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GWSkyNet-Multi: A Machine-learning Multiclass Classifier for LIGO–Virgo Public Alerts

Abbott, Thomas C. and Buffaz, Eitan and Vieira, Nicholas and Cabero, Miriam and Haggard, Daryl and Mahabal, Ashish and McIver, Jess (2022) GWSkyNet-Multi: A Machine-learning Multiclass Classifier for LIGO–Virgo Public Alerts. Astrophysical Journal, 927 (2). Art. No. 232. ISSN 0004-637X. doi:10.3847/1538-4357/ac5019.

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Compact object mergers which produce both detectable gravitational waves and electromagnetic (EM) emission can provide valuable insights into the neutron star equation of state, the tension in the Hubble constant, and the origin of the r-process elements. However, EM follow-up of gravitational wave sources is complicated by false-positive detections, and the transient nature of the associated EM emission. GWSkyNet-Multi is a machine learning model that attempts facilitate EM follow-up by providing real-time predictions of the source of a gravitational wave detection. The model uses information from Open Public Alerts (OPAs) released by LIGO–Virgo within minutes of a gravitational wave detection. GWSkyNet was introduced in Cabero et al. as a binary classifier and uses the OPA skymaps to classify sources as either astrophysical or as glitches. In this paper, we introduce GWSkyNet-Multi, an extension of GWSkyNet which further distinguishes sources as binary black hole mergers, mergers involving a neutron star, or non-astrophysical glitches. GWSkyNet-Multi is a sequence of three one-versus-all classifiers trained using a class-balanced and physically motivated source mass distribution. Training on this data set, we obtain test set accuracies of 93.7% for binary black hole-versus-all, 94.4% for neutron star-versus-all, and 95.1% for glitch-versus-all. We obtain an overall accuracy of 93.4% using a hierarchical classification scheme. Furthermore, we correctly identify 36 of the 40 gravitational wave detections from the first half of LIGO–Virgo’s third observing run (O3a) and present predictions for O3b sources. As gravitational wave detections increase in number and frequency, GWSkyNet-Multi will be a powerful tool for prioritizing successful EM follow-up.

Item Type:Article
Related URLs:
URLURL TypeDescription Paper
Abbott, Thomas C.0000-0001-5002-0868
Buffaz, Eitan0000-0003-2205-2912
Vieira, Nicholas0000-0001-7815-7604
Cabero, Miriam0000-0003-4059-4512
Haggard, Daryl0000-0001-6803-2138
Mahabal, Ashish0000-0003-2242-0244
McIver, Jess0000-0003-0316-1355
Alternate Title:GWSkyNet-Multi: A Machine Learning Multi-Class Classifier for LIGO-Virgo Public Alerts
Additional Information:© 2022. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Received 2021 November 7; revised 2022 January 19; accepted 2022 January 24; published 2022 March 21. The authors acknowledge funding from the New Frontiers in Research Fund Exploration program. N.V. and D.H. acknowledge funding from the Bob Wares Science Innovation Prospectors Fund. D.H. acknowledges support from the Canada Research Chairs (CRC) program and the NSERC Discovery Grant program. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center (, a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max Planck Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. Software: astropy 9 : Astropy Collaboration et al. (2018); BAYESTAR 10 : Singer & Price (2016); ligo.skymap 11 ; scikit-learn 12 ; TensorFlow. 13
Funding AgencyGrant Number
New Frontiers in Research Fund ExplorationUNSPECIFIED
Bob Wares Science Innovation Prospectors FundUNSPECIFIED
Canada Research Chairs ProgramUNSPECIFIED
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Science and Technology Facilities Council (STFC)UNSPECIFIED
Max Planck SocietyUNSPECIFIED
State of Niedersachsen/GermanyUNSPECIFIED
Australian Research CouncilUNSPECIFIED
European Gravitational ObservatoryUNSPECIFIED
Centre National de la Recherche Scientifique (CNRS)UNSPECIFIED
Istituto Nazionale di Fisica Nucleare (INFN)UNSPECIFIED
Subject Keywords:Gravitational wave astronomy; Gravitational wave sources
Issue or Number:2
Classification Code:Unified Astronomy Thesaurus concepts: Gravitational wave astronomy (675); Gravitational wave sources (677)
Record Number:CaltechAUTHORS:20220413-369906800
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Official Citation:Thomas C. Abbott et al 2022 ApJ 927 232
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:114278
Deposited By: Tony Diaz
Deposited On:13 Apr 2022 17:37
Last Modified:13 Apr 2022 17:37

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