Exploiting PhoSim for Data Management
Algorithm Development::Simulated Effects (3)
A.Rasmussen (SLAC)
Are our current models good enough? What about other models
of physical sensor effects outside of Phosim? Should we
incorporate them or use them differently?
-orHow can sensor characterization data be used to apply a realistic
instrument signature onto simulated data..? (to help close loop
between simulations, DM, find and quantify systematics in data)
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Good enough?
• It appears that LSST sensors will inject astrometric and shape
transfer errors into the data if it is assumed that the sky is
recorded onto a continuous, regular grid of pixels with square
boundaries.
• By the same mechanisms (and assumptions), flat field
response will contain anomalies/distortions.
• Physical effects are broadly divided into fixed pattern and
dynamic effects.
• Short answer to 1st question: we don’t know yet, it depends
on how data will be used, but quantitative comparisons are
still being made between sensor characterization data and
modeled response:
– Heuristic (deduce distortions from raw/stacked flat field response)
– Ad-hoc (insert pixel boundary, etc., errs to impart FF & sky distortions)
– Physical (grounding to provide dependence on physical conditions, “”)
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Good enough? (2)
• Heuristic response has gained traction following confirmation of
pixel size variation as origin specific spot projector tests that
confirmed pixel size variation as origin of anomalies in flat field
response (edge & midline) and DECam instrumental magnitude
error correlation with flat field response (tree rings)
• Ad-hoc approach may qualitatively reproduce characterization (flat
field) data while injecting self-consistent distortions into PhoSim
images.
• Physical (electrostatic) treatment appears to provide quantitative
agreement to flat field response. Focused targets, flat field
response and autocorrelation matrices prepared and analyzed
within a simulation framework. (calculation result files, not drift
calculation code, may provide efficient interface)
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Some leading terms
• Excluding:
– PRNU driving fixed pattern pixel boundary noise σy
– PRNU driving fixed pattern channel stop noise σξ
– Exotic flat field response problems like “Bamboo”:
Backside bias
voltage
dependence of
bimodal fixed
pattern flat
field distortion
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Results & comparisons to data: midline
midline feature modeled as a
isolated channel stop implant
extending across sensor’s width
cf. a (black:data; red:drift calculation)
Flat field distortion (rel.)
Astrometric shift (pixels)
Quantitative agreements between drift model &
flat field distortions: “tearing” onset features
Black: data; Red: calculation
“dark border” (occurs between adjacent amplifier segments)
ξ=+5ξ0
ξ=+1ξ0
ξ=-5ξ0
ξ=-1ξ0
“bright finger” (occurs in column pairs straddling isolated,
hole-saturated channel stops)
Results & comparisons to data: edge rolloff
cf. b (black:data; red:drift calculation)
Flat field distortion (rel.)
Edge rolloff modeled
as a truncation in the
channel stop array of
implants
Astrometric shift (pixels)
nb: no guard drain bias included in calc.
Example for edge roll-off:
Predicted depth- or wavelength-dependence
Surface conversions
(100um from channel)
Midway conversions
(50um from channel)
Deep conversions
(20um from channel)
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Depth dependence of pixel
boundaries near sensor edge
Wavelength
dependence of
edge response
(p.doherty) -
Depth dependence of
column boundaries, first
16 pixels (drift calc):
Note differences in curve
shapes!
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Additional dependencies to the roll-off
measured in lab (p.doherty)
Difference by manufacturer/design
Guard ring drain bias voltage dependence
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Summarize or continue?
• Modeling & data comparison: Tree rings
• Modeling & data comparison: channel content (B/F)
• Detailed pixel distortion data appears to be available
in flat field response measurements
– Keep abreast of distortion flux dependence (FF)
– Coordinate with DM to understand what sort of ancillary
pixel data* should be prepared, that can realistically be
extracted from data
– Efficiently generate flat field, focused spot and generalized
mean-variance (autocorrelations)
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Tree rings
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Backdrop field validation (4)
Tree ring distortion feature amplitude depends
on backside bias:
Tree ring feature extraction
1%
1%
1%
Backdrop field validation (5)
Functional Impurity
derivative gradient
Drift coefficient function is drawn from the
drift calculation
Predicted wavelength
dependence of PSF-level
distortions (excl. pixel-level)
Pixel-level and PSF-level
distortions arising from a
periodic function in underlying
“hidden variable”
Scaling parameter
determination by fitting
observable quantities
Drift coefficient curves
specific to backside bias
setting
Example of a drift coefficient calculation – for tree ring
flat field-, astrometric- and pixel shape distortions
Channel content Greens function
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Depth (distance from gates)
Depth (distance from gates)
Cross-cuts in the periodic electrostatic
barrier (perturbative) field
Serial address (x)
Parallel address (y)
Drift calculation examples
Launching position:
(x,y,z)=(9.31,8.71,100)um
BSS=-48,-58,-68,-78V
Serial address (x)
8um
Parallel address (y)
Depth (distance from gates)
[01]
2um
[11]
y
y
x
[00]
@ (-5,-5)um
2um
x
8um
@ (-5,-5)um
2um
[10]
8um
Pixel distortion Greens function
(induced by collected charge dipole moment)
1p0
2p0
8p0
16p0
4p0
(4p0)
Detailed mean-variance curves, autocorrelation matrices and point-source distortion
may be computed (also for adjacent BSS, barrier clock and wavelength/SED)
Relative barrier strengths (channel stops vs. barrier clocks)
Antilogus et al. 2014:
Pixel correlations vary
By factor of 3 (A01 vs. A10)
Channel stop barrier strength is
tuned to reproduce the factor of 3
between pixel area distortion
response to collected charge in (0,0)
(A01 vs A10)
Parameter estimation may be
further constrained with inclusion of
additional autocorrelation matrix
element ratios
Autocorrelation maps from simulated flat fields
(two drift coefficients, all distance effects applied < 0.5 pixels)
å( m1
k,l
Ai, j º
k,l
- m2 k,l ) ( m1k+i,l+ j - m2 k+i,l+ j )
å( m1
k,l
+ m2 k,l )
I,J
å (m
k,l
»
- m ) ( mk+i,l+ j - m )
k=K,l =L
k,l
s
I,J
2
å
k=K,l=L
Mean-variance term
Shifted terms (i != 0 && j !=0 )
1
Brighter/Fatter
Ellipticity
kernel S2E1
Systematic effects to focused
images consistent with
autocorrelation features
Source input parameters:
aspect ratio = 1.05:1.0
FWHM
= 3.0 pix
centroid
=
(0.25,0.25)
Orientation = 30°
E1 component
E2 component
Ellipticity
(delivered)
Orientation
(delivered)
parallel
φ
serial
Black: no covariance
Red: covariance model
parameterized by the drift
coefficients:
Ancillary pixel data corresponding to channel
content Greens function (
)
Pixel astrometric errors
Pixel 2nd moments (S2, E1, E2)
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