Probabilistic record linkage in astronomy: Directional cross-identification and beyond

TL;DR

This paper reviews probabilistic Bayesian methods for cross-identifying astronomical objects across surveys, addressing challenges of measurement uncertainty, astrophysical effects, and selection biases to improve multi-wavelength and multi-messenger astronomy.

Contribution

It introduces hierarchical Bayesian models for astronomical record linkage, integrating astrophysical factors and measurement errors, and surveys recent advances and open research areas.

Findings

Bayesian probabilities improve cross-identification accuracy.

Hierarchical models incorporate astrophysical effects and measurement uncertainties.

Open research areas include handling complex source properties and large datasets.

Abstract

Modern astronomy increasingly relies upon systematic surveys, whose dedicated telescopes continuously observe the sky across varied wavelength ranges of the electromagnetic spectrum; some surveys also observe non-electromagnetic "messengers," such as high-energy particles or gravitational waves. Stars and galaxies look different through the eyes of different instruments, and their independent measurements have to be carefully combined to provide a complete, sound picture of the multicolor and eventful universe. The association of an object's independent detections is, however, a difficult problem scientifically, computationally, and statistically, raising varied challenges across diverse astronomical applications. The fundamental problem is finding records in survey databases with directions that match to within the direction uncertainties. Such astronomical versions of the record linkage problem are known by various terms in astronomy: cross-matching, cross-identification, and directional, positional, or spatio-temporal coincidence assessment. Astronomers have developed several statistical approaches for such problems, largely independently of related developments in other disciplines. Here we review emerging approaches that compute (Bayesian) probabilities for the hypotheses of interest: possible associations, or demographic properties of a cosmic population that depend on identifying associations. Many cross-identification tasks can be formulated within a hierarchical Bayesian partition model framework, with components that explicitly account for astrophysical effects (e.g., source brightness vs. wavelength, source motion, or source extent), selection effects, and measurement error. We survey recent developments, and highlight important open areas for future research.