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
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Momentum resolution typically ~0.2% for |cos(q)| < 0.65
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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
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~98% in core of 500 GeV light quark jets
Most tracks multiple scattering limited – resolution approaches ~4mm at high pT
>99% of tracks have ≤1 mis-assigned hits
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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
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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
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Realistic Detector Geometry
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Blow-up of vertex detector showing hits on planar sensors
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SiD LOI Geometry – CAD Drawing
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SiD LOI Geometry – Event Display
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Realistic Hit Digitization
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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
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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
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Track Finding Improvements
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No significant changes to track finding algorithm
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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
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See LOI and RP talk at 2008 LCWS for details of tracking algorithm
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
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A Partial List of Outstanding Issues
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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
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Forward Tracking Efficiency
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Look at 100 GeV single muons in sidloi3
Efficiency for reconstructing “findable” tracks drops
precipitously in the forward region
Momen
tum
Genera
ted
Findabl
e
90
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5000
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5001
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4906
3844
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4519
1
Angle
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Found
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Source of the Problem
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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
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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
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Consistency in the s-z View
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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
z  z0  s tan 
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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)
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Kludged solution for now – ultimately should properly account for MS errors
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Updated Tracking Efficiency
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Repeat the tracking efficiency measurements for single muons
100 GeV muons
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Check Low-pT Tracking Efficiency
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“Kludge” to FastCheck class appears to be working
1 GeV muons
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Charge Asymmetry
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First noticed >2 years ago by summer student at SLAC
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Observed in all “circle fit” parameters (curvature, f0, DCA)
Likely an artifact of fitting algorithm
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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
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100 GeV
muons
pT Pull Distributions
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Kalman Fitter Status
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Stanford summer student worked on adapting the trf code to
lcsim track fitting during summer 2010
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Project turned out to be more difficult than expected
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Large and complex code base
Some difficulty in understanding how to properly use trf toolkit
Some success…
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Developed by Dave Adams and Norman Graf for the DØ experiment
Kalman filter portion of trf ported to Java / lcsim by Norman
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
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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
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Pixel Hit Resolution
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The charge deposition model adapted from CDF drifts
segments of charge to the sensor surface
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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-ofgravity 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
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Duplicate Tracks
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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
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Planar geometry can produce multiple hits per layer for tracks
passing through regions where there is sensor overlap
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SeedTracker only allows 1 hit to be shared between two tracks and has strong
bias towards maximizing the number of hits on a track
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
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Slow Track Reconstruction
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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
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Tracker design may play a role
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3D measurements are less prone to combinatorics
Track finding strategy may also have an influence
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Additional hits from overlaps have a multiplicative effect
Outside-in strategies vs inside-out
Hard problem to address / debug
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Especially if the code is working “as designed”
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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
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Position of stereo hits depends strongly on track angle due to
gap between stereo planes
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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
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
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Summary
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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:
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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
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