I tried to come up with a reasonable set of cuts based on the results from the HSC S16A visit data. I use the following quantities derived from the second moment quantities:

- e1 = (Ixx-Iyy)/(Ixx+Iyy)
- e2 = 2*Ixy/(Ixx+Iyy)
- size = determinant radius

I have also rejected ccd 9 from this analysis because of the known problems there that cause many visits to fail.

For each CCD I looked at the residuals of the two ellipticity components and the size. Here is the median of residuals for each CCD.

Here is a plot of the scatter (as defined by the median absolute deviation scaled to be a Gaussian.) for each of these quantities.

Finally, I plot the fraction of objects with values greater than 5*scatter from the median.

Given these plots I chose a conservative set of cuts to remove outliers:

- median ellipticity residual < 0.015 (equivalent to ~10sigma of the distribution)
- median size residual < 0.008 (equivalent to ~10sigma of the distribution)
- size scatter < 0.03
- ellipticity scatter < 0.065
- size outlier fraction < 0.065
- ellipticity outlier fraction < 0.1

With these cuts I remove 6808 ccds from 971 different visits which corresponds to ~5% of the data. Here is the number of ccds removed from each visit.

You can see that most visits only have a few ccds with poorly modeled data. I also noticed that a number of ccds from the visits we rejected in S16A don't have any problems.

Here are the number of rejected visits for each ccd which show fairly uniform coverage except for the outer ccds:

We could also try to be more aggressive to exclude more objects as these are cuts are quite conservative.

I tried to come up with a reasonable set of cuts based on the results from the HSC S16A visit data. I use the following quantities derived from the second moment quantities:

I have also rejected ccd 9 from this analysis because of the known problems there that cause many visits to fail.

For each CCD I looked at the residuals of the two ellipticity components and the size. Here is the median of residuals for each CCD.

Here is a plot of the scatter (as defined by the median absolute deviation scaled to be a Gaussian.) for each of these quantities.

Finally, I plot the fraction of objects with values greater than 5*scatter from the median.

Given these plots I chose a conservative set of cuts to remove outliers:

With these cuts I remove 6808 ccds from 971 different visits which corresponds to ~5% of the data. Here is the number of ccds removed from each visit.

You can see that most visits only have a few ccds with poorly modeled data. I also noticed that a number of ccds from the visits we rejected in S16A don't have any problems.

Here are the number of rejected visits for each ccd which show fairly uniform coverage except for the outer ccds:

We could also try to be more aggressive to exclude more objects as these are cuts are quite conservative.