Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911717659770880 |
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| author | Lu, Hai-Ling Li, Yu-Yang Li, Yin-Bi Wang, Cun-Shi Luo, A-Li Liang, Jun-Chao Li, Shuo |
| author_facet | Lu, Hai-Ling Li, Yu-Yang Li, Yin-Bi Wang, Cun-Shi Luo, A-Li Liang, Jun-Chao Li, Shuo |
| contents | Stellar spectra encode key information on the physical properties and chemical compositions of stars. Accurate stellar parameter determination is essential for addressing major questions such as galaxy and stellar evolution. Large-scale spectroscopic surveys have accumulated unprecedented spectral data. Traditional feature extraction or model-fitting approaches struggle with high-dimensional, massive datasets, limited generalization, and computational inefficiency. Recent advances in large language models demonstrate strong generalization and feature-learning in tasks like natural language processing, DNA/RNA sequence analysis, and protein/chemical parsing. Stellar spectra are continuous sequential signals, enabling the transfer of language models to stellar spectroscopy. Here, we propose a two-stage large language model framework for stellar parameter inference, achieving accurate estimation of effective temperature, surface gravity, metallicity, and abundances of ~20 chemical elements. Scaling-law analyses show systematic performance improvements with increasing data, providing a scalable framework for forthcoming large-scale surveys. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22162 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference Lu, Hai-Ling Li, Yu-Yang Li, Yin-Bi Wang, Cun-Shi Luo, A-Li Liang, Jun-Chao Li, Shuo Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Machine Learning Stellar spectra encode key information on the physical properties and chemical compositions of stars. Accurate stellar parameter determination is essential for addressing major questions such as galaxy and stellar evolution. Large-scale spectroscopic surveys have accumulated unprecedented spectral data. Traditional feature extraction or model-fitting approaches struggle with high-dimensional, massive datasets, limited generalization, and computational inefficiency. Recent advances in large language models demonstrate strong generalization and feature-learning in tasks like natural language processing, DNA/RNA sequence analysis, and protein/chemical parsing. Stellar spectra are continuous sequential signals, enabling the transfer of language models to stellar spectroscopy. Here, we propose a two-stage large language model framework for stellar parameter inference, achieving accurate estimation of effective temperature, surface gravity, metallicity, and abundances of ~20 chemical elements. Scaling-law analyses show systematic performance improvements with increasing data, providing a scalable framework for forthcoming large-scale surveys. |
| title | Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference |
| topic | Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2605.22162 |