Following on from above, I've re-run the $AP_PIPE_DIR/pipelines/DarkEnergyCamera/RunIsrForCrosstalkSources.yaml pipeline to regenerate crosstalk sources using the MEDIAN_PER_ROW fitType, and then regenerated flats using the $CP_PIPE_DIR/pipelines/DarkEnergyCamera/cpFlat.yaml pipeline.
Including the original (default) flats data I have available, I now have three flats outputs:
- isr:overscan.fitType='MEDIAN' AND cpFlatNorm:level='DETECTOR' (current default)
- isr:overscan.fitType='MEDIAN_PER_ROW' AND cpFlatNorm:level='DETECTOR'
- isr:overscan.fitType='MEDIAN_PER_ROW' AND cpFlatNorm:level='AMP'
The plots attached here show comparisons for a few selected DECam detectors:
I'm unsure which of these flats configurations look the healthiest, and would be very keen to hear from others who are more experienced here. From the bias testing above, it seems that MEDIAN_PER_ROW is optimal for DECam, but the impact of switching cpFlatNormalizationTask from per-detector to per-amp varying results, with a large variation as a function of detector.