self-calibration

While America voted, Fergus, Krishnan, Foreman-Mackey, and I discussed with Fadely his self-calibration results. He is trying to infer the calibration parameters (dark and flat for every pixel) in a detector without any calibration data, just using science data. The idea is to build a statistical model of the data and then find the calibration parameters that make that model most likely; when calibration is good, the data will be more informative or more compact in the calibrated data space (we hope). Right now performance is looking bad. I'm still optimistic, of course! Krishnan pointed out some issues, one of which is that generative models are not always highly discriminative and this project lives somewhere in the intersection of generative and discriminative. The distinction between generative and discriminative has rarely come up in astronomy. At the same meeting, we discussed Fergus's direct detection successes: He has spectra of some exoplanets! More soon, I hope.