Tsalmantza and I implemented a Gaussian-processes-like prior on her spectral model, to enforce smoothness at wavelengths where there are very few data. We looked at the results today and they are very nice. It works great, costs almost no time per iteration, and doesn't slow down convergence. This is great for a bunch of projects we are working on.
Late in the day I discussed image modeling with Lang; we have a (somewhat) practical proposal to replace the SDSS imaging catalog with something that is optimal (under some assumptions) in the pixel space. This should improve the catalog at the faint end and in regions where the data have issues.