Bayesian Real-Time Classification of Multi-Messenger Electromagnetic and Gravitational-Wave Observations

Abstract

Because of the Electromagnetic (EM) Radiation Produced during the Merger, Compact Binary Coalescences with Neutron Stars May Result in Multi-Messenger Observations. in Order to Follow Up on the Gravitational-Wave (GW) Signal with EM Telescopes, It is Critical to Promptly Identify the Properties of These Sources. This Identification Must Rely on the Properties of the Progenitor Source, Such as the Component Masses and Spins, as Determined by Low-Latency Detection Pipelines in Real Time. the Output of These Pipelines, However, Might Be Biased, Which Could Decrease the Accuracy of Parameter Recovery. Machine Learning Algorithms Are Used to Correct This Bias. in This Work, We Revisit This Problem and Discuss Two New Implementations of Supervised Machine Learning Algorithms, K-Nearest Neighbors and Random Forest, Which Are Able to Predict the Presence of a Neutron Star and Post-Merger Matter Remnant in Low-Latency Compact Binary Coalescence Searches Across Different Search Pipelines and Data Sets. Additionally, We Present a Novel Approach for Calculating the Bayesian Probabilities for These Two Metrics. Instead of Metric Scores Derived from Binary Machine Learning Classifiers, Our Scheme is Designed to Provide the Astronomy Community Well-Defined Probabilities. This Would Deliver a More Direct and Easily Interpretable Product to Assist EM Telescopes in Deciding Whether to Follow Up on GW Events in Real Time.

Department(s)

Physics

Comments

Mississippi Space Grant Consortium, Grant DMS-1925919

Keywords and Phrases

compact binary coalescences; gravitational waves; LIGO; low latency; machine learning; Virgo

International Standard Serial Number (ISSN)

1361-6382; 0264-9381

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 IOP Publishing, All rights reserved.

Publication Date

18 Apr 2024

Link to Full Text

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