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Census of the Local Universe (CLU) II: Identifying Nearby Galaxies Using Machine Learning

Presentation #326.05D in the session Gravitational Wave Cosmology and Methodologies.

Published onJun 29, 2022
Census of the Local Universe (CLU) II: Identifying Nearby Galaxies Using Machine Learning

A complete catalog of galaxies in the local universe is critical for efficient electromagnetic follow-up of gravitational wave events (EMGW), and the same catalog can be used for a range of other projects focused on uniform populations of nearby galaxies and/or identification of emission-line sources. The Census of the Local Universe (CLU; Cook et al. 2019) aims to provide a galaxy catalog out to ~200 Mpc that is as complete as possible. The original CLU implemented a narrow-band Hα survey of ~3π of the sky covering out to redshifts of 0.05 with the goal of cataloging those galaxies that are likely hosts of EMGW events. Here we present the tool we developed to extend CLU to a wider wavelength regime with machine learning (ML) techniques. We show that our ML tool recovers more galaxies (~67%-93%) in the local volume compared to selections based on Hα colors alone (~22%-53%) at same false alarm rate. When compared to the Galaxy List for the Advanced Detector Era (GLADE; Dálya et al. 2018, 2021) catalog, our CLU-ML catalog is able to achieve higher completeness in the nearby universe with appropriate classification threshold. The CLU-ML catalog will serve as an important tool in the EM follow-up campaigns during the upcoming LIGO-Virgo-KAGRA observing runs.

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