detecting cosmic rays in infrared data

Detecting Detecting Cosmic Rays in Cosmic Rays in

Infrared DataInfrared Data

Rachel Anderson Rachel Anderson Karl Karl GordonGordon

04/22/23RIAB Monthly Meeting

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




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



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 =

The CR Problem (cont.)The CR Problem (cont.)


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.



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



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

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’


Regan, M. 2007, JWST-STScI-001212Robberto, M. 2008, JWST-STScI-0001490, SM-12

2-Point Difference 2-Point Difference


| di – μd |σd

Ratio =

di = yi – yi-1

μd: median of di’s

σd = √2rn2 + pn

2

… where pn = √μd

Deviation From FitDeviation From Fit


devi =yi – fi

σi

Y-InterceptY-Intercept


| b2 – b1 |σb

Ratio =

σb = √2rn2 + pn

2

… where pn = √ m ,

and rn is calculatedfrom un-correlated errors in our linear-fit program.

Results: Results: Fraction Found vs. False Fraction Found vs. False

DetectionsDetections


40 Frames, Input Slope: 10.00 DN/s

Results: Results: Fraction Found vs. False Fraction Found vs. False

DetectionsDetections


40 Frames, Input Slope: 0.00 DN/s

Results: Multiple CR’sResults: Multiple CR’s


2-Point Difference

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.


Results: Number of Results: Number of FramesFrames


Slope = 10.0 DN/sFraction of False Detections = 0.05

Results: Various Results: Various Slopes Slopes


2- Point Difference



Deviation from Fit



Y-Intercept

Linear Fit Algorithm Linear Fit Algorithm (cont.)(cont.)


slope

calc /

(sl

ope-1

)



Slope = 0.00 DN/s Slope = 10.00 DN/s

2-Point Difference



Deviation from Fit

Slope = 0.00 DN/s Slope = 10.00 DN/s



Y-Intercept

Slope = 10.00 DN/sSlope = 0.00 DN/s



Deviation from Fit

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




Y-Intercept

detecting cosmic rays in infrared data

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