Spectra as Language: Large Language Models for Scalable Stellar Parameter and Abundance Inference
arXiv:2605.22162v1 Announce Type: cross Abstract: 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 f
The proliferation of powerful large language models and vast astronomical datasets enables novel applications of AI to complex scientific challenges.
This development allows for highly scalable and efficient analysis of stellar data, which is crucial for advancing astrophysics and understanding cosmic evolution.
Traditional, computationally intensive methods for stellar parameter inference are being supplanted by more generalized and efficient AI-driven approaches.
- · Astrophysicists
- · Astronomical observatories
- · Data scientists in scientific research
- · AI/ML research organizations
- · Developers of traditional spectral analysis software
This enables faster and more accurate classification of stars and cosmic phenomena from large spectroscopic surveys.
Accelerated understanding of galaxy formation, stellar evolution, and exoplanet characterization due to improved data interpretation.
New astrophysical discoveries may emerge from patterns identified by AI that are unobservable with previous analytical methods, potentially redirecting research priorities.
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