LLMs Automate Analysis of NASA's GCN Circulars

This research explores the use of Large Language Models (LLMs) to automate the analysis of NASA's General Coordinates Network (GCN) Circulars. The GCN, a time-domain and multimessenger alert system, distributes automated Notices and human-generated Circulars, reporting observations of high-energy and multimessenger astronomical transients. The unstructured format of over 40,500 Circulars accumulated over three decades poses a challenge for manual information extraction. The study employs LLMs to facilitate automated parsing of these transient reports, developing a neural topic modeling pipeline for clustering and summarizing astrophysical topics. The system classifies Circulars based on observation wave bands and messengers, separating gravitational-wave event clusters and their electromagnetic counterparts. Furthermore, it extracts gamma-ray burst (GRB) redshift information with high accuracy (97.2%) using the open-source Mistral model. This work demonstrates the potential of LLMs to automate and enhance astronomical text mining, providing a foundational work for future advances in transient alert analysis. The neural search-enhanced RAG pipeline accurately retrieved 96.8% of redshift Circulars from the manually curated archive. The success of this approach hinges on the quality of the prompts and the data used for retrieval augmented generation. Further research is needed to assess the robustness and generalizability of the system across different datasets and formats.

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