Problem
Research questions and friction points this paper is trying to address.
Astronomical spectra
expert interpretation
spectrum-language model
instruction-following conversations
natural-language explanations
AstroSpecLM: A Spectrum-Language Model for Evidence-Grounded Astronomical Spectral Analysis
🤖 AI Summary
本文提出AstroSpecLM模型，通过结合DESI光谱与Qwen3-4B生成基于光谱证据的问题解答和解释，解决了天文光谱分析需要专家解读的问题。
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Spectrum-Language Model
Evidence-Grounded Explanation
Instruction-Following Conversations
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