Globally optimal and scalable $N$-way matching of astronomy catalogs

TL;DR

This paper presents a new probabilistic method for matching astronomical catalogs that is both statistically optimal and computationally scalable, especially effective in crowded fields for large surveys like LSST.

Contribution

A novel formulation of probabilistic cross-identification that scales better than previous methods, enabling globally optimal matching of multiple catalogs in crowded astronomical fields.

Findings

Method outperforms existing approaches in crowded fields

Scales efficiently with multiple catalogs

Provides a publicly available software tool

Abstract

Building on previous Bayesian approaches, we introduce a novel formulation of probabilistic cross-identification, where detections are directly associated to (hypothesized) astronomical objects in a globally optimal way. We show that this new method scales better for processing multiple catalogs than enumerating all possible candidates, especially in the limit of crowded fields, which is the most challenging observational regime for new-generation astronomy experiments such as the Rubin Observatory Legacy Survey of Space and Time (LSST). Here we study simulated catalogs where the ground-truth is known and report on the statistical and computational performance of the method. The paper is accompanied by a public software tool to perform globally optimal catalog matching based on directional data.