Commission B3 Astroinformatics and Astrostatistics



  • Yuan-Sen Ting (The Australian National University, Australia

    Expediting Astronomical Discovery with Large Language Models: Progress, Challenges, and Future Directions 

    Abstract: The vast and interdisciplinary nature of astronomy, coupled with its open-access ethos, makes it an ideal testbed for exploring the potential of Large Language Models (LLMs) in automating and accelerating scientific discovery. In this talk, we present our recent progress in applying LLMs to tackle real-life astronomy problems. We demonstrate the ability of LLM agents to perform end-to-end research tasks, from data fitting and analysis to iterative strategy improvement and outlier detection, mimicking human intuition and deep literature understanding. However, the cost-effectiveness of closed-source solutions remains a challenge for large-scale applications involving billions of sources. To address this issue, we introduce our ongoing work at AstroMLab on training lightweight, open-source specialized models and our effort to benchmark these models with carefully curated astronomy benchmark datasets. We will also discuss our effort to construct the first LLM-based knowledge graph in astronomy, leveraging citation-reference relations. The open-source specialized LLMs and knowledge graph are expected to guide more efficient strategy searches in autonomous research pipelines. While many challenges lie ahead, we explore the immense potential of scaling up automated inference in astronomy, revolutionizing the way astronomical research is conducted, ultimately accelerating scientific breakthroughs and deepening our understanding of the Universe.

Astrostatistics Newsletter

If you have been wanting to stay up to date on the happenings in the astrostatistics community, or to learn more about astrostatistics, please subscribe to Astrostatistics News.  Astrostatistics News (AN) is a newsletter designed to inform, promote, cultivate, and inspire the astrostatistics community.  AN serves the astrostatistics community by highlighting and describing recent research developments in astrostatistics at an accessible level to the diverse backgrounds of its members, sharing interesting new algorithms, software, or data sets, promoting relevant events, and striving to inspire new researchers to join in the fun.  We anticipate 2 - 3 issues per year, with the potential for more.

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