MJ Vakili (NYU) showed me today what he has been working on to generate a data-driven prior probability distribution over galaxies. It is great work. He finds that he can do a shapelet decomposition, truncate it, and then do a dimensionality reduction (again, as it were), and then fit the resulting distribution of components with a mixture of Gaussians. We have yet to show that the model is good, but when he samples from it, the samples look like actual galaxies. The point is this: If you want to measure a shear map (or anything else, for that matter) from galaxy images, you can't do proper inference if you don't have a prior over galaxy images. So we are playing around with the possibility of making one.
In the brown-bag today, Craig Lage (NYU) showed detailed simulations he is doing of the "Bullet Cluster". He is doing by-hand likelihood optimization, with an hours-long simulation inside the loop! But the results are gorgeous: He can reproduce all the large-scale features, and a lot of the small-scale details. He says it isn't a challenge to CDM, but it is a challenge to theories in which there is no dark matter. One of his goals is to test dark-matter interactions; it looks very promising for that.