How to handle filters with close but different transmission in coadd

Data Management
#1

After an accident the Megacam i filter has been replaced by a similar filter but with a slightly different transmission curve. I know that the photometric calibration is able to handle this using different color terms, but I am wondering how to proceed for coaddition of images with the old and the new filter ? Is there a mechanism to correct the flux of one set of images in order to get coherent magnitudes for all the stacked images ? Or should we handle both set of images separately ?

I guess that this kind of problem may be relevant for LSST is we have to replace a filter or even if the transmission evolves with time.

#2

HSC is facing the same issue, with two i-band filters. For our most recent production run, we decided to simply throw everything in together, combining all data taken with an i-band filter regardless of which generation it was. Certainly this is not optimal, but the result isn’t too bad — we haven’t identified any problems so far. Actually, we were kinda already combining data taken with subtly different i-band filters because the filter curve varies over the focal plane, so combining images at the center of the field and at the edge of the field has a similar effect.

I believe @rhl and @jbosch have plans to track not just the filter of each observation but the transmission curve as a function of position as well. I’m not sure what can be done to correct images to a common bandpass given that it’s not just a scaling but a color-dependent scaling that needs to be applied, but perhaps techniques being developed for image subtraction in the presence of DCR could find utilisation here. However, I think that coadds sweep enough details under the rug (PSF, clipping) that the filter issue isn’t such a big problem. Coadds are principally intended for detection and rough estimation of quantities for faint objects. If precision measurements are required, you’re better off looking at the individual exposures (e.g., multifit), which can account for the different bandpasses given a model SED.

#3

I want to track the spatial and temporal variation of the filters so that we have the option to use the information (two different i filters is a variant of temporal variation). As Paul says, in multifit using this filter information is pretty straightforward, as it is in single-visit processing and forced photometry, and this implies that we’ll be able to handle jointcal correctly too. For coadds I’m not sure what the best course is. We can handle the chromatic PSFs by including the filter band passes in the coaddPsf (if it matters). If we do per-object coadds (analogous to coadd PSF) then we can allow for the band passes given the SED – but that only works if we run the deblender first and neglect colour gradients.