Probabilistic Cross-Identification of Astronomical Sources

TL;DR

This paper introduces a Bayesian probabilistic framework for cross-matching astronomical sources across multiple observations, incorporating spatial and physical data, enabling more accurate identification in multi-wavelength and time-domain astronomy.

Contribution

It presents a novel, symmetric Bayesian formalism and an efficient recursive algorithm for source cross-identification that includes physical properties alongside positional data.

Findings

Provides a practical recursive algorithm for Bayesian cross-matching.

Enables integration of physical properties like colors and redshift.

Facilitates multi-wavelength and time-domain astronomical studies.

Abstract

We present a general probabilistic formalism for cross-identifying astronomical point sources in multiple observations. Our Bayesian approach, symmetric in all observations, is the foundation of a unified framework for object matching, where not only spatial information, but physical properties, such as colors, redshift and luminosity, can also be considered in a natural way. We provide a practical recipe to implement an efficient recursive algorithm to evaluate the Bayes factor over a set of catalogs with known circular errors in positions. This new methodology is crucial for studies leveraging the synergy of today's multi-wavelength observations and to enter the time-domain science of the upcoming survey telescopes.