Regions of Bad Photometry » History » Version 6
Eli Rykoff, 10/29/2013 05:30 PM
h1. Regions of Bad Photometry
h2. Bad Photometry Maps
Starting from the bad images below, I pixelized the sky and made a map of the fraction of objects in a given pixel (~0.5 deg^2) that has "crazy colors" in either g-r or i-z. These colors are defined as g-r < -1 or g-r > 4 or i-z < -1 or i-z > 4 (using MAG_AUTO). Of course there should be some number of faint objects that have these strange colors due to errors, but these should be negligible. In fact, almost all of these objects are spurious detections of artifacts. The following map shows the output map. Note that the overall mask is chosen to have decent imaging in g,r,i,z according to a rough calculation of limiting magnitudes. This is why the central strip at dec = -55 isn't covered, because this region is extremely shallow.
While most of the survey is okay, some regions are not, with some pixels having as many as 25% of their objects with crazy colors. We want to both mask these in SVA1 at the catalog level, and mask these in the individual images for Y1 runs.
Bright satellite streaks (which are yet unmasked) show up as long ~3 degree narrow clusters of bad objects in the coadds. The good news is that these can be masked in the finalcut images because they are "unique" events doing this masking won't significantly decrease the depth in the final coadds. The streaks range from bright satellites:
To crazy insane streaks. The following (at RA~65, Dec~-57) appears to be an airplane. Looking at the raw images (before the sky subtraction screws things up) it's clear that this is low enough to be out of focus...plus there's an extra blinking light. It should be possible to use the size of the streak to calculate the altitude of the airplane...
h1. Original Post
Brad Benson looked at SPT S/N for all the redMaPPer (v5.7) clusters in the SPT region. He noticed there was a class of high richness clusters with no SPT signal. What's up with that?
h2. The bad clusters
These are riz color composite images of the bad clusters. Note that all of them are junk regions due to scattered light!
print, j, spt_sn(ind(j)), data(ind(j)).zred, data(ind(j)).lambda_zred, data(ind(j)).ra, data(ind(j)).dec
1 -1.85195 0.707742 315.477 69.466146 -61.502703
2 0.839335 0.743467 237.402 69.810693 -61.506111
3 -1.07636 0.807284 226.846 14.894564 -49.750827
5 0.223801 0.888624 192.095 15.071309 -49.751638
6 0.172056 0.808576 191.317 69.758349 -62.785692
7 -0.903793 0.730482 167.473 75.576196 -49.709253