A Bayesian approach to star-galaxy classification

Star-galaxy classification is one of the most fundamental data-processing tasks in survey astronomy, and a critical starting point for the scientific exploitation of survey data. For bright sources this classification can be done with almost complete reliability, but for the numerous sources close to a survey’s detection limit each image encodes only limited morphological information. In this regime, from which many of the new scientific discoveries are likely to come, it is vital to utilise all the available information about a source, both from multiple measurements and also prior knowledge about the star and galaxy populations. It is also more useful and realistic to provide classification probabilities than decisive classifications. All these desiderata can be met by adopting a Bayesian approach to star-galaxy classification, and we develop a very general formalism for doing so. An immediate implication of applying Bayes’s theorem to this problem is that it is formally impossible to combine morphological measurements in different bands without using colour information as well; however we develop several approximations that disregard colour information as much as possible. The resultant scheme is applied to data from the UKIRT Infrared Deep Sky Survey (UKIDSS), and tested by comparing the results to deep Sloan Digital Sky Survey (SDSS) Stripe 82 measurements of the same sources. The Bayesian classification probabilities obtained from the UKIDSS data agree well with the deep SDSS classifications both overall (a mismatch rate of 0.022, compared to 0.044 for the UKIDSS pipeline classifier) and close to the UKIDSS detection limit (a mismatch rate of 0.068 compared to 0.075 for the UKIDSS pipeline classifier). The Bayesian formalism developed here can be applied to improve the reliability of any star-galaxy classification schemes based on the measured values of morphology statistics alone.

💡 Research Summary

The paper presents a comprehensive Bayesian framework for star‑galaxy classification, addressing the fundamental challenge of reliably separating point‑like stars from extended galaxies in large astronomical surveys, especially near the detection limit where morphological information is scarce. Traditional classifiers rely on deterministic thresholds applied to shape parameters (e.g., half‑light radius, concentration, asymmetry) and work well for bright objects but degrade sharply for faint sources. The authors argue that a probabilistic approach, which explicitly incorporates both measured morphology and prior knowledge of the underlying stellar and galactic populations, is essential for modern deep surveys.

The core of the methodology is the direct application of Bayes’ theorem:

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