modeling the garbage

In our transit search in the GALEX data, we turn up large numbers of false positives. Some of these are other kinds of stellar variability and some are various kinds of detector or spacecraft issues. Normally we would cut these with some kind of heuristic filtering (as in look at this one; it doesn't look right), but Lang and I have been trying to filter them by creating a quantitative hypothesis or model for each one, and then doing likelihood (or posterior probability) ratios to rule them out more objectively or quantitatively. It has been working well; in fact with the sample as large as it is, it is faster to write the relevant code than it is to make all the relevant judgements. It doesn't remove judgement, of course, because which models we make depends on which artifacts we see or anticipate.

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