An agentic framework for gravitational-wave counterpart association in the multi-messenger era

2026-05-11

AI Summary

This work addresses the challenges posed by the rapidly growing volume of data and the limited efficiency of conventional methods in associating gravitational-wave events with their electromagnetic counterparts in multi-messenger astronomy. To this end, the authors propose GW-Eyes, an intelligent agent framework built upon large language models (LLMs). GW-Eyes pioneers the deep integration of LLMs with domain-specific tools—such as sky-map processors and transient-source databases—and enables natural-language interaction to support an end-to-end automated pipeline encompassing data retrieval, visualization, autonomous reasoning, and hypothesis validation. The framework not only substantially enhances the efficiency and traceability of association analyses but also establishes a novel AI-driven paradigm for multi-messenger astrophysical data analysis, offering critical technical capabilities for real-time response in the era of next-generation observatories.

Abstract

With the detection of gravitational waves (GWs), multi-messenger astronomy has opened a new window for advancing our understanding of astrophysics, dense matter, gravitation, and cosmology. The GW sources detected to date are from mergers of compact object binaries, which possess the potential to generate detectable electromagnetic (EM) counterparts. Searching for associations between GW signals and their EM counterparts is an essential step toward enabling subsequent multi-messenger studies. In the era of next-generation GW and EM detectors, the rapid increase in the number of events brings not only unprecedented scientific opportunities, but also substantial challenges to the existing data analysis paradigm. To help address these challenges, we develop GW-Eyes, an agentic framework powered by large language models (LLMs). For the first time, GW-Eyes integrates domain-specific tools and autonomously performs counterpart association tasks between GW and candidate EM events. It supports natural language interaction to assist human experts with auxiliary tasks such as catalog management, skymap visualization, and rapid verification. Our framework leverages the complex decision-making capabilities of LLMs and their traceable reasoning processes, offering a new perspective to the multi-messenger astronomy.

Problem

• gravitational-wave counterpart association
• multi-messenger astronomy
• electromagnetic counterparts
• data analysis challenge
• event association

Innovation

• agentic framework
• large language models
• multi-messenger astronomy
• gravitational-wave counterpart association
• autonomous data analysis