RADAR-Radio Afterglow Detection and AI-driven Response: A Federated Framework for Gravitational Wave Event Follow-Up

TL;DR

RADAR is a federated, AI-driven framework designed to improve gravitational wave event follow-up by enhancing community collaboration, data sharing, and long-term radio afterglow monitoring.

Contribution

The paper introduces RADAR, a novel federated framework integrating AI for GW detection and radio follow-up, addressing resource constraints and promoting data rights preservation.

Findings

RADAR effectively coordinated follow-up strategies in a GW170817 case study.

The framework enables community-driven data sharing without compromising data ownership.

AI methods within RADAR improve GW signal identification and radio data analysis.

Abstract

The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of ground-based GW detectors. Within this context, the radio band of the EM spectrum presents unique challenges. Sensitive radio facilities capable of detecting the faint radio afterglow seen in GW170817, and with sufficient angular resolution, have small fields of view compared to typical GW localization areas. Additionally, theoretical models predict that the radio emission from binary neutron star mergers can evolve over weeks to years, necessitating long-term monitoring to probe the physics of the various post-merger ejecta components. These constraints, combined with limited radio observing resources, make the development of more coordinated follow-up strategies essential -- especially as the next generation of GW detectors promise a dramatic increase in detection rates. Here, we present RADAR, a framework designed to address these challenges by promoting community-driven information sharing, federated data analysis, and system resilience, while integrating AI methods for both GW signal identification and radio data aggregation. We show that it is possible to preserve data rights while sharing models that can help design and/or update follow-up strategies. We demonstrate our approach through a case study of GW170817, and discuss future directions for refinement and broader application.