1 / 16

Pixel Cluster Splitting Using Templates

Pixel Cluster Splitting Using Templates. D. Fehling, G. Giurgiu, P. Maksimovic, S. Rappoccio, M.Swartz Dept of Physics+Astronomy, Johns Hopkins University. Two-Track Separation in Pixel System. Pixel clusters have a characteristic shape caused by Lorentz drift.

lalo
Download Presentation

Pixel Cluster Splitting Using Templates

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Pixel Cluster Splitting Using Templates D. Fehling, G. Giurgiu, P. Maksimovic, S. Rappoccio, M.Swartz Dept of Physics+Astronomy, Johns Hopkins University

  2. Two-Track Separation in Pixel System Pixel clusters have a characteristic shape caused by Lorentz drift • clustering algorithm needs to include corner adjacency • thresholds can create apparently unlikely cluster shapes • minimum two-track separation in f (local x) is ~3 pixels (300 mm) • minimum two-track separation in z (local y) varies from ~2 pixels (h=0) to ~12 pixels (h=2.5) or 0.3-18 mm • standard and template reconstruction will fail when clusters merge • template reco will return bad probabilities when this happens

  3. Template Reconstruction Slides 4-13 summarize the pixel template reconstruction technique. Lots more detail can be found in CMS Note-2007/033

  4. Sensor Modeling Over the last 4 years, we (VC + MS) have successfully modeled irradiated pixel sensors fabricated on DOFZ substrates at several F and T, • Pixelav transport simulation + E-field modeling w/ TCAD 9.0 • data well described by tunable double-junction model from F =(0.5-6)x1014 neq/cm2 • Use to calculate a priori cluster shapes for improved analysis technique

  5. Template-based Reconstruction Algorithm Fit projected cluster shapes to simulated shapes (templates): • Sum charges on all pixels: Qclus • Truncate individual pixel signals to cotb-dependent maximum • sum projections: Py/xi • Account for thresholds: • add information back by creating Pseudo-Pixels at the ends of the cluster • have 50% of threshold height and 100% uncertainties • pulls fit near cluster edges and improves resolution Apply fitting procedure to projections Pyi and Pxi: -> scale and translate shape to fit

  6. Comparison with Standard Algorithm After small corrections for residual effects • RMS residuals not Gaussian fit sigma (tails included) • Before irradiation, template algorithm improves the resolution at all h • for Q/Qavg<1 (~70% of all hits), 10-20% improvement • for 1<Q/Qavg<1.5 (~30% of all hits), 20-100% improvement high-h deltas

  7. After irradiation, Standard technique is more affected than templates • z-resolution in both charge bands, 100% improvement • f-resolution at large h, 30-200% improvement high-h deltas

  8. Implementation in CMS Tracking • Template reconstruction has moderate sensitivity to track angles • use Standard technique for first pass track finding/fitting • use Template technique in second pass track fit (angles from 1st pass) • Study with sample of simulated muon tracks • Template technique exceeds the Standard technique at all h and Qclus • x(f) resolution worsens at large h ? • caused by low Qclus “junk” from showering in our not-so-thin detector • ~ 7% of high-h tracks have low-Qclus hits on them

  9. Effect of 2nd pass on track parameters • Pulls are sensitive to resolution tails • template reconstruction kills tails! • Biggest improvements are in d0, f0 pulls in the regions > 3 s • expect to see significant S/N improvements inb/t-tagging 10 GeV m’s + standard alg d0 + template alg f0

  10. Goodness-of-fit No Probability Cut • A by-product of the template fitting procedure is a x2that reflects the consistency between the shapes of the cluster projection and the interpolated template • template object stores the expected x2 distribution in a simple parameterization that depends upon Qclus • convert these into x- and y-probabilities • Suppresses low-Q junk clusters that arise from secondary interactions with 1-2 % inefficiencies • Can remove low-Q with no inefficiency P>10-3

  11. Track Seeding • A. Dominguez has been developing an improved pixel track seeder that compares the lengths of y-clusters (global z) in the pixel barrels • can significantly reduce the number of trial seeds and therefore the track finding time (dominates reco time) • Intrinsic y-length resolution of the templates is about twice that of the simple cluster length method • seeds have local angles, can use templates in 2nd pass • template probabilities determine consistency with angle hypotheses and are normalized to resolution • can do both x- and y-projections • can do barrel/FPix seeds • Avoids “junk” hits on tracks (may be more junk in real LHC environment) y (global z) x (global f)

  12. First Seeding Results (preliminary) D. Fehling, P. Maksimovic (JHU) have created a template-based seed cleaner that works with pixel-doublet seeds. The first test was done with a sample of 750 simulated t-tbar events: 1085k seeds Kalman Filter 1.80 s/event Seed Generator 0.13 s/event 37.6k tracks 1.92 s/event 1085k seeds 476k seeds Seed Cleaner 0.06 s/event Kalman Filter 0.96 s/event 37.0k tracks 1.15 s/event • Reduces number of seeds and tracking time by factor of ~2 • Loses 1.6% of tracks • quality of lost tracks is unknown as yet • No attempt to optimize cuts or use low-Q cut yet • New seeding in CMSSW 2_0: improvement smaller but still significant

  13. b-Tagging (preliminary) D. Fehling has studied the effect of the 2nd-pass template reco and templated-based seed cleaning+2nd-pass reco on b-tagging: Standard Reco • Use 80-120 GeV PT QCD events • Track counting doesn’t need re-calibration • track probability also improves /wo calib • Improvement in S/N is in range 2-3! Template Reco Only Template Seeding+Reco udsg-efficiency b-efficiency

  14. Templates in Cluster Splitting • Template technique has only modest sensitivity to the track angles • 1-2 mm shifts in cluster position do not affect resolution • Template probabilities flag unlikely cluster shapes/sizes • should avoid using the probabilities at the seeding level • want to include “bad” hits on tracks (to associate merged clusters to tracks and get angle estimates) • Current version of Template Technique works in two 1-D projections • full 2-D templates are possible but don’t exist currently • very cpu intensive to generate • would be significantly slower (not usable for everyday seeding) • no resolution advantage • would improve discrimination of template probability • would improve cluster splitting capabilities • The following is a sketch of a high pt jet re-tracking algorithm based on current 1-D cluster technology

  15. Step 1: • first pass tracking with “loose” cuts on x2 • road search and/or • CTF with simple seeder • templates in second pass only • Step 2: • examine template probabilities of tracked pixel hits • if small, try fitting two hit hypotheses in both projections • take the angles to be the same for both hits • should improve template probabilities • produces 4 new hits w/ 2-fold ambiguity (2-x X 2-y coords) • Step 3: • re-track event w/ tighter cuts hit 1 hit 2

  16. How to Begin • Coding of a cluster splitter should be fairly straightfoward: • 1-3 weeks for initial development/coding (tuning/iterating could take longer) • initial testing with merged pixelav hits • test code needs to be developed also • 2-hit hypothesis probability needs calibration • add more info to the basic template infrastructure? • Need full re-tracking procedure • Testing splitting as part of a re-tracking procedure • need samples of problematic events • need diagnostics that identify the inefficiency and resolutions

More Related