An agentic framework for gravitational-wave counterpart association in the multi-messenger era
For multi-messenger astronomers, this framework offers a new paradigm to handle the data analysis bottleneck caused by next-generation detectors, though it is an incremental step combining existing LLMs with domain tools.
GW-Eyes is an LLM-powered agentic framework that autonomously performs gravitational-wave counterpart association, integrating domain-specific tools and supporting natural language interaction. It addresses the challenge of rapidly increasing event rates in the multi-messenger era.
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.