Brendon J. Brewer, Daniel Foreman-Mackey, David W. Hogg
We introduce a probabilistic (Bayesian) method for producing catalogs from images of crowded stellar fields. The method is capable of inferring the number of sources (N) in the image and can also handle the challenges introduced by overlapping sources. The luminosity function of the stars can also be inferred even when the precise luminosity of each star is uncertain. This is in contrast with standard techniques which produce a single catalog, potentially underestimating the uncertainties in any study of the stellar population and discarding information about sources at or below the detection limit. The method is implemented using advanced Markov Chain Monte Carlo (MCMC) techniques including Reversible Jump and Nested Sampling. The computational feasibility of the method is demonstrated on simulated data where the luminosity function of the stars is a broken power-law. The parameters of the luminosity function can be recovered with moderate uncertainties. We compare the results obtained from our method with those obtained from the SExtractor software and find that the latter significantly underestimates the number of stars in the image and leads to incorrect inferences about the luminosity function of the stars.
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http://arxiv.org/abs/1211.5805
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