Status of SiD Silicon Tracker and Tracking Performance Richard Partridge SLAC U. Oregon SiD Workshop

Tracking Performance Overview LOI shows excellent tracking performance for baseline SiD tracker >99% track finding efficiency over most of the solid angle ~98% in core of 500 GeV light quark jets Momentum resolution typically ~0.2% for |cos(q)| < 0.65 s(pT) / pT < 0.5% over most of solid angle for 1 GeV < pT < 100 GeV DCA resolution typically ~15mm for pT = 1 GeV, |cos(q)| < 0.65 Most tracks multiple scattering limited – resolution approaches ~4mm at high pT >99% of tracks have ≤1 mis-assigned hits Fake track rate is 0.07% for tt events New since the LOI: Planar sensor geometry Realistic charge deposition and digitization/clustering Improvements to tracking performance for high occupancy Richard Partridge

Switch to Planar Geometry LOI geometry consisted of cylinders and disks with virtual segmentation New geometry models each silicon sensor – rectangular detectors in barrel, trapezoidal detectors in endcaps Richard Partridge

Realistic Detector Geometry Blow-up of vertex detector showing hits on planar sensors Richard Partridge

SiD LOI Geometry – CAD Drawing Richard Partridge

SiD LOI Geometry – Event Display Richard Partridge

Realistic Hit Digitization Charge deposition for strip detectors based on CDF Si sensor simulation algorithm implemented by Tim Nelson TKN + RP extended strip charge deposition model to pixel detectors, while Nick Sinev has developed a detailed modeling using electric field maps Strip/pixel charges clustered by a nearest neighbor algorithm Hash maps used to achieve approximately linear scaling of clustering time Settable parameters for noise, readout and clustering thresholds Form tracker hits from clusters with expected hit errors Richard Partridge

Track Finding Improvements No significant changes to track finding algorithm See LOI and RP talk at 2008 LCWS for details of tracking algorithm Some structural/implementation changes to improve tracking performance, especially with large numbers of hits Layout of strips / pixels on a sensor was separated from the geometry code allowing for more user control Developed a set of tracking strategies for the planar geometry Strategies used to guide track reconstruction Created a “standard” driver that to run hit digitization / tracking for the sidloi geometry: org.lcsim.recon.tracking.seedtracker.trackingdrivers.sidloi3.MainTrackingDriver Richard Partridge

A Partial List of Outstanding Issues Forward tracking efficiency has gotten worse since the LOI Small charge asymmetry in helix parameters Pixel hit resolution is larger than expected Occasionally produce duplicate tracks Track reconstruction is very slow for some events CLIC people report hits sometimes located incorrectly More on these issues in the following slides Richard Partridge

Forward Tracking Efficiency Look at 100 GeV single muons in sidloi3 Efficiency for reconstructing “findable” tracks drops precipitously in the forward region Angle Momentum Generated Findable Found 90 100 5000 5001 5026 110 5002 120 130 5003 140 150 160 4906 3844 170 4519 1 Richard Partridge

Source of the Problem To speed up the track finding performance, the FastCheck class is used to see if a given pair of hits is consistent with the pT and DCA requirements for the strategy This algorithm was “improved” when I finally figured out how to solve for the circle(s) passing through 2 hits that is tangent to a circle whose radius is the maximum DCA Can use this to determine the allowed pT range for these two hits Reject hit pairs inconsistent with the minimum pT cut New algorithm also gave accurate determination of range in arc lengths s1, s2 Richard Partridge

Consistency in the s-z View Hits passing the checks on pT and DCA in the x-y plane were then checked for consistency with the s-z impact parameter z0 With the improved x-y fits, the range in arc length s became quite narrow (high pT tracks from the origin have s ~ r) The z coordinate for endcap disks is fixed, so the calculated range of z0 for a hit pair can be quite small The geometry of the forward region magnifies multiple scattering errors such that the calculated z0 range is frequently not consistent with the maximum s-z impact parameter To address this problem, the range in s was increased by the maximum allowed x-y impact parameter (1 mm) Kludged solution for now – ultimately should properly account for MS errors Richard Partridge

Updated Tracking Efficiency Repeat the tracking efficiency measurements for single muons 100 GeV muons Richard Partridge

Check Low-pT Tracking Efficiency “Kludge” to FastCheck class appears to be working 1 GeV muons Richard Partridge

Charge Asymmetry First noticed >2 years ago by summer student at SLAC Observed in all “circle fit” parameters (curvature, f0, DCA) Likely an artifact of fitting algorithm Karimake algorithm has no MS correlations, is a non-iterative approximation Proposed solution is development of a Kalman fitter to perform a true helix fit 1 GeV muons 100 GeV muons pT Pull Distributions Richard Partridge

Kalman Fitter Status Stanford summer student worked on adapting the trf code to lcsim track fitting during summer 2010 Developed by Dave Adams and Norman Graf for the DØ experiment Kalman filter portion of trf ported to Java / lcsim by Norman Project turned out to be more difficult than expected Large and complex code base Some difficulty in understanding how to properly use trf toolkit Some success… Ran forward (inside out) track fits using “XY Plane” hits (e.g., barrel) from SeedTracker tracks for the proposed Heavy Photon Search experiment …but work is not complete Need to translate endcap hits into trf “Z-Plane” hits Existing track fitter (FullKalmanFit) works on a list of hits – needs to be modified / extended to include dead material surfaces Further work needed to track down dead material along track path Richard Partridge

Pixel Hit Resolution The charge deposition model adapted from CDF drifts segments of charge to the sensor surface Width of charge distribution on suface depends on track angle, Lorentz drift, and diffusion parameterization The Gaussian charge density from diffusion is integrated over the pixel array If the charge spreads to more than one pixel, a center-of-gravity algorithm is used to determine the hit position In this case, the electronic noise assigned to the pixel readout can potentially have a big effect on hit position resolution If the pixel is very thin, there is little drift/diffusion and this algorithm may tend towards single-hit clusters A more sophisticated pixel charge deposition model has been developed by Nick – someone would need to interface this into the planar geometry/digitization/hit making code Richard Partridge

Duplicate Tracks Occasionally find two tracks with nearly identical track parameters This was not a problem for the LOI where we used virtual segmentation of cylinders / disks SeedTracker only allows 1 hit to be shared between two tracks and has strong bias towards maximizing the number of hits on a track Planar geometry can produce multiple hits per layer for tracks passing through regions where there is sensor overlap Tracking code currently only tries to add 1 hit per layer If the track produces multiple hits in enough layers, can form two separate tracks that do not share hits Solution requires some thought – want to add overlap hits to tracks – but not clear how to do this while performing exhaustive search of all possible track candidates Richard Partridge

Slow Track Reconstruction Does not appear to be an infinite loop Evidence to date points towards it being a “feature”, not a bug May just be a combinatoric increase in the number of possible tracks to check in the core of high energy jets Additional hits from overlaps have a multiplicative effect Tracker design may play a role 3D measurements are less prone to combinatorics Track finding strategy may also have an influence Outside-in strategies vs inside-out Hard problem to address / debug Especially if the code is working “as designed” Richard Partridge

Mis-Placed Hits Past experience suggests the following possible causes: Not enough bits assigned to the strip/pixel readouts to uniquely identify each strip/pixel First thing to check when you see bizarre hit behavior More than once have endured long and tedious tracking of problem through complex digitization code to find the problem was in the compact.xml file Jeremy may be able to add some checking in his identifier code Position of stereo hits depends strongly on track angle due to gap between stereo planes Can give bizarre hit positions, especially for small angle stereo Not a problem during tracking since the hit position and covariance matrix account for the track angle Reports seemed to be correlating this with poor efficiency in the forward direction, but that problem appears to have been due to fast hit check algorithm Richard Partridge

Summary LOI demonstrated that an all-silicon tracker with ~10 hit measurements would give excellent performance at the ILC Substantial effort in developing a more realistic detector simulation is bearing fruit Forward tracking efficiency problem appears to have a “kludge” fix that is working Other issues: Charge asymmetry probably requires the Kalman filter fitter to be completed Large hit error in pixels may be due to noise setting being too large Duplicate tracks appear on occasion – no easy solution Some events take a long time to complete – no easy solution Misplaced hits – too few strip / pixel readout bits?? Some of these issues require focused / concentrated effort – difficult to achieve with present manpower situation Richard Partridge