Probabilistic Cross-identification of Multiple Catalogs in Crowded Fields

TL;DR

This paper introduces an integer linear programming approach for cross-identifying multiple astronomical catalogs in crowded sky regions, improving upon previous methods and analyzing scalability for large surveys.

Contribution

The paper presents a novel integer linear programming method for multi-catalog cross-identification, extending previous two-catalog solutions to multiple catalogs with scalability analysis.

Findings

Effective on two-catalog problems, matching previous results.

Successfully applied to three-catalog problems.

Discusses scalability for large astronomical surveys.

Abstract

Matching astronomical catalogs in crowded regions of the sky is challenging both statistically and computationally due to the many possible alternative associations. Budav\'ari and Basu (2016) modeled the two-catalog situation as an Assignment Problem and used the famous Hungarian algorithm to solve it. Here we treat cross-identification of multiple catalogs by introducing a different approach based on integer linear programming. We first test this new method on problems with two catalogs and compare with the previous results. We then test the efficacy of the new approach on problems with three catalogs. The performance and scalability of the new approach is discussed in the context of large surveys.