Suggestions for galaxy-count extinction calibration » History » Version 1

Gary Bernstein, 05/05/2013 12:27 PM

1 1 Gary Bernstein
h1. Suggestions for galaxy-count extinction calibration
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3 1 Gary Bernstein
The goal is to determine the mean galaxy counts per CCD as a function of (filter, seeing, sky brightness, extinction) and then invert this to estimate the extinction given measurements of (galaxy counts, seeing, sky brightness).  Also as a byproduct be able to identify CCDs with highly unlikely galaxy counts (too high or too low) which would indicate either a hardware failure or some bright object contaminating the image.
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5 1 Gary Bernstein
Based on discussions with Basilio & others, here are suggestions for steps in this project:
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7 1 Gary Bernstein
# Choose a standard SExtractor configuration and a catalog cut used to identify galaxies.  The cut should be on some signal-to-noise ratio (e.g. the error in one of the magnitudes) rather than an absolute magnitude cut since the whole point is to identify images with extinction before we have set magnitude zeropoints.  It would be best if the SExtractor outputs can be reproduced from the First Cut catalogs at DESDM.
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# Selecting images on photometric nights at low airmass, get an idea of the mean and distribution of galaxy counts per CCD as a function of (seeing FLUX_RADIUS, sky brightness) at the extinction factor f=1 that we'll define to be one airmass in clear conditions.
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# The galaxy-count calibration can be extended to brighter skies by simply adding noise to the image and re-running it (adding Gaussian noise with variance V ADU^2 is equivalent to observing with a sky that is Vg ADU per pixel brighter, where g is the gain).
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# The extension of the galaxy-count prediction to atmospheric transmission f<1 can't be done with cloudy data because we don't know what the extinction is for any given exposure (unless we have post-facto photometric solutions in hand.  I expect it is better to artificially construct extincted images as follows:  
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## Take a cloudless, low-airmass image and multiply it by f to reduce the signal.
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## The variance of the sky has also been scaled by f^2.  So if the sky level was S (and the variance was S/g), you need to add back Gaussian noise to the image with variance (1-f^2)S/g to restore the noise appropriate to the observed sky level.