probabilistic self-calibration

Fadely and I got together a simpler formulation of Fadely, Fergus, and my self-calibration project. I think we have something that makes sense but also might be computable in practice. I realized that there are two different kinds of self-calibration: The first category contains methods (like "grid tests") in which you know the identities of the sources in the images and you are checking that the measurements of those sources in properly calibrated images do not depend on detector position. The second category contains methods (like the "super-flat") in which you don't know what the pointing is of any image, but you expect that the statistics of properly calibrated detector pixels (in the long run) will all be identical. We are working on generalizations of the second type, which I am calling "probabilistic" self-calibration.

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