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ZEUS Tracking Tutorial

ZEUS Tracking Tutorial. Rainer Mankel ZEUS Weekly Meeting 6-Nov-2006. Who needs tracking…?. “Who needs tracking…?”. Different kinds of analyses have very different ideas as to which information tracking should deliver some analyses only test whether there is a “good” primary vertex

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ZEUS Tracking Tutorial

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  1. ZEUS Tracking Tutorial Rainer Mankel ZEUS Weekly Meeting 6-Nov-2006

  2. Who needs tracking…? R. Mankel, Tracking Tutorial

  3. “Who needs tracking…?” • Different kinds of analyses have very different ideas as to which information tracking should deliver • some analyses only test whether there is a “good” primary vertex • some analyses only need to know (roughly) the primary vertex position • a substantial set of analyses explicitly reconstruct more complex final states using track parameters • HERA-II state-of-the-art analyses use lifetime signatures  precision tracking(m, not cm) Level of tracking requirements  R. Mankel, Tracking Tutorial

  4. Which detectors do our tracking…? R. Mankel, Tracking Tutorial

  5. Straw Tube Tracker (STT) Micro-Vertex Detector (MVD) The ZEUS Tracking System for HERA-II Central Tracking Detector (CTD) e p R. Mankel, Tracking Tutorial

  6. The Central Tracking Detector (CTD) • Cylindrical drift chamber • Nine superlayers (five axial + 4 stereo) with eight layers each • drift cells tilted by 45o with respect to radial direction • official coordinate resolution ~160 m R. Mankel, Tracking Tutorial

  7. The Straw Tube Tracker (STT) • 2 superlayers of straw chambers in the forward region (5o-25o) • 12 layers per superlayer • oriented in four stereo views • 7.5 mm straw diameter, Ar/CO2 • During 2005, STT had to stay off due to insufficient cooling • 2005 data have no STT • STT cooling has been upgraded, 2004 + 2006 data have STT R. Mankel, Tracking Tutorial

  8. The Micro-Vertex Detector (MVD) The forward section: • 4 wheels • each composed of 2 layers of 14 Si detectors • in total 112 hybrids, 50k channels The barrel section: • 30 ladders • each composed of 5 modules of 4 Si detectors • in total 300 hybrids, >150k channels The rear section: • Cooling pipes and manifolds • Distribution of FE, slow control and alignment cables R. Mankel, Tracking Tutorial

  9. The Layout of the MVD Barrel • Major part of azimuthal acceptance covered by three cylinders of ladders ( six measurements per track) • Optimal use of available space between beam pipe & CTD Mechanical view Tracking view R. Mankel, Tracking Tutorial

  10. How Does Track Reconstruction Work? R. Mankel, Tracking Tutorial

  11. The Track Reconstruction Chain Coordinate reconstruction Track pattern recognition Track fitting Vertex finding Vertex fitting Higher level analysis R. Mankel, Tracking Tutorial

  12. MVD Cluster Finding • Cluster algorithm is one of the crucial items determining tracking resolution • Present reconstruction uses centre-of-gravity algorithm • obtained 25-35 m resolution for vertical incidence • Alternative algorithms are under study • head-tail • three-strip-algorithm • eta algorithm R. Mankel, Tracking Tutorial

  13. Track Finding (=Pattern Recognition) • ZEUS uses a combined track pattern recognition of MVD and CTD • not merely an extension of CTD tracks into the MVD • improved efficiency • complex multi-pass procedure • Main challenge: “ganging” of barrel MVD strips • 50% of clusters are ghosts • Presently being extended into forward area • This combined MVD-CTD-STT pattern recognition is a major highlight of ZEUS reconstruction R. Mankel, Tracking Tutorial

  14. Track Finding (cont’d) • Example: seed creation in barrel and forward MVD R. Mankel, Tracking Tutorial

  15. The Track Fit direction of flight  • Using the Kalman filter method with smoother to account for multiple scattering and ionization energy loss on MVD part of trajectory • Also performs rejection of outlier hits purification of track • Working on extension of Kalman filter into forward region (MVD+CTD+STT) production vertex  direction of filter R. Mankel, Tracking Tutorial

  16. “Does the track fit influence the quality of my analysis?” Yes! • Enhancements in the track fit during the last year have improved the optimal momentum resolution from 1.2%  0.8% • Also the parameter error estimates are now correct within ~ 5 - 20% • important for significance plots • MVD considerably improves momentum resolution at large momentum • Direct impact on mass resolutions R. Mankel, Tracking Tutorial

  17. “Does the track fit influence the quality of my analysis?” (cont’d) • And this pays off directly! New kffit improves mass resolution of • K0S: by factor of 1.3 • J/: by factor of 1.8 • Huge gain on S/B R. Mankel, Tracking Tutorial

  18. And the Future? • Traditionally, tracks are classified according to their outermost CTD superlayer (SL1…SL9) • The typical analysis discards tracks below CTD SL3 • In future, the combined forward tracking (CTD+BMVD +FMVD+STT) will open up the range below ~20o • Considerable increase of acceptance R. Mankel, Tracking Tutorial

  19. Standard (2006a.1) new: Rigorous track fit Coming Soon: New Rigorous Track Fit SL 1 tracks! • For Combined Forward Tracking we need a powerful track fit to fully exploit all detector information • inhomogeneous magnetic field in forward region • huge amounts of material (CTD end-plate) • combination of STT, CTD and MVD hits • This is the task of the Rigorous Track Fit (RTF) • state-of-the-art Kalman filter, adaptive treatment of field map, STT+CTD+MVD at hit level, rigorous treatment of multiple scattering & energy loss, navigation scheme, C++ • To appear in new software release R. Mankel, Tracking Tutorial

  20. I’m really confused about all these different kinds of tracks… R. Mankel, Tracking Tutorial

  21. = end product of the track reconstruction chain  the tracks you should use wherever possible (Re-)fitted tracks = output of pattern recognition, with some level of (non-rigorous) fit applied  interim product of tracking chain “Regular” tracks = tracks reconstructed from CTD hits only  for testing purposes only CTDonly tracks Why Different Kinds of Tracks? • Mainly owed to commissioning history, but the picture is clearing up “ZTT” R. Mankel, Tracking Tutorial

  22. Pre-Vertex vs. Vertex Tracks • Initially, tracks are reconstructed independently • pre-vertex tracks: ZTTRHL, … • Then, the vertex reconstruction groups them into a primary vertex, secondary vertices • primary vertex tracks: ZTTRPRM, … • secondary vertex tracks: ZTTRSEC, … • non-vertex tracks (ZTTRHL,…) • Important: because of the magnetic field, a meaningful momentum vector can only be calculated for a track whose origin is known • it does not make sense trying to calculate invariant masses etc using non-vertex tracks R. Mankel, Tracking Tutorial

  23. Should I care about vertexing methods…? R. Mankel, Tracking Tutorial

  24. Primary Vertex Reconstruction • Until recently, standard method for primary vertex finding/fitting has been “kfvertex” • based on Kalman filter technique • good resolution, but limited efficiency. Also very slow. • Topology of heavy flavor events poses additional challenges to primary vertex finder • long-lived particles  outliers • Needed a robust method… Residual of primary vertex x position R. Mankel, Tracking Tutorial

  25. Truth Need Robust Method for Primary Vertexing Estimation Primary vertex before outlier rejection • Outliers (in vertex case: outlier tracks) destroy quality of primary vertex position • There is the acute danger of discarding “good” tracks & keeping “bad” tracks • local but not global optimum • need a sophisticated fit procedure After successful outlier rejection After unsuccessful outlier rejection R. Mankel, Tracking Tutorial

  26. The Deterministic Annealing Filter (DAF)* for Vertexing * R. Frühwirth, A. Strandlie Comp.Phys.Comm. 120 (1999) 197 • Replace hard 2cuts by a smooth temperature-dependent weight function, which is sharpened by iteratively lowering the temperature • more robust determination of primary vertex • after convergence, the resulting weight could be used for tagging • At ZEUS, we start from the “regular” vertex and “refine” it with the DAF Weight: R. Mankel, Tracking Tutorial

  27. Performance of Vertex DAF Pattern Recognition • The DAF itself obtains a similar resolution as kfvertex • But its efficiency is higher: similar to “regular” vertexing, also for low multiplicity • DAF combined with beam constraint (DAFbeam) gives the best primary vertex resolution “ refitted Kalman Filter R. Mankel, Tracking Tutorial

  28. “What should I do to get the best primary vertex efficiency & resolution?” • Be sure to run vertex DAF with beam constraint! • Tricky: as a matter of principle, the beam spot is calculable only after the bulk reconstruction in zephyr, and thus not available at reconstruction time • Therefore, DAFbeam should be run at Orange level • ORANGE-doDAFVtx ON • DAFVTX-BeamCstr ON • fortunately it is very fast (only several ms/evt) • Will only work if beam spot GAF for this period is available (be careful with MC) R. Mankel, Tracking Tutorial

  29. … and why so much fuss about the beam spot? R. Mankel, Tracking Tutorial

  30. The Beam Spot… • is also a powerful constraint for impact parameter & decay length analysis • is practically uncorrelated with the tracks in the actual event • gives an unbiased reference e.g. for decay lengths • while primary vertex may be biased by other long-lived particles • Downside: this helps only in the transverse plane • so in the end, the final reference for heavy flavor tagging is probably a “reduced DAF primary vertex” … • let’s take one step at a time R. Mankel, Tracking Tutorial

  31. The Beam Spot 50 m • By design, in HERA-II the beams have Gaussian widths of ~110 m horizontally and ~30 m vertically • Powerful constraint for impact parameter & decay length analysis • But movement of the beam spot must be measured very accurately • position can undergo sizable movements (~100 m) even within a fill • beam spot GAFs 50 m R. Mankel, Tracking Tutorial

  32. Recently We Have Directly Measured The Beam Spot Width • Done with impact parameter correlations of track pairs • H1 have recently copied this method, get similar results R. Mankel, Tracking Tutorial

  33. What should I know about alignment & all that …? R. Mankel, Tracking Tutorial

  34. Alignment Issues • Naturally, it takes experiments years to squeeze the ultimate precision out of a (silicon) tracker • reason: alignment needs to be known at the ~10 m scale • Pre-installation surveys measured positions of MVD sensors within ladders & wheels well, but knowledge for 3D arrangements is less precise • During 2002-04, cosmic runs were basis of a first track-level alignment • The best alignment accuracy to date has been reached using tracks from ep collisions • “eplocal” alignment R. Mankel, Tracking Tutorial

  35. How Alignment Improves the Impact Parameter Resolution • Experts are working hard to improve the alignment even further R. Mankel, Tracking Tutorial

  36. Subtracting the beam spot width, we can estimate our track-level resolution Three-Cylinder tracks • Resolution in data still somewhat wider than MC • remaining alignment uncertainty? • But clearly more than good enough for first round of MVD-based analyses… • In some regions (e.g. ~180o) we have to rely on two-cylinder tracks which have worse resolution (not yet based on “perfect” fits) R. Mankel, Tracking Tutorial

  37. DIS event from 12-Mar-2005 500 m D+ vertex Primary vertex How the aligned MVD allows detecting heavy flavor signatures + K + R. Mankel, Tracking Tutorial

  38. D+ K-+ + ZEUS 2005 reprocessed with ep alignment. ICHEP06 conference paper. R. Mankel, Tracking Tutorial

  39. D0 K-+ R. Mankel, Tracking Tutorial

  40. Do all our data have the same level of alignment? R. Mankel, Tracking Tutorial

  41. Do all our data have the same level of alignment? Not yet… ! • The 2005 data have been reprocessed this spring with eplocal alignment • presently our most precise data (132 pb-1 with MVD, no STT) • the best data for analyses using precision tracking! • We expect reprocessing of the 2004 data with eplocal alignment to start in December • now even with forward MVD alignment & combined forward tracking • Reprocessing of 2006-07 data will follow later R. Mankel, Tracking Tutorial

  42. How can I access track information for my analysis? R. Mankel, Tracking Tutorial

  43. How can I access track information for my analysis? • The primary output of track reconstruction are the Adamo tables, which can be accessed within an Orange job • ZTVTXPRM (primary vertex position) • ZTTRPRM (primary tracks) • ZTVTXSEC (secondary vertices’ positions) • ZTTRSEC (secondary tracks) • ZTTRHL (pre-vertex tracks) • These tables are connected by relations R. Mankel, Tracking Tutorial

  44. How can I access track information for my analysis? (cont’d) • Orange provides standard blocks with tracking & vertex information • note: these are not responsibility of tracking group • do not trust contents blindly • Main danger: depending on control cards, Orange will fill tracks in several variants • danger of double or triple counting be careful • Recommendation: use fitted tracks (ZTT) wherever possible. You may have to watch for detailed cards in settings of individual blocks. ORANGE-TRACKING ZTT [...] C Tracking code for Charm finding parameters: SEE orange_Dmesons.fpp ORANGE-CHARMTRK ZTT [...] R. Mankel, Tracking Tutorial

  45. Analysis-Level Vertexing • Seen with MVD precision, standard vertex reconstruction is not enough to detect all possible topologies automatically • For this reason, in ZEUS many precision-tracking vertex signatures are found by context-dependent revertexing techniques at analysis-level • based on tools in tLite library • very powerful • would hardly have been possible ~10 years ago (CPU time) • example: revertexing D+, D0, D*, V0Lite finders R. Mankel, Tracking Tutorial

  46. XY reconstructed XY MC true + jets Analysis-Level Vertexing (cont’d) • Various revertexing particle finders can used in Orange just by toggling a card • revertexing D+, D0, D*, V0Lite & inclusive secdry vertex finders • Revertexing charm finders use their own “charm tracking block” • Recently, analysis vertices are even displayed in ZeVis R. Mankel, Tracking Tutorial

  47. Can I develop my own vertex analysis ? R. Mankel, Tracking Tutorial

  48. Can I develop my own vertex analysis ? • Yes! the tLite library holds many useful tools: • fast vertex fitting at analysis level • DCA and impact parameter calculation • helix utilities • kinematic fits • Build your own sophisticated vertex cascade analysis • enjoy… • and submit it to Orange when done R. Mankel, Tracking Tutorial

  49. Just an Appetizer… ?? • Analysis of more complex final states comes into reach • vertex and mass constraints on whole decay chain will increasingly play a role • Example: • (2S)  J/ +- (+-) +- • simple mass calculation: only weak signal • full kinematical fit of decay chain: sharp signal simple calculation of invariant mass (2S) full kinematical fit of decay chain R. Mankel, Tracking Tutorial

  50. Need further information? • ZEUS Tracking Web: • http://www-zeus-data.desy.de/tracking/ R. Mankel, Tracking Tutorial

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