Probabilistic cross-identification of multiple catalogs in crowded fields

Xiaochen Shi, Tamas Budavari, and Amitabh Basu, Applied Mathematics and Statistics, Johns Hopkins University

Poster

Matching astronomical catalogs in crowded regions of the sky is challenging both statistically and computationally due to the many possible alternative associations. Budavári and Basu (2016) modeled the two-catalog situation as an Assignment Problem and used the well-known 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 based on the Hungarian algorithm. 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.