BEACH 04J. Piedra1 Tracking: From Raw Data to Analysis Raw Data and Calibration Clusters Hits, Resolution Tracking Physics Conclusion Matthew Herndon, May 2006 University of Wisconsin CDF Silicon Detector Workshop

2 Raw Data Organized by HDI, Chip and Strips Interpreted by bank unpacker Bit errors common from optical readout Chip ID errors, Strip ID errors Dropped readout Errors in termination characters Improved version: NN(nearest neighbour) bank unpacker Chip ID errors and termination characters corrected by understanding readout order Nearest neighbour readout mode allows correction of strip IDs from context Bits 0-1, lowest significance bits correctable: higher bits correctible if lower bits look correct Large amount of data corrected Efficiency improved by a few %, number of bad clusters reduced Rick Snider, Matt Herndon, Lester Miller Bit 0 Bit 1 Bit ff, 7f Dup Chan 33 types Cannot abandon nearest neighbour readout

3 Calibration Operational requirements Calibration needed on short time scale(24 hours): Before Production calibration jobs Production calibration jobs include beamline determination using tracking Calibration Calibrate per strip pedestals, noise, and identify bad strips(hot or dead) Provide short time scale detector performance evaluation along with SVXMon Procedure Take SVXCAL run with DPS-on and DPS-off Integrate calibration run information with dead strip list and create final tables Other calibration runs: bias scans/gain scans performed by experts Calibration benefits Improves cluster resolution and identifies clusters with potentially bad resolution Used in tracking efficiency measurements and realistic detector simulation SiExpected and bad strips M. Herndon Jason Nielsen SICHIPPED, SICHIPON, SISTRIPDH PROD_PHYS_SVX Small residual pedestal: Dynamic Pedestal Subtraction(DPS)

4 Clustering Clustering algorithm Thresholds: 1 strip 4 , 2 strips 3  and 2 , 3 strips all 2  Loose clustering requirements for high efficiency Clusters Typically two strips: ~1/2 each Charge varies between types/sides of sensors Online Thresholds Needs to be efficient for Landau peak of ~25ADC and RMS of order 5 counts 7-10 ADC conservative >12 ADC aggressive Rick Snider, Matt HerndonDoug Glenzinski

5 The Integration Gate Detector needs to be timed in Integration window set relative to beam crossing time Done for each type of sensor Found that optimum window was different for different types of sensors No optimum choice: May have changed with integrated radiation M. Herndon Matt Herndon, David StuartR-phi, SAS, Z

6 Cluster Resolution Measuring intrinsic resolution Constrain track to hits on two layers around layer of interest Calculate intersection point using hit positions and transverse momentum of track Measure resolution using likelihood fit Found resolution had a considerable tail Dropped entries outside of 2.5  to measure core Track Fit Tails still a problem for the track fit Toy MC with % in tails reproduces misestimates of resolution seen in track fit Double Gaussian model for track fit would be too time consuming M. Herndon Aaron Dominguez

7 Alignment Alignment Methodology Start from survey results: extremely important as a starting point, crosscheck or constraint. Compare to survey at every step First step global alignment SVXII, ISL, L00 - compare COT and silicon beamlines Fix tracks to layer 5 and beamline and align inner layers Align r-phi, SAS and then Z Ailgn for bows and small rotations Constrain/realign using overlap regions ISL and L00 Separate process starting with OI tracks after SVXII alignment M. Herndon ISL: Juan Pablo Fernandez Ray Culbertson L00: Ray, Aart Heijboer

8 Tracking Overview Uses a general outside-in methodology Start where evidence of tracks is most clear Highest angular resolution portions of detector Outer layers of COT and silicon M. Herndon Algorithms Form track seeds in outer layers Helix from outer hits and beamline or vertex Look for consistent hits moving inward Refit and repeat after adding each hit This method used for COT, OI(COT to silicon outside-in) and silicon only algorithms

9 OI Tracking Efficiency Measurement of the efficiency of adding silicon hits to COT tracks Use J/ψ → μ + μ - data M. Herndon ε SVX = 74.5 ± 0.3(stat) ± 2.2(sys)% ( data) Average efficiency for adding silicon to two tracks: In silicon fid. ( data) Very low and went down with p T and time! Matt Herndon Problem is with COT alignment Pointing very precise at high pt unless the alignment is not perfect

10 New OI Efficiency Improved silicon pattern recognition code and detector performance Hold search window open to at least 1mm until first silicon hit found M. Herndon old avg (88%) w/ same data (2003) +2.5% and flat - improved code +6% ε SVX improved code and new quality cuts ε SVX = 74.5% → 88.5% → data The Silicon GroupMatt Herndon

TDR 80%  11 Silicon Only Tracking Silicon Standalone Tracking: SiSA Algorithm Use two outer 3D point(r-phi + SAS) and vertex to find initial helix. Gives helix and constraint in z. ISL cooling problems, wire bond oscillation, AVDD2 chip failures, beam incidents effect the stereo side disproportionately SiSA Performance is suboptimal M. Herndon Stephanie Menzemer, Thorsten Schiedle, Matt Herndon

12 Silicon Forward Tracking New forward silicon algorithms Use 1 outer 3D point(r-phi + SAS) and vertex to find initial helix. No constraint Much higher potential performance M. Herndon 45% 70% 85% Z->ee MC Matt Herndon, Thorsten Schiedle Also new forward COT segment based algorithm Combination will be very powerful Antonio Boveia, David Stuart Realistic MC predicts efficiency well

13 L3 Tracking Algorithm to confirm L2 SVT decisions Reduces output from L3 Often a limiting factor - Consumer Server Logger Requirements - Fast Solution Only perform OI tracking Apply no calibration (calibration people were unhappy) Don’t read unnecessary code: Reading L00 or forward ISL Profile all code and optimize anything that seems slow Level 3 tracking Executes in 1/5 the time Loose a few % in efficiency and resolution M. Herndon Matt Herndon Dynamic Pedestal Sub(DPS) very effective

14 L00 Makes a huge difference in B physics Data not usable as is Large coherent noise: Continuous pattern across all strips on a sensor Induced by silicon readout: Different event by event Coupling between cables and shields and due to space constraint Pedestal fit Event by event fit to find the pedestal distribution Ignoring sharp peaks: real clusters Effectively finds clusters CPU intensive: can’t be used in L3 Clusters after pedestal fit good for physics Using tight quality cuts, but probably not necessary Special track fit routine and alignment Tim Nelson Auke Pieter Colijn, Chris Hill, David Stuart Matt Herndon, Aart Heijboer

15 Physics Impact Z strips and L00 B  hh Two tracks always meet in 2D. 3D needed to constrain vertex B s   3D pointing to vertex Use of L00 M. Herndon The B Group Requires Z 3D2D

16 B s Mixing L00/forward tracking B s mixing L00 improves proper time resolution Sensitivity dependent on the exponential of the square of  t 30% muon tags from forward tracking - most powerful tag M. Herndon The B s Mixing Group Improvement in sensitivity at high  ms comes from improvement in vertex resolution Sensitivity at 25ps -1 Adding L00 |V td | / |V ts | =

M. Herndon17 Conclusions Highly successful silicon tracker All aspects of the system making a difference in physics analysis SVXII gives high efficiency tracking and good vertexing for all analysis ISL forward tracking: Seeded tracking used for Z and electroweak physics standard forward tracking for B physics Z strips reduce background in B physics two track modes L00 and SVT critical for B s mixing Many things could still be improved Use of L00 Forward tracking Tracking at high Luminosity Faster code See Rainer and Mircea A/  A (17.25 ps -1 ) = 3.5