likelihood functions for imaging

Mario Juric (UW) showed up for the day and we spoke for hours about many things. One category was image likelihood functions (for things like The Tractor or weak lensing). We came up with a very dumb (read: good!) idea for testing out ideas around likelihood functions: Take two image data sets from different telescopes that overlap on the sky. Build a catalog of sources (with positions and colors and so on) from a joint analysis of both data sets. Then do the same, but in a world in which your only interface to each data set is a callable API to a likelihood function! That is, something that takes as input a parameterized high-resolution image model and returns a likelihood value, given what it knows about its data and calibration, PSF, and so on. This would force us to figure out what would be needed in such an API. I think we would learn a lot, and it would help us think about how to construct next-next-generation data products. We also talked about image differencing, Dun Wang's Causal Pixel Model, and other matters of mutual interest.

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