LLMs for Scalable Stellar Parameter Inference

This paper explores the application of large language models (LLMs) to stellar spectroscopy, specifically for inferring stellar parameters and chemical abundances. Traditional methods struggle with the high dimensionality and volume of data from large-scale spectroscopic surveys. The proposed two-stage LLM framework demonstrates accurate estimation of key stellar parameters, including effective temperature, surface gravity, metallicity, and the abundances of approximately 20 chemical elements. Scaling-law analyses indicate that performance improves with increasing data, suggesting a scalable solution for future large-scale surveys. This approach leverages the generalization and feature-learning capabilities of LLMs, previously demonstrated in natural language processing and other sequence analysis tasks. However, potential biases inherent in LLMs and the need for rigorous validation against established methods should be considered. The successful application of LLMs in this domain could significantly accelerate astrophysical research and our understanding of stellar evolution and galactic structure. The use of alphaXiv, CatalyzeX, DagsHub, Gotit.pub, Hugging Face, and ScienceCast indicates a commitment to open science and reproducible research.

_Context: This intelligence report was compiled by the DailyOrbitalWire Strategy Engine. Verified for Art. 50 Compliance._