AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity

2026-07-01

AI Summary

Real-time detection of binary neutron star gravitational-wave signals faces significant challenges due to high computational costs and latency. This work proposes an AI-based search method leveraging deep neural networks, combined with heterodyned data preprocessing and an inference-as-a-service architecture. The approach achieves sensitivity comparable to traditional matched filtering while substantially reducing computational resource requirements. It represents the first demonstration of real-time binary neutron star detection using AI that matches the performance of classical algorithms, operating online with only a single non-flagship GPU. Furthermore, the framework supports distributed GPU pools for efficient offline follow-up analyses and has been successfully deployed during the fourth observing run of the LIGO–Virgo–KAGRA collaboration.

Abstract

Gravitational Waves (GWs) represent the newest window of astronomy, furthering our understanding of compact objects like black holes and neutron stars in the Universe. The signal from two merging neutron stars is especially interesting since it brings the prospect of concordant electromagnetic and neutrino emissions. Such multi-messenger observations have a transformational impact on fundamental physics, nuclear matter, astrophysics, and gravity. It was first witnessed in 2017 with the detection of the binary neutron star (BNS) merger GW170817. However, searching for BNS signals in real-time in the LIGO-Virgo-KAGRA (LVK) GW detectors presents a computational challenge, as the data streaming out must be matched against $\sim$ million reference waveforms, which requires up to a thousand CPU cores. We present a different approach using neural networks to learn the presence of a signal in the data. Our algorithm, called Aframe, was deployed in the LVK's fourth observing run and was the first artificial intelligence (AI)-enabled search to detect multiple binary black holes (BBHs) live. In this work, we demonstrate that the approach extends to the lower-mass BNS regime, and is the first AI-enabled search that achieves sensitivity comparable to matched-filter pipelines at lower computational and latency costs. The challenge of the longer-duration BNS signals is addressed by heterodyning the data, following which the network architecture used for BBHs is sufficient to distinguish signal versus background. We also show that this analysis requires a single non-flagship GPU for online deployment. Furthermore, the design and adoption of inference-as-a-service tools allow rapid offline analysis using a distributed pool of GPU resources. Hence, aside from the use case of rapid online data analysis, we also establish the use of Aframe for efficient archival data analysis.

Problem

• gravitational waves
• binary neutron stars
• real-time search
• computational challenge
• matched filtering

Innovation

• AI-enabled gravitational-wave search
• binary neutron stars
• neural networks
• heterodyning
• inference-as-a-service