An agentic framework for gravitational-wave counterpart association in the multi-messenger era
Yiming Dong, Yacheng Kang, Junjie Zhao, Xinyuan Zhu, Ziming Wang, Lijing Shao

TL;DR
This paper introduces GW-Eyes, an agentic framework utilizing large language models to autonomously associate gravitational wave signals with electromagnetic counterparts, enhancing multi-messenger astronomy in the era of next-generation detectors.
Contribution
It presents the first integration of LLMs with domain-specific tools for autonomous GW-EM counterpart association, supporting natural language interaction and complex decision-making.
Findings
GW-Eyes can autonomously perform GW-EM counterpart association tasks.
Supports natural language interaction for auxiliary tasks.
Leverages LLM reasoning for complex decision-making.
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…
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