A Neural Network for Estimating Compact Binary Coalescence Parameters of Gravitational-wave Events in Real Time

Abstract

Low-latency pipelines analyzing gravitational waves from compact binary coalescence events rely on matched filter techniques. Limitations in template banks and waveform modeling, as well as non-stationary detector noise cause errors in signal parameter recovery, especially for events with high chirp masses. We present a quantile regression neural network (NN) model that provides dynamic bounds on key parameters such as chirp mass, mass ratio, and total mass. We test the model on various synthetic datasets and real events from the LIGO-Virgo-KAGRA gravitational-wave transient GTWC-3 catalog. We find that the model accuracy is consistently over 90% across all the datasets. We explore the possibility of employing the NN bounds as priors in online parameter estimation (PE). We find that they reduce by 9% the number of likelihood evaluations. This approach may shorten PE run times without affecting sky localizations.

Department(s)

Physics

Comments

Istituto Nazionale di Fisica Nucleare, Grant 25384/2023

Keywords and Phrases

compact binary coalescence; gravitational waves; low-latency inference; machine learning

International Standard Serial Number (ISSN)

1361-6382; 0264-9381

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2025 IOP Publishing, All rights reserved.

Publication Date

19 Dec 2025

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