Martin Kuemmel

I did a fairly complete parameter study and changed, from a baseline solution, all conceivable parameters that could have an effect on the fitting (can provide a protocol if desired)....

I made a small script to derive the reduced chi-square value from output imaging material (segmentation image, residual image, rms image). When I compare the reduced chi-2 values from the...

Turns out that the fit are kind of reasonable:  when the chi-2 scale is changed to: `set_modified_chi_squared_scale(0.003)` With this settings a disk+bulge fit works reasonably well with Gaussian PSF,...

As discussed yesterday I added Poisson noise to the RMS and ran SE++ with the PSFEX model (with the default mod_scale). The results are a bit better, but not really...

Lastly, I checked whether providing a gain value does help. I do that by giving explicitly the value ` --detection-image-gain 3.5 --` and `--weight-type background` in both the ASCII and...

Happy to check that out if I know exactly what and how. The image indeed has an effective exptime of 1s. There are 4 exposure with 565s each, that would...

Running with the very large gain value is so far the only reduction with a reasonable result for the photometry and without the modified chi-2 scaling:  Actually the result...

With this formula: > total_noise_rms = numpy.sqrt(numpy.fabs(`img_data`) / `gain` + background_rms^2) the noise gets very large and the 'old' detection parameters are kind of obsolete. Shouldn't there be the exposure...

I was using the above formula(s) with: (effective) gain = gain * exposure time = 3.48 * 4 * 565. The gain comes from the calibrated images and the exposure...

Until the - you do have the fitting parameters, so it should be possible to generate them from those.