detecting cosmic rays in infrared data rachel anderson karl gordon 11/26/2015riab monthly meeting
TRANSCRIPT
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Detecting Detecting Cosmic Rays in Cosmic Rays in
Infrared DataInfrared Data
Rachel Anderson Rachel Anderson Karl Karl GordonGordon
04/18/23RIAB Monthly Meeting
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OutlineOutline
The CR ProblemThe CR Problem
Linear Fit AlgorithmLinear Fit Algorithm
CR Detection MethodsCR Detection Methods The 2-Point Difference MethodThe 2-Point Difference Method The Deviation from Fit MethodThe Deviation from Fit Method The Y-Intercept MethodThe Y-Intercept Method
ResultsResults
04/18/23RIAB Monthly Meeting
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The CR ProblemThe CR Problem
04/18/23RIAB Monthly Meeting
Every 1000 seconds, up to 20% of the field Every 1000 seconds, up to 20% of the field of view of JWST will be affected by CRsof view of JWST will be affected by CRs
Offenberg, J.D., et al. 1999Rouscher, B., et al. 2000, STScI-NGSTR-0003A
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The CR ProblemThe CR Problem
04/18/23RIAB Monthly Meeting
Every 1000 seconds, up to 10% - 20% of the field Every 1000 seconds, up to 10% - 20% of the field of view of JWST will be affected by CRsof view of JWST will be affected by CRs
Offenberg, J.D., et al. 1999Rouscher, B., et al. 2000, STScI-NGSTR-0003A
+ CR =
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The CR Problem (cont.)The CR Problem (cont.)
04/18/23RIAB Monthly Meeting
The Question: What is the best we can do, The Question: What is the best we can do, given the noise in the ramp?given the noise in the ramp?
The How: The How: Simulate non-destructive read ramps. Simulate non-destructive read ramps. Add some CRs with various magnitudes and Add some CRs with various magnitudes and
locations.locations. Test CR detection methods, then try to find Test CR detection methods, then try to find
them.them.
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Linear Fit AlgorithmLinear Fit Algorithm
04/18/23RIAB Monthly Meeting
Fixsen, D. J., et al. 2000, PASP, 112, 1350Gordon, K. D., et al. 2005, PASP, 117, 503Hogg, D. W. et al. 2010, ArXiv e-prints
We want to solve the equation: Y = AX, with solution: X = [ATC-1A]-1[ATC-1Y]
Y =
y1
y2
…
yn
, A = , and C =
1 x1
1 x2
… …
1 xn
σy12 c1,2 … c1,n
c2,1 σy22 … c2,n
… … … …
cn,1 cn,2 … σyn2
, X = b
m
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Linear Fit AlgorithmLinear Fit Algorithm
04/18/23RIAB Monthly Meeting
Fixsen, D. J., et al. 2000, PASP, 112, 1350Gordon, K. D., et al. 2005, PASP, 117, 503Hogg, D. W. et al. 2010, ArXiv e-prints
We want to solve the equation: Y = AX, with solution: X = [ATC-1A]-1[ATC-1Y]
Y =
y1
y2
…
yn
, A = , and C =
1 x1
1 x2
… …
1 xn
σy12 c1,2 … c1,n
c2,1 σy22 … c2,n
… … … …
cn,1 cn,2 … σyn2
, X = b
m
It is easiest to think of C as the sum of two matrices: C = R + P
, and P =
p12 p1
2 p12 …
p12 p2
2 p22 …
p12 p2
2 … …
… … … pn2
R = r2 I
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CR Detection MethodsCR Detection Methods
Three methods:Three methods: 2- Point Difference2- Point Difference Deviation from FitDeviation from Fit Y-Intercept Y-Intercept
For each method:For each method: Detect CRs (largest first)Detect CRs (largest first) Calculate the slope for the resulting ‘semi-ramps’Calculate the slope for the resulting ‘semi-ramps’ Calculate final slope of entire ramp by taking Calculate final slope of entire ramp by taking
weighted average of the slopes of the ‘semi-weighted average of the slopes of the ‘semi-ramps’ramps’
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Regan, M. 2007, JWST-STScI-001212Robberto, M. 2008, JWST-STScI-0001490, SM-12
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2-Point Difference 2-Point Difference
04/18/23RIAB Monthly Meeting
| di – μd |σd
Ratio =
di = yi – yi-1
μd: median of di’s
σd = √2rn2 + pn
2
… where pn = √μd
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Deviation From FitDeviation From Fit
04/18/23RIAB Monthly Meeting
devi =yi – fi
σi
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Y-InterceptY-Intercept
04/18/23RIAB Monthly Meeting
| b2 – b1 |σb
Ratio =
σb = √2rn2 + pn
2
… where pn = √ m ,
and rn is calculatedfrom un-correlated errors in our linear-fit program.
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Results: Results: Fraction Found vs. False Fraction Found vs. False
DetectionsDetections
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40 Frames, Input Slope: 10.00 DN/s
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Results: Results: Fraction Found vs. False Fraction Found vs. False
DetectionsDetections
04/18/23RIAB Monthly Meeting
40 Frames, Input Slope: 0.00 DN/s
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Results: Multiple CR’sResults: Multiple CR’s
04/18/23RIAB Monthly Meeting
2-Point Difference
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ConclusionsConclusions
To optimize results, our linear fit algorithm To optimize results, our linear fit algorithm must take into account correlated and un-must take into account correlated and un-correlated errors.correlated errors.
The 2-Point Difference method is simple, The 2-Point Difference method is simple, fast, consistent, and best for photon-noise fast, consistent, and best for photon-noise dominated regime.dominated regime.
The Y-Intercept method is better in read-The Y-Intercept method is better in read-noise dominated regime.noise dominated regime.
04/18/23RIAB Monthly Meeting
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Results: Number of Results: Number of FramesFrames
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Slope = 10.0 DN/sFraction of False Detections = 0.05
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Results: Various Results: Various Slopes Slopes
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2- Point Difference
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Results: Various Results: Various Slopes Slopes
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Deviation from Fit
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Results: Various Results: Various Slopes Slopes
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Y-Intercept
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Linear Fit Algorithm Linear Fit Algorithm (cont.)(cont.)
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slope
calc /
(sl
ope-1
)
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Results: Number of Results: Number of FramesFrames
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Slope = 0.00 DN/s Slope = 10.00 DN/s
2-Point Difference
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Results: Number of Results: Number of FramesFrames
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Deviation from Fit
Slope = 0.00 DN/s Slope = 10.00 DN/s
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Results: Number of Results: Number of FramesFrames
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Y-Intercept
Slope = 10.00 DN/sSlope = 0.00 DN/s
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Results: Multiple CR’sResults: Multiple CR’s
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Deviation from Fit
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MIRI ParametersMIRI Parameters
Frame Time (s) 27.7
Slope (SN/s) 10.0
Zero Point (DN) 3,000.0
Number of Frames 40
Gain (e-/DN) 7.0
Dark Current (e-/s) 0.02
Read Noise (e-/sample)
16.0/√8
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Results: Multiple CR’sResults: Multiple CR’s
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Y-Intercept