| 5 | 5 | The two metrics that are most likely to clarify whether a given stacking recipe is better than another are the average value of the .num image produced (the average number of inputs for a given pixel in the stack) and the S/N distribution of detected objects. For the initial test, I've run the rejection I've been developing (the standard recipe), along with the rejection with PSF.INPUT.THRESH set to 10.0 (the unrestricted recipe). The following image shows the number image on the bottom row, along with a zoom in of a section of the unconvolved image (with finding chart displayed). In the region of this zoom, the additional inputs that are not rejected at input (and have therefore survived the PSF matching phase as well) seem to help fill in gaps and correct some defects (dark spot, bottom middle of image; linear feature, middle right of image). Below this is a plot of the ratio of S/N values for all objects detected by psphot on these images as a function of y-position. As the change in depth is largely a function of y-position, this shows the difference. Note that at y < 1500pxl, the S/N ratio bifurcates, with the samples from areas of added inputs yielding a higher S/N (largely consistent with an improvement of 2 inputs: sqrt(8)/sqrt(10) ~ 0.9). The scatter in the S/N ratio increases at high y, correlating with the other region of increased depth. |
