Sub-Pixelizing Mangle Masks » History » Version 2

Eli Rykoff, 01/29/2014 06:33 PM

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h1. Sub-Pixelizing Mangle Masks
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Currently, we have the full mangle mask which gives the 2" aperture depth at every point in the survey, and the healpix nside=4096 approximation of the mangle masks.  The full mangle mask is ... unwieldy, which it would be nice to use a pixelized version.  Note that this is close to the finest practical pixelization before desktop workstations will struggle with the memory requirements.
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However, as currently implemented, the pixelization simply takes the depth at the central point of that pixel.  As each pixel at nside=4096 is 0.75 arcmin^2, this has trouble around stars, boundaries, etc.
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h2. The Subpixel Method
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A few of us (Alex D-W in particular) have been working on seeing what can be done to calculate the depth on a finer scale and then average it.  Note that in order for this to work we need 2 output maps: the first has the average depth in the pixel *where the image was not completely masked*, and the second is the fraction of the pixel area that is not masked.  In this way we can create an "anti-aliased": map that contains most of the information from the fine scales but is more practical to use.  (In particular, this format is useful for cluster masked-region corrections.)
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The reason that Aurelien has been doing a single-pixel value is that the regular mangle code is too slow for this procedure (which is optimized for other things.)  Luckily, Erin Sheldon has adapted some super-fast code from Martin White in the "pymangle" python package that is over 50x faster than the default mangle code for looking up weights ... this is very useful!  
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h2. The Tests
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For a first test, I have taken the i-band mask in the field of RXJ2248, and run nside=4096 with 1, 4, 16, 64, and 256 sub-pixels.  In practice, to do all of SVA1 (or the DES footprint), 256 subpixels is "doable" but significantly slower.  If we can get away with 16 or 64 that would make things run a lot faster.  (The run time for the whole lot on this field was 20 minutes, about half of that for the 256 subpixel run).
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I look at the output weight and fractional observed maps, as well as the ratio of the weights and fracs to the fiducial 256 sub-pixel run.  For each of these, I calculate the fraction of "bad" pixels where the weight is misestimated by >2% or the bad fraction is misestimated by >5%.  These are somewhat arbitrary cuts, but they get to the rate of bad outliers where our sampling was clearly insufficient.  I have also plotted a zoomed in region around a star mask.
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h3. 256 Subpixels
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h3. 64 Subpixels
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h3. 16 Subpixels
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h3. 4 Subpixels
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h3. 1 (sub)pixel
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h2. Summary
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In terms of getting the average depth, things actually converge very quickly.  Although the single pixel has a significant number of outliers (3%) and some scatter, even at 4 subpixels the scatter is reduced and the outlier fraction is down to 1%.  But 64 subpixels is almost as good as 256, with only 0.3% of pixels with a difference of more than 2% for the mean depth.  
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In terms of getting the bad fraction, things are trickier.  First, we're dealing with a quantized value when we only have 16 or 64 subpixels, but that's something we can live with.  But even with 16 subpixels we have 5% of the time the masked area is misestimated by more than 5%.  On the other hand, with 64 subpixels only 2% of the pixels are misestimated by > 5% and most of these are only slight outliers.
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Looking at these plots, I think that 64 subpixels is a good compromise between computation time and fidelity.