AI 中文总结
本文提出AstroSpecLM，通过将一维DESI光谱与Qwen3-4B结合，利用事实介导的指令数据生成基于光谱证据的解释，在分类和红移估计上媲美专家基线，实现预测与解释兼备。
AI 中文摘要
天文光谱编码了丰富的物理信息，但从光谱特征中得出科学结论通常需要专家解读。本文提出了AstroSpecLM，一种谱-语言模型，它将一维DESI光谱与Qwen3-4B连接起来，以回答问题并提供基于光谱证据的解释。我们不是直接从模板或原始星表字段生成问答对，而是首先将每条光谱提炼为一组紧凑的星表及光谱衍生事实，然后以这些事实为参考生成遵循指令的对话。所得模型在分类和红移估计方面与专家监督基线相当，同时还能生成引用特定光谱特征的自然语言解释。我们的结果表明，将语言模型基于一维科学光谱进行接地是可行的，且事实介导的指令数据能产生一个既能预测又能解释的模型。
英文摘要
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.
Comments15 pages, 6 figures, 7 tables, including supplementary material
