Work

Fast Catalog Matching for Improved Posteriors via Constrained Clustering

Year: 2025

Type: article

Abstract: We propose a novel approximate method for the probabilistic catalog matching problem that provides better solutions than previously used heuristics and scales well for large real-world applications. We also improve probabilistic catalog matching by including a simple but powerful prior and optimizing the posterior instead of just the likelihood as in previous formulations. Our new approach uses constrained clustering, specifically COP-KMeans, to provide near-optimal solutions in a fraction of the time of previous methods. We empirically demonstrate our constrained clustering’s efficacy through simulations and data from the Hubble Source Catalog.

Source: The Astronomical Journal

DOI: 10.3847/1538-3881/adba4d

Authors: Jacob Feitelberg, Tamás Budavári, Amitabh Basu

Language: English

Topic: Data Quality and Management

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $4,499

Funder: National Aeronautics and Space Administration

Awards: G3441197975, G58316358