Regions of Bad Photometry

See here for Bad Photometry Maps

-Eli Rykoff

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. The red circles are stars brighter than R<5 in the Yale Bright Star Catalog.

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 there's an extra blinking light. It should be possible to use the size of the streak to calculate the altitude of the airplane...


Scattered Light

Considerably more troublesome is the scattered light from bright stars. In particular, there is a large patch at RA~70, Dec~-63 that is completely compromised. In these region is R Doradus, a bright (R~5) Mira, which means it is very, very red which may be why it's causing us such fits:


Note that if you look at individual chips where the scaling is more appropriate (my DS9 fu fails me with the mosaics) then the scattered light contaminates basically the full image).

However, the scale of the problem is surprising. This is more than a degree away from the corner, and the scattered light may also be from R Doradus:


I'm not sure how to mitigate this going forward. At the moment, we may need to cut a large patch out of the corner of the SPTE region because the photometry isn't reliable.

Background Subtraction Issues

One more thing! As has been noted in Peter & Erin's visual inspection tool, sometimes SExtractor has trouble with the background model in the corner of chips ([[desdmug:Dark Corners]]). This may be more widespread than I (at least) thought was a problem. Here is the background mosaic for one of the satellite images:


I've included the cutout graphs as well. You can see that (a) three of the chips have bad background subtraction in the corner and (b) the background is somewhat concave rather than convex. Presumably this is because of flat field issues...

In addition, you can see the amplifier shift on CCD 31, which is presumably being fixed.

My only thought on the bad corner background model problem is that a "global" fit to the background wouldn't have this problem since we know the background should be roughly contiguous across chips. Though this may not be trivial to implement.

There are also small jumps from CCD to CCD in the background model. What is the cause of this?

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?

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