A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences - state-of-the-art low-latency pipeline

Aug 5, 2026

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The promise of multi-messenger astronomy depends on rapidly detecting gravitational waves with very low latencies (about 1 second) to maximize follow-up observing time. Neural networks have shown robust non-linear modeling and millisecond-scale inference, yet integrating them into gravitational-wave research workflows remains challenging due to deployment hurdles and the lack of standardized, astrophysically meaningful sensitivity probes under non-stationary noise. This work presents a low-latency machine-learning pipeline for compact binary coalescences, demonstrating reduced latency versus matched filtering while reaching state-of-the-art sensitivity for higher-mass stellar binary black holes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Multi-messenger astronomy and low-latency requirements

Machine learning maturity and integration challenges

Deep learning approach (Aframe) and motivation

Aframe algorithm overview

Performance metric

Datasets, training, and evaluation

Model longevity

Latency and computational requirements

Comparison with existing pipelines

arXiv :2403 . 1866 1v2 [gr-qc] 4 Sep 2025 A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences Ethan Marx, 1, 2 William Benoit,3 Alec Gunny, 1, 2 Rafia Omer,3 Deep Chatterjee, 1, 2 Ricco C. Venterea,3, 4 Lauren Wills,3 Muhammed Saleem,5, 3 Eric Moreno, 1, 2 Ryan Raikman, 1, 6 Ekaterina Govorkova, 1, 2 Malina Desai, 1, 2 Jeffrey Krupa, 1 Dylan Rankin,7 Michael W. Coughlin,3 Philip Harris, 1 and Erik Katsavounidis 1, 2 1 Department of Physics, MIT, Cambridge, MA 02139, USA 2 LIGO Laboratory, 185 Albany St, MIT, Cambridge, MA 02139, USA 3 School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA 4 Department of Astronomy, Cornell University, Ithaca, NY, 14853, USA 5 Physics Department, University of Texas at Austin, Austin TX 78712 USA 6 Department of Physics, Carnegie Mellon University, Pittsburgh, PA, 15213 7 Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA (Dated: September 5, 2025) The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies (O(1s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference at a comparatively small computational footprint, making them an attractive family of algorithms in this context. However, integration of these algorithms into the gravitational-wave astrophysics research ecosystem has proven non-trivial. Here, we present a machine learning-based pipeline for the detection of gravitational waves from compact binary coalescences (CBCs) designed to run in low-latency. We demonstrate this pipeline to have a fraction of the latency of traditional matched filtering search pipelines while achieving state-of-the-art sensitivity to higher-mass stellar binary black holes. I. INTRODUCTION Gravitational-wave astronomy has developed rapidly since the first direct detection of gravitational waves from a binary black hole merger in 2015[1], with new detections now a common occurrence[2] . With the fourth observing run (O4) of the LIGO-Virgo-KAGRA (LVK) collaboration [3–5] already underway, and with future ground and space based detectors planned for various points in the next decade[6–8], ever more frequent discoveries of gravitational waves will enable follow-up observation of events across other cosmic messengers such as electromagnetic radiation and astrophysical neutrinos[9– 14] . The insights we gain in this era of multi-messenger astrophysics will directly correlate with the volume and diversity of data we are able to collect. Already, the first multi-messenger event, which included the gravitational wave event GW170817 [15], the gamma ray burst GRB170817A [16–19], and the kilonova AT2017gfo [20– 22] has led to a gold mine of new science. While machine learning (ML) is ubiquitous in some areas of physics[23], it has only recently approached a stage of maturity in the gravitational-wave community. To date, there have been a number of machine learning models proposed for the detection of compact binary coalescences (CBCs); e.g.,[24–28]; but there are none currently running in O4[29](though, ML-based unmodeled gravitational-wave searches have seen production usage[30]) . This is both a product of well-known infrastructure hurdles separating the development and deployment of machine learning models[31], as well as a lack of standardized, astrophysically meaningful probes of the sensitivity of these models in the face of non-stationary and transient background noise. The most well-modeled and frequently observed gravitational-wave events to date are the mergers of binary black hole (BBH) systems[2, 32 , 33] Their comparatively high number of confirmed detections has given us reasonable models of their population statistics, allowing for astrophysically

FAQs

Why is low latency crucial for gravitational-wave detection in multi-messenger astronomy?

Low latency preserves more time for follow-up observations across other messengers such as electromagnetic radiation and neutrinos.

What are the main obstacles to deploying machine-learning models in current gravitational-wave workflows?

Infrastructure hurdles and the lack of standardized, astrophysically meaningful ways to evaluate sensitivity under non-stationary, transient background noise.

How does the proposed pipeline (Aframe) compare with traditional matched filtering?

The pipeline runs with a fraction of the latency of traditional matched-filtering search pipelines while achieving state-of-the-art sensitivity for higher-mass stellar binary black holes.

Aug 5, 2026

Upload by Maeve