Detecting Cosmic Rays in Infrared Data

1. Detecting Cosmic Rays in Infrared Data Rachel Anderson  Karl Gordon RIAB Monthly Meeting

2. Outline • The CR Problem • Linear Fit Algorithm • CR Detection Methods • The 2-Point Difference Method • The Deviation from Fit Method • The Y-Intercept Method • Results RIAB Monthly Meeting

3. The CR Problem • Every 1000 seconds, up to 20% of the field of view of JWST will be affected by CRs Offenberg, J.D., et al. 1999 Rouscher, B., et al. 2000, STScI-NGSTR-0003A RIAB Monthly Meeting

4. The CR Problem • Every 1000 seconds, up to 10% - 20% of the field of view of JWST will be affected by CRs + CR = Offenberg, J.D., et al. 1999 Rouscher, B., et al. 2000, STScI-NGSTR-0003A RIAB Monthly Meeting

5. The CR Problem (cont.) • The Question: What is the best we can do, given the noise in the ramp? • The How: • Simulate non-destructive read ramps. • Add some CRs with various magnitudes and locations. • Test CR detection methods, then try to find them. RIAB Monthly Meeting

6. Linear Fit Algorithm We want to solve the equation: Y = AX, with solution: X = [ATC-1A]-1[ATC-1Y] , and C = , A = , X = Y = Fixsen, D. J., et al. 2000, PASP, 112, 1350 Gordon, K. D., et al. 2005, PASP, 117, 503 Hogg, D. W. et al. 2010, ArXiv e-prints RIAB Monthly Meeting

7. Linear Fit Algorithm We want to solve the equation: Y = AX, with solution: X = [ATC-1A]-1[ATC-1Y] , and C = , A = , X = Y = It is easiest to think of C as the sum of two matrices: C = R + P R = r2I , and P = Fixsen, D. J., et al. 2000, PASP, 112, 1350 Gordon, K. D., et al. 2005, PASP, 117, 503 Hogg, D. W. et al. 2010, ArXiv e-prints RIAB Monthly Meeting

8. CR Detection Methods • Three methods: • 2- Point Difference • Deviation from Fit • Y-Intercept • For each method: • Detect CRs (largest first) • Calculate the slope for the resulting ‘semi-ramps’ • Calculate final slope of entire ramp by taking weighted average of the slopes of the ‘semi-ramps’ Regan, M. 2007, JWST-STScI-001212 Robberto, M. 2008, JWST-STScI-0001490, SM-12 RIAB Monthly Meeting

9. 2-Point Difference | di – μd | σd Ratio = di = yi – yi-1 μd: median of di’s σd = √2rn2 + pn2 … where pn = √μd RIAB Monthly Meeting

10. Deviation From Fit yi – fi σi devi = RIAB Monthly Meeting

11. Y-Intercept | b2 – b1 | σb Ratio = σb = √2rn2 + pn2 … where pn = √ m , and rn is calculated from un-correlated errors in our linear-fit program. RIAB Monthly Meeting

12. Results: Fraction Found vs. False Detections 40 Frames, Input Slope: 10.00 DN/s RIAB Monthly Meeting

13. Results: Fraction Found vs. False Detections 40 Frames, Input Slope: 0.00 DN/s RIAB Monthly Meeting

14. Results: Multiple CR’s 2-Point Difference RIAB Monthly Meeting

15. Conclusions • To optimize results, our linear fit algorithm must take into account correlated and un-correlated errors. • The 2-Point Difference method is simple, fast, consistent, and best for photon-noise dominated regime. • The Y-Intercept method is better in read-noise dominated regime. RIAB Monthly Meeting

16. Results: Number of Frames Slope = 10.0 DN/s Fraction of False Detections = 0.05 RIAB Monthly Meeting

17. Results: Various Slopes 2- Point Difference RIAB Monthly Meeting

18. Results: Various Slopes Deviation from Fit RIAB Monthly Meeting

19. Results: Various Slopes Y-Intercept RIAB Monthly Meeting

20. Linear Fit Algorithm (cont.) slopecalc / (slope-1) RIAB Monthly Meeting

21. Results: Number of Frames 2-Point Difference Slope = 0.00 DN/s Slope = 10.00 DN/s RIAB Monthly Meeting

22. Results: Number of Frames Deviation from Fit Slope = 0.00 DN/s Slope = 10.00 DN/s RIAB Monthly Meeting

23. Results: Number of Frames Y-Intercept Slope = 0.00 DN/s Slope = 10.00 DN/s RIAB Monthly Meeting

24. Results: Multiple CR’s Deviation from Fit RIAB Monthly Meeting

25. MIRI Parameters RIAB Monthly Meeting

26. Results: Multiple CR’s Y-Intercept RIAB Monthly Meeting