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Probabilistic Catalogs for Crowded Stellar Fields | | Brendon J. Brewer
; Daniel Foreman-Mackey
; David W. Hogg
; | | Date: |
25 Nov 2012 | | Abstract: | 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. | | Source: | arXiv, 1211.5805 | | Services: | Forum | Review | PDF | Favorites | |
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