Fadely handed me a draft manuscript which I expected to be about star–galaxy classification but ended up being about all the photometric measurements ever! He proposes that we can improve the photometry of an individual object in some band using all the observations we have about all other objects (and the object itself but in different bands). This would all be very model-dependent, but he proposes that we build a flexible model of the underlying distribution with hierarchical Bayes. We spent time today discussing what the underlying assumptions of such a project would be. He already has some impressive results that suggest that hierarchical inference is worth some huge amount of observing time: That is, the signal-to-noise ratios or precisions of individual object measurements rise when the photometry is improved by hierarchical modeling. Awesome!
Fadely and I also discussed with Vakili and Foreman-Mackey Vakili's project of inferring the spatially varying point-spread function in large survey data sets. He wants to do the inference by shifting the model and not shifting (interpolating or smoothing) the data. That's noble; we wrote the equations on the board. It looks a tiny bit daunting, but there are many precedents in the machine-learning literature (things like convolutional dictionary methods).
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