Background Model and Over and Under Subtraction on GPC1 Images

Bill identified an image from the CNP dataset which was generating large numbers of detections and taking an extremely long time to process. Checking into this, I discovered that there is a problem with the source detection, particularly in cases where the background model is unable to follow the structures in the background, and when the image quality is poor (large FWHM). At a suggestion from Michael Wood-Vasey, I have added an additional source filtering which appears to improve this situation.

Here are some images to illustrate the problem, and the improvements:

The upper-left image shows o5579g0223o.XY63 after detrending, but without background subtraction. The image has been somewhat smoothed to enhance the visibility of the background variations. Note that the variations in the background in this image are caused by clouds (and can be seen in earlier and later images from the same night). The background ranges from ~1000 counts (black) to ~1300 counts (light yellow).

The upper-right image shows the background model for this image. To the eye, it follows the background variations reasonable.

The lower-left image shows the background-subtracted image, again with some smoothing. The stretch is tighter than the upper 2 images: the dark black regions are at -10, the light red is at about +10. Clearly, the background model is not able to follow the somewhat higher-frequency structure in the image. Also note that it does not particularly help to make a finer-structure background model -- there are still errors, but on different scales.

The lower-right images shows the detected peaks from this background-subtracted image, after smoothing with the PSF. In this particular image, the PSF is large (FWHM ~ 11 pixels), which means the significance image is quite heavily smoothed by the detection process. Note that the high regions have low significance per pixel: the S/N per pixel is only in the vicinity of 0.5. In this case, it is the smoothing which enhances the background subtraction residuals.

Clearly, we are detecting too many objects in these regions. But, can we identify and filter them somehow. The common feature of these sources is that they originate in a region of low surface brightness structures. They do not have very PSF-like shapes. Perhaps we can remove them on that basis.

In the initial steps of the photometry analysis, we measure several aperture-like values: the Kron flux (an aperture flux measured within a circle 2.5 times the 1st radial moment of the source). I added a 'core' aperture flux, using a fixed aperture for all sources scaled to the PSF size. The figure below plots the log of the Core Flux S/N vs the log of the Kron Flux / Core Flux ratio. The uninteresting detections above may lie in 2 areas in this plot: they may have insignificant flux on the small scale of the PSF, or they may have a small flux in the core compared with the kron flux. The more PSF-like an object, the closer it will lie to the bottom of this figure.

I have selected 'good' and 'bad' objects using the above plot: good objects are required to have Core Flux S/N > 5.0 and Kron Flux / Core Flux < 10.0. This selects against features found on large, low-surface-brightness structures like those in the under-subtracted images above. The next few images illustrate the rejections resulting from these cuts.

The image above shows a portion of the CNP image from above. The red boxes mark all sources detected in the image; the blue boxes mark those sources which passed the above filter. It is clear that this cut rejects a large fraction of the detections coming from the poor background subtracted region. (It may be difficult to tell, but all bright stars are identified in the good sample).

The above 2 images show portions of another exposure, this time of MD04. Again, the red boxes mark all detections, while the blue boxes show the 'good' objects after filtering.