Real-time gravitational-wave inference for binary neutron stars using machine learning

TL;DR

This paper introduces a machine learning framework capable of performing real-time, accurate inference of binary neutron star mergers from gravitational-wave data, significantly improving speed and precision over traditional methods.

Contribution

The authors develop a novel machine learning approach that achieves complete binary neutron star inference in one second without approximations, enhancing localization and parameter estimation for multi-messenger astronomy.

Findings

Achieves inference in one second without approximations.

Improves sky localization accuracy by ~30%.

Scales to signals up to an hour long.

Abstract

Mergers of binary neutron stars (BNSs) emit signals in both the gravitational-wave (GW) and electromagnetic (EM) spectra. Famously, the 2017 multi-messenger observation of GW170817 led to scientific discoveries across cosmology, nuclear physics, and gravity. Central to these results were the sky localization and distance obtained from GW data, which, in the case of GW170817, helped to identify the associated EM transient, AT 2017gfo, 11 hours after the GW signal. Fast analysis of GW data is critical for directing time-sensitive EM observations; however, due to challenges arising from the length and complexity of signals, it is often necessary to make approximations that sacrifice accuracy. Here, we present a machine learning framework that performs complete BNS inference in just one second without making any such approximations. Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $\sim30\%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses, which can be used to prioritize expensive telescope time. Additionally, the flexibility and reduced cost of our method open new opportunities for equation-of-state studies. Finally, we demonstrate that our method scales to extremely long signals, up to an hour in length, thus serving as a blueprint for data analysis for next-generation ground- and space-based detectors.