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 .
Star–galaxy classification for bright sources 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 about the source .
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 .
This also makes it clear that it is 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 .