Bayesian Classification of Astronomical Objects -- and what is behind it

TL;DR

This paper introduces a Bayesian method for automatically identifying and classifying astronomical objects across catalogs, effectively handling exceptions and integrating tasks for reliable catalog generation.

Contribution

It presents a novel Bayesian framework that combines object identification and classification, including a practical solution for handling exceptions in astronomical data.

Findings

Efficient Bayesian method for catalog classification and object association

Effective handling of exceptions using the evidence term in Bayes' theorem

Links between Bayesian classification and philosophical concepts of science

Abstract

We present a Bayesian method for the identification and classification of objects from sets of astronomical catalogs, given a predefined classification scheme. Identification refers here to the association of entries in different catalogs to a single object, and classification refers to the matching of the associated data set to a model selected from a set of parametrized models of different complexity. By the virtue of Bayes' theorem, we can combine both tasks in an efficient way, which allows a largely automated and still reliable way to generate classified astronomical catalogs. A problem to the Bayesian approach is hereby the handling of exceptions, for which no likelihoods can be specified. We present and discuss a simple and practical solution to this problem, emphasizing the role of the "evidence" term in Bayes' theorem for the identification of exceptions. Comparing the practice and logic of Bayesian classification to Bayesian inference, we finally note some interesting links to concepts of the philosophy of science.