vertex and Track Reco in CMS - Vertex06

  11. Baseline track reconstruction • Track Finding: combinatorial Kalman Filter approach • Starts with initial estimate provided by seed • Fast navigation and selection of compatible sensors & hits • KF for iterative growing of candidates and quality measure • Adds compatible hits (+ “null” hypothesis == hit inefficiency) R uncertaintyat TIB1 TIB TOB Px b-jets, L=2x1033pT=120-170GeV WA, Vertex and Track Reco in CMS - Vertex06

  12. Baseline track reconstruction • Control of combinatorial growth while iterating • Limit #candidates (ranking) • Quality filter (rejection of poor candidates) • Resolve ambiguities during and after candidate building • Sufficiently fast & flexible even for dense environments! • An alternative P.R. algorithm using a road search is also available Fraction withspurious hits #candidates formed on TIB1 (“worst case”) Before finalcleaning! with spurious hits (candidates with hits in all layers) Barrel Strips B-Pix WA, Vertex and Track Reco in CMS - Vertex06

  13. Baseline track reconstruction • Efficiencies • Algorithmic: close to 100% except for low-E ’s (elastic interactions) • Global: for pions dominated by hadronic interactions • ~ no degradation in b-jets; fake rates <0.3% (1%) in barrel (forward) Single particles Global efficiencies ( 8 hits) pions muons ~0.35X0 / ~0.10 ~1.4X0 / ~0.60 () can be improved byrequiring less hits WA, Vertex and Track Reco in CMS - Vertex06

  14. Baseline track reconstruction • Track fitting • LS-fit implemented as a Kalman filter • Inside-out “forward” fit • Removes approximation of building stage • optimal estimate at exit from tracker • Outside-in “smoother” optimal estimate at vertex • In combination with forward fit: optimal estimates at each layer • Goodness-of-fit • Global track 2 • Compatibility of each hit • Execution time is small comparedto pattern recognition pT≥10GeV pT=1GeV WA, Vertex and Track Reco in CMS - Vertex06

  15. Baseline track reconstruction Reduced 2 pT (rel.) Resolution & track quality , pT=1GeV Long.. IP Transv. IP , pT=10GeV , pT=100GeV WA, Vertex and Track Reco in CMS - Vertex06

  16. pout/pin Advanced tracking algorithms • Gaussian Sum Filter • “minimal” extension of KF for non-Gaussian components: modeled by sum of Gaussians • Resembles several KFs in || - measurements change parametersand relative weights • Implemented in CMS SW: radiative energy loss of electrons Single track example layers radiation Gaussian sum true value q/p WA, Vertex and Track Reco in CMS - Vertex06

  17. Advanced tracking algorithms • GSF provides an estimated pdf more than just mean & sigma! • CPU-intensive  use on pre-selected tracks • Other advanced algorithms implemented in CMS • Deterministic annealing filter • Adaptive tracking with high density of background hits • Multi-track fitter • Simultaneous fit of narrow bundles of tracks with ambiguity resolution KF electron fit vs. GSF ElectronspT=10GeV GSF modevs. mean In- / outside estimates provide measure of radiated energy WA, Vertex and Track Reco in CMS - Vertex06

  18. Tracking for Pb-Pb-collisions • Standard algorithms can cope with 3000 Nch/y ! • Small modification to reduce CPU-time and tighter quality cuts for lower fake rate: • Start with pixel triplets instead of pairs • Don’t pre-combine hits in stereo layers • Recognize merged clusters High occupancyin first strip layers! Barrel Strips B-Pix efficiency Low fake-rate tuning fake rate • Alternative working point: •  ~ 90% for fakerates up to ~ 20% central Pb-Pb collisions, Nch/y = 3000-3500 WA, Vertex and Track Reco in CMS - Vertex06

  19. HLT tracking pT vs #hits • Adapting existing algorithms • Regional tracking • Reconstruct only in an externally defined region (e.g. from lower-level trigger object) • Conditional tracking • Stop when required precision is achieved(e.g. to confirm p<pmin) - typically 5 layersare sufficient • Use more constraints • E.g. (first estimate) of primary vertex • Use alternative reconstruction • Extend pixel seed pairs to triplets • Fast estimation of track parameterfrom triplets • Can be used for fast primary vertex reconstruction Barrel 2.5<pT<5 2 pixel hits 3 pixel hits 2&3 pixel hits pT resolution (GeV) Full reconstruction d0 vs #hits d0 resolution (m) WA, Vertex and Track Reco in CMS - Vertex06

  20. Vertex reconstruction Secondary & tertiary vertices Primary vertex Vertex finding &track association Vertex fitting Robustified KF Adaptive Filters Kalman filter … WA, Vertex and Track Reco in CMS - Vertex06

  21. Vertex fitting • Algorithmic base: Kalman filter • Tracks are iteratively added to a vertex • Last track  best vertex estimate • Possibility to smooth & update track parameters • Complication • Track  vertex association • Non-Gaussian track residuals  P(2) peaked at 0 • Conventional robustification: “trimmed Kalman vertex fitter” • Define 2-cut / track • Remove “worst” outlier and reiterate hard assignment • Simple concept, but low break-down point • Fails for highly contaminated vertex candidates CMS studies:min. compatibility = 5% WA, Vertex and Track Reco in CMS - Vertex06

  22. Vertex fitting • Adaptive fitting • Iterative fit with reweighting • Introduces fractional weight / trackand weight function • Starts at high T to avoid local minima • Decreases T after each iteration (“annealing”) • Results in soft assignment (unless T0) • #tracks  effective #tracks = wi • 2 pseudo- 2 • High break-down point CMS studies: 2cut=9,geometric annealing WA, Vertex and Track Reco in CMS - Vertex06

  23. Vertex fitting Residuals (z) Pulls (z) KF Adaptive Trimmed ttH, mH=120GeVL=2x1033cm-2s-1 WA, Vertex and Track Reco in CMS - Vertex06

  24. Vertex fitting • Adaptive vertex fit • Slightly better resolution • Slower for low Ntrack • Faster for highcomplexity Rejected tracks ttH, mH=120GeVL=2x1033cm-2s-1 Time / fit KF Adaptive Trimmed WA, Vertex and Track Reco in CMS - Vertex06

  25. Vertex fitting • Gaussian Sum Filter: • accepts tracks with multi-Gaussian states • electrons from GSF track fit or parameterization of tailsobserved in reconstruction • Here: simple model Many KF verticeswith P(2)<0.01 KF 4 tracks 90% (d0)=100m 10% (d0)=1000m GSF residuals almostwithout tails GSF residuals P(2) WA, Vertex and Track Reco in CMS - Vertex06

  26. Vertex finding • Primary vertex finding • Using fully reconstructed tracks • Preselection (i.p. significance & pT) & clusterization in z • Robustified vertex fit & cleaning (2-cut and compatibility with beam line) • Sorting by pT2 • Alternative: use pixel triplets (HLT) Efficiency forfinding signal PV  % HZZ4e b-jets ttH (z) (x) (z) Resolution (m) Resolution (m) Pixeltriplets ttH H b-jets DY, 2 H WA, Vertex and Track Reco in CMS - Vertex06

  27. Vertex finding Vertex finding efficiency purity  (%) • Secondary vertex finding • “Trimmed Kalman Vertex Finder” • First fit with complete set • Continue with rejected tracks • “Tertiary Vertex Finder” • Start with TKVF • Choose additional tracks close to flight path • Only used for kinematics (not for position) Trackvertex assoc. efficiency purity  (%) WA, Vertex and Track Reco in CMS - Vertex06

  28. Vertex finding • Secondary vertex quality Flight distance c g u,d,s 3D anlge Combined secondaryvertex tag on 50-80GeV QCD sample c-vertices b-vertices WA, Vertex and Track Reco in CMS - Vertex06

  29. First Step Beyond MC Real tracks in TID TEC Tracker integration tests and CMS cosmicchallenge WA, Vertex and Track Reco in CMS - Vertex06

  30. Conclusions & Outlook • A powerful set of track and vertex reconstruction algorithms • Performance and application have been demonstrated inCMS Physics TDR Vol I & II • New data model and software framework • Basic algorithms have been ported • Need to finish with advanced algorithms and validate! • Commissioning and startup • Becomes highest priority! • Concentrate on geometry for commissioning • Work on calibration issues • Alignment, material, detector condition, … We are eagerly waiting for the first tracks & verticesfrom the underground of LHC Point 5 !!! WA, Vertex and Track Reco in CMS - Vertex06

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