large galaxies, cosmic rays

It comes as a surprise to many that it is much harder to precisely measure the properties of very bright, nearby galaxies than it is to measure the properties of much more distant but similar objects! (Same for very bright stars too, in modern digital imaging.) Part of this is because at high signal-to-noise you see the (badly modeled) details of your point-spread function better. But the bigger issues are that nearby galaxies span field boundaries (in any blind survey, like SDSS), span flat-field and sky variations (because of their large angular sizes), and tend to be blended with background galaxies and foreground stars. Mykytyn, Foreman-Mackey, and I discussed all these issues over a long post-lunch meeting.

In the early morning, Andrew Flockhart (NYU), Fergus, and I discussed our project to use supervised classification machine-learning techniques to identify the cosmic rays robustly in single-epoch, single-exposure HST imaging. We decided to start with nearest-neighbor techniques and move to support vector machines, before going to any heavy machinery. We built our training set with multi-exposure imaging from the HST Archive.