Multimodal analysis of Gravitational Wave signals and Gamma-Ray Bursts from binary neutron star mergers

TL;DR

This paper proposes a multimodal machine learning framework to analyze and interpret gravitational wave signals and gamma-ray bursts from binary neutron star mergers, enhancing multi-messenger astronomy capabilities.

Contribution

It introduces a novel multimodal machine learning approach for simultaneous analysis of gravitational wave and electromagnetic data from neutron star mergers.

Findings

Framework enables real-time multi-messenger event analysis

Improves event characterization accuracy

Facilitates comprehensive understanding of cosmic phenomena

Abstract

A major boost in the understanding of the universe was given by the revelation of the first coalescence event of two neutron stars (GW170817) and the observation of the same event across the entire electromagnetic spectrum. With 3rd Generation gravitational wave detectors and the new astronomical facilities, we expect many multi messenger events of the same type. We anticipate the need to analyse the data provided to us by such events, to fulfill the requirements of real-time analysis, but also in order to decipher the event in its entirety through the information emitted in the different messengers using Machine Learning. We propose a change in the paradigm in the way we will do multi-messenger astronomy, using simultaneously the complete information generated by violent phenomena in the Universe. What we propose is the application of a multimodal machine learning approach to characterize these events.