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AstroSpecLM demonstrates the feasibility of grounding large language models in one-dimensional astronomical spectra to generate fact-based explanations alongside redshift estimation and classification tasks. By distilling spectral data into compact facts before generating instruction-following conversations, this approach bridges the gap between raw physical data and expert-level scientific interpretation without relying on templates. This work highlights a significant advancement in applying AI to specialized domains where interpretability and evidence grounding are critical for scientific discovery.
AstroSpecLM: A Spectrum-Language Model for Evidence-Grounded Astronomical Spectral Analysis
arXiv:2609.07102v1 Announce Type: cross Abstract: Astronomical spectra encode rich physical information, but drawing scientific conclusions from spectral features typically requires expert interpretation. This paper presents AstroSpecLM, a spectrum-language model that connects one-dimensional DESI spectra with Qwen3-4B to answer questions and provide explanations grounded in spectral evidence. Instead of generating question-answer pairs directly from templates or raw catalog fields, we first distill each spectrum into a compact set of catalog- and spectrum-derived facts, then use these facts as references to generate instruction-following conversations. The resulting model is competitive with specialist supervised baselines on classification and redshift estimation, while additionally producing natural-language explanations that reference specific spectral features. Our results indicate that grounding a language model in one-dimensional scientific spectra is feasible, and that fact-mediated instruction data yields a model capable of both prediction and explanation.
Abstract:Astronomical spectra encode rich physical information, but drawing scientific conclusions from spectral features typically requires expert interpretation. This paper presents AstroSpecLM, a spectrum-language model that connects one-dimensional DESI spectra with Qwen3-4B to answer questions and provide explanations grounded in spectral evidence. Instead of generating question-answer pairs directly from templates or raw catalog fields, we first distill each spectrum into a compact set of catalog- and spectrum-derived facts, then use these facts as references to generate instruction-following conversations. The resulting model is competitive with specialist supervised baselines on classification and redshift estimation, while additionally producing natural-language explanations that reference specific spectral features. Our results indicate that grounding a language model in one-dimensional scientific spectra is feasible, and that fact-mediated instruction data yields a model capable of both prediction and explanation.
Instrumentation and Methods for Astrophysics (astro-ph.IM); Artificial Intelligence (cs.AI)
arXiv:2609.07102 [astro-ph.IM]
(or arXiv:2609.07102v1 [astro-ph.IM] for this version)
https://doi.org/10.48550/arXiv.2609.07102 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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