1. Background Estimation and Matching for Strip82 Coadds Yusra AlSayyad University of Washington DM Review September 6, 2012

2. 2 Outline 1) Overview of processing steps – Image to image matching 2)Discussion of Design Choices -Estimation -Median vs. Mean. -NN2 vs. Not. -Common Mask vs. per-Pair Mask -Interpolation -Detection.getBackground() {binsizes 128,256,378,512} -Vs. Chebyshev polynomial orders {2,3,4,5} 3)Continuation of processing steps – strip to strip matching 4)Plan for next quarter

3. 3 Very High level processing steps 1)Define any sized area of sky to coadd 2)Select a reference run per strip 3) For each field in reference run, generate a background matched coadd 4) Stitch fields together to make coadded strips. 5) Match and stitch strips 6) Fit background to large coadd; subtract. 7) Iterate NOTE: this process works for Stripe 82 only. Extending to LSST is a non-trivial problem.

4. 4 Single field Coadd Processing Steps 1) Find Images that overlap reference run 2)PSF matching and warping 3)Photometric scaling 4) Background Matching –Estimation –Fitting spatial variation 5) Coaddition

5. 5 Photometric Scaling Reference Run

6. 6 Photometric Scaling Field 110 Field 111 Input images warped image

7. 7 Photometric Scaling Field 110 Field 111 Overlap regions need to be identical

8. 8 Photometric Scaling Scaling x in pixels (warped image frame)

9. 9 Photometric Scaling Scaling x in pixels (warped image frame)

10. 10 Background Matching

11. 11 Background Estimation: Difference Image

12. 12 How much to mask? Sum of masks from 2 images Only. Call this “pairwise mask”

13. 13 How much to mask? Sum of all ~30 masks. Call this “common mask”

14. 14 Background Estimation: Bin difference image

15. 15 Background Estimation: counts histogram For example: Some are nice and Gaussian. Some are not. mean median Diff of medians

16. 16 A few Background Estimation Options For a superpixel (bin) in a difference image: –Mean –Median –For N input images, find least sq. solution of all N(N-1)/2 difference images. (“NN2” Barris et al. 2009)

17. 17 We will examine these 3 Estimate Choices Later in presentation: -Estimation -Common Mask vs. per-Pair Mask -Median vs. Mean. -NN2 vs. Not.

18. 18 Background Matching: Fitting Spatial Variation RA Dec –meas.algorithms.detection.getBackground(differenceImage) –afw.math.Chebyshev1Function2D()

19. 19 Background Matching: Fitting Spatial Variation –meas.algorithms.detection.getBackground(differenceImage) –afw.math.Chebyshev1Function2D() RA Dec

20. 20 Examine choices Background estimation - Mean vs Median - Masking: common mask vs. per-pair mask - least squares fit of n(n-2)/2 difference images? Spatial Variation –How to model difference image (reference image – input image)? –Compare:.detectionBinsize{512,378,256,128}, vs.backgroundOrder = {2,3,4,5}

21. 21 1) First Qualitative Look 2) Quality Metric –Because input images are matched to a reference run, resulting coadds should be seamless. –=> Use RMS of overlap regions as background matching quality metric R.A.

22. 22 take rms of the overlap region’s unmasked pixels For example: The difference of two overlap regions

23. 23 Qualitative Look at Estimation: Pairwise Masks

24. 24 Qualitative Look at Estimation: Common Mask

25. 25

26. 26 Metric: Estimation

27. 27 Qualitative Look: Interpolation –meas.algorithms.detection.getBackground(differenceImage) –afw.math.Chebyshev1Function2D() RA Dec

28. 28 –meas.algorithms.detection.getBackground(differenceImage) –afw.math.Chebyshev1Function2D() RA Dec Bin size comparison Qualitative Look: Interpolation

29. 29 –meas.algorithms.detection.getBackground(differenceImage) –afw.math.Chebyshev1Function2D() RA Dec Qualitative Look: Interpolation

30. 30 Qualitative Look: Interpolation

31. 31 meas.algorithms.detection.getBackground() Bin size comparison Qualitative Look: Interpolation

32. 32 Quality Metric: Interpolation Methods

33. 33 Strip to Strip matching

34. 34 The Plan 1)Refactor uw background matching code to use the butler and skyMap. Merge in features from ticket 2216. Make useable by other pipeTasks. 2) Incorporate stitching code: a) Stitch patches (RA-wise) into strips (should just work) b) Finish code to match and stitch N-S Strips (in the declination direction using skyMap) 3) Validate by comparing mags, counts (to assess depth) with deeper catalogs. 4)Add enhancements: a) Allow for coadds of any size to be generated per input specs. b) Post-stitching background subtraction. e.g.: i) Coadd components and stitch together. ii) Fit a background model to this large chunk. iii) Subtract this background model from the coadd. iv) Use this coadd as the reference for another iteration of background matched coadd, leaving the individual science images with a well-subtracted background optimal for detection and measurement.