Fix Version/s: None
Sprint:AP F19-5 (October), AP F19-6 (November)
In connection to
DM-21702, it seems that the main reason of the exploding number of sources in the science convolution case is a lower variance level in the difference images.
- Check the operations that result this lower value.
- Check what operations are performed in the decorrelation step.
Gabor Kovacs [X] (Inactive) added a comment -
Could you please have a look at this ticket ? Beside the comments, there is a code for fixing the usage of the decorrelation afterburner; though it does not really rectify anything at the moment due to
DM-21868, the decorrelation afterburner itself needs fixing. Shall we merge?
Gabor Kovacs [X] (Inactive) added a comment - Could you please have a look at this ticket ? Beside the comments, there is a code for fixing the usage of the decorrelation afterburner; though it does not really rectify anything at the moment due to DM-21868 , the decorrelation afterburner itself needs fixing. Shall we merge?
Variance plane operations.
The relevant definition are in include/lsst/afw/image/Pixel.h:375 and Pixel.h:391 in variance_multiplies and variance_plus template structures. For multiplication of two pixels the variance is cross-weighted: x2 * var_y + y2 * var_x
The convolution kernel does not have any variance, but pixel variances are weighted by kernel^2^ values. In effect the variance plane is convolved by the square of the AL kernel solution. Assuming independent noise in the pixels, this is what expected. This means that convolution by a 10x10 pixel 1-sum kernel can modify the variance by a factor of 0.01 in the most extreme case.
The decorrelation afterburner
While swapping the two images in image differencing seems straightforward, regarding the variance plane of the difference image, the cases are asymmetric. If we convolve the negligible noise template, then the difference image inherits all the noise from the science image variance plane. If we convolve the science image, the variance in the science image decreases significantly due to the convolution. The difference image will have a lower level (and correlated) noise. It is then the task of the decorrelation afterburner to deconvolve and scale back the variance of the difference image. This functionality does not perform well at the moment.
We note that applying the fix in this ticket, the decorrelated images are still bad quality:
For follow up investigation,