Probabilistic Catalogs for Crowded Stellar Fields

TL;DR

This paper introduces a Bayesian probabilistic method for creating stellar catalogs from crowded field images, effectively inferring the number of stars, their luminosities, and handling noise and overlaps.

Contribution

The novel hierarchical Bayesian approach improves accuracy in crowded stellar fields and estimates uncertainties, outperforming traditional methods like SExtractor.

Findings

Successfully recovers input parameters in simulated crowded fields

Provides more accurate faint star and source count estimates

Demonstrates computational feasibility on simulated data

Abstract

We present and implement a probabilistic (Bayesian) method for producing catalogs from images of stellar fields. The method is capable of inferring the number of sources N in the image and can also handle the challenges introduced by noise, overlapping sources, and an unknown point spread function (PSF). The luminosity function of the stars can also be inferred even when the precise luminosity of each star is uncertain, via the use of a hierarchical Bayesian model. The computational feasibility of the method is demonstrated on two simulated images with different numbers of stars. We find that our method successfully recovers the input parameter values along with principled uncertainties even when the field is crowded. We also compare our results with those obtained from the SExtractor software. While the two approaches largely agree about the fluxes of the bright stars, the Bayesian approach provides more accurate inferences about the faint stars and the number of stars, particularly in the crowded case.