Fast Tracking of Strip and MAPS Detectors
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Transcript of Fast Tracking of Strip and MAPS Detectors
Fast Tracking of Strip and MAPS Detectors
Joachim GläßComputer Engineering, University of
Mannheim
Target application is trigger 1. do it fast 2. check precision
• Contents– STS Tracking (Strip Detectors)
• Hough Transform
– MAPS Tracking• Kalman Filter
October 7, 2004 CBM Collaboration Meeting
STS TrackingHough Transform of Parabola
z
x1 /P z
de te c to r Hough space
one track one po in t
z
x1 /P z
de te c to r Hough space
seven h its seven cu rves
x = z20.3 By
2 Pz
=0.3 By z2
2 x
Pz
1
=0.3 By (z cos + x sin)2
2 (z sin – x cos)
Pz
1
<=>
rotated by (Px/Pz):
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
Z
X
Y
STS Tracking3-D Hough Transform
1/Pz
Px/PzPy/Pz
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
• 3-D according to the three parameters of a track– bending 1/Pz, angles and (Px/Pz, Py/Pz)
– Py/Pz detector slice corresponds to one 2-D Hough-histogram– 2-D Hough-histograms can be processed independently– Py/Pz planes are overlapping ( due to multiple scattering)
z
x
de te c to r
STS TrackingHardware Implementation
hit coordinatesx, z
LUT shiftregisters
1 bit/row
start
Systolic processing of space points (1 hit/cycle)
D Q
CNT
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
z
x
de te c to r
STS TrackingHardware Implementation
hit coordinatesx, z
LUT shiftregisters
1 bit/row
start
Systolic processing of space points (1 hit/cycle)
D Q
CNT
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
z
x
de te c to r
STS TrackingHardware Implementation
hit coordinatesx, z
LUT shiftregisters
1 bit/row
start
Systolic processing of space points (1 hit/cycle)
D Q
CNT
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
z
x
de te c to r
STS TrackingHardware Implementation
hit coordinatesx, z
LUT shiftregisters
1 bit/row
start
Systolic processing of space points (1 hit/cycle)
one hit -> one curve
Cell number of peakdetermines track parameters
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
STS TrackingSimulation Results
• Efficiency• e: found tracks/all tracks with P > 1GeV/c• g: ghost tracks/processed tracks• i: identified tracks/processed tracks
– 31 x 95 x 383 e: 95 %, g: 25 %, i: 45 %– 63 x 191 x 255 e: 93 %, g: 12 %, i: 65 %
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
STS TrackingSimulation Results
• Precision of the reconstructed momentum– 63 x 191 x 255
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
STS TrackingHardware Implementation
• Processing speed (rough estimations)• Real-time tracking (emphasis is on fast)
– 1 hit/cycle
– e.g. 10 Gb/s link with 64 bit/hit => 150 x 106 hits/s1 hit/cycle => 150 MHz
– 1500 to 10000 hits/event => 10µs to 100µs
– total number of processing unitsca. 200 x 10 Gb/s links needed for STS=> ca. 200 units
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
z
x
de te c to r
STS Tracking of Strip DetectorsHardware Implementation
hit coordinatesx, z
LUT shiftregisters
1 bit/row
start
Processing of strip detector data
one hit (x strip) -> one plane (horizontal)
stop
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
z
x
de te c to r
STS Tracking of Strip Detectors Hardware Implementation
hit coordinatesx, z
LUT shiftregisters
1 bit/row
startstop
Processing of strip detector data
one hit (y strip) -> one plane (vertical)
Logical AND gives same Hough Transform thanintersection point of strips(+ all fakes given by strip layout)
to do: angles other than 90°,especially small angles
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
• MAPS layer 1 and 2(monolithic active pixel sensors)– high resolution < 10 µm– slow readout > 10 µs
pile up of ca. 100 events
• Kalman Filter track following– track hits from L3 – L5 as
seed • later Hough transform
– emphasis is on fast:process 1 track/cycle
10
0 µ
m S
i
10
0 µ
m S
i 10
0 µ
m S
i
MAPS TrackingKalman Filter Track Following
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
• y-z plane (non-bending) => straight line– y = m * z + c
– start with m0 = y0/z0, c0=0
– predict position in previous layer yk = mk-1 * zk + ck-1
– measure position (distance predicted – real yk)
– update estimate with measurement• yk, mk, ck are simple function of mk-1, ck-1 and yk
• yk < 500 µm => needs few bits to code
• noise and error covariance are chosen to „believe“ the latest measurement
^
^
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
MAPS TrackingKalman Filter Track Following
• x-z plane (magnetic field) => parabola– x = a z2 + b z + c
– start with a0, b0 from hits in layer 3, 4, 5 (or Hough-Transform), c0=0
– predict position in previous layer xk = ak-1 zk2 + bk-1 zk + ck-1
– measure position (distance predicted – real xk)
– update estimate with measurement• xk, ak, bk, ck are simple functions of ak-1, bk-1, ck-1, xk
• xk < 500 µm => needs few bits to code
• noise and error covariance are chosen to „believe“ the latest measurement
^
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
^
MAPS TrackingKalman Filter Track Following
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
• no binning of data• max distance 0.5 mm
• nearest hit as function of PZ
• tracks with lower momentumare worse
• w/o pileup– 98% of nearest hits
from same track
• with pileup– no missing hits– less hits from same track
(ca. 10 %)
MAPS TrackingSimulation Results
• coefficients and parameters with 10 – 12 bit sufficient– no double precision floating point needed– old values -> LUTs -> adder -> LUT -> new value
• associative hit memory
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering
hits from detector layer
predicted position x, y of nearest hit
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MAPS TrackingHardware Implementation
Summary
• Hough Transform– global algorithm– processing time ~ number of hits– possible implementation using FPGA and LUT– efficiency ca. 95% of tracks found– relatively high ghost rate– able to handle strip detectors
• Kalman Filter– MAPS pile up ca. 100 min. bias events– w/o pile up ca. 98% of nearest hits from same track– with pile up ca. 88% of nearest hits from same track
ca. 12 % of nearest hits from other events– possible implementation using FPGA and LUT
• simple calculation• associative hit memory
Joachim Gläß, Univ. Mannheim, Institute of Computer Engineering