Probabilistic Cross-Identification in Crowded Fields as an Assignment Problem
Instrumentation and Methods for Astrophysics
2016-09-21 v1 Optimization and Control
Abstract
One of the outstanding challenges of cross-identification is multiplicity: detections in crowded regions of the sky are often linked to more than one candidate associations of similar likelihoods. We map the resulting maximum likelihood partitioning to the fundamental assignment problem of discrete mathematics and efficiently solve the two-way catalog-level matching in the realm of combinatorial optimization using the so-called Hungarian algorithm. We introduce the method, demonstrate its performance in a mock universe where the true associations are known, and discuss the applicability of the new procedure to large surveys.
Keywords
Cite
@article{arxiv.1609.03065,
title = {Probabilistic Cross-Identification in Crowded Fields as an Assignment Problem},
author = {Tamas Budavari and Amitabh Basu},
journal= {arXiv preprint arXiv:1609.03065},
year = {2016}
}
Comments
6 pages, 4 figures, accepted for publication in the Astronomical Journal