arXiv 2507.019392025-12-19astro-ph.IMastro-ph.SRcs.AIcs.LG

SpecCLIP：对齐和翻译恒星光谱测量

SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars

School of Astronomy；Space Science, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China；National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, People's Republic of China；Department of Physics \& Astronomy, The Johns Hopkins University, Baltimore, MD 21218, USA；Zhejiang Laboratory, Hangzhou 311121, People's Republic of China；Research Center for Astronomical Computing, Zhejiang Laboratory, Hangzhou 311121, People's Republic of China；Department of Physics；Astronomy, University of Notre Dame, Notre Dame, IN 46556, USA；Joint Institute for Nuclear Astrophysics -- Center for the Evolution of the Elements (JINA-CEE), USA；Department of Astronomy, The Ohio State University, 140 West 18th Avenue, Columbus, OH 43210, USA；Center for Cosmology；AstroParticle Physics (CCAPP), The Ohio State University, Columbus, OH 43210, USA

Xiaosheng Zhao, Yang Huang, Guirong Xue, Xiao Kong, Jifeng Liu, Xiaoyu Tang, Timothy C. Beers, Yuan-Sen Ting, A-Li Luo

备注：: 29 pages, 8 figures, 6 tables. Accepted for publication in ApJ. Comments welcome

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AI总结：

SpecCLIP通过对比学习和光谱意识解码器提升恒星光谱分析的精度和应用灵活性。

摘要：

近年来，大型语言模型（LLMs）通过庞大的数据集和大规模参数化改变了自然语言理解。受此成功启发，我们提出了SpecCLIP，一种基础模型框架，将LLM启发的方法扩展到恒星光谱分析。恒星光谱，类似于结构化的语言，编码了关于恒星丰富的物理和化学信息。通过在大规模光谱数据集上训练基础模型，我们的目标是学习稳健且信息丰富的嵌入，以支持多种下游应用。作为概念验证，SpecCLIP包括在两种光谱类型——LAMOST低分辨率和Gaia XP——上进行预训练，随后使用CLIP（对比语言-图像预训练）框架进行对比对齐，适应于将不同仪器的光谱关联起来。这种对齐通过辅助解码器得到补充，这些解码器保留了光谱特定的信息，并使光谱类型之间实现翻译（预测），前者通过最大化嵌入与输入光谱之间的互信息来实现。结果是一种跨光谱框架，能够实现内在校准，并在不同仪器上灵活应用。我们证明了在中等大小的标记数据集上微调这些模型可以提高对恒星参数估计和化学丰度确定等任务的适应性。SpecCLIP还提高了其参数估计的准确性和精度，与外部调查数据相比。此外，其相似性搜索和跨光谱预测能力为异常检测提供了潜在的可能性。我们的结果表明，通过对比训练并结合光谱意识解码器的基础模型可以推动精确的恒星光谱学发展。我们的代码SpecCLIP在https://github.com/Xiaosheng-Zhao/SpecCLIP上公开。

英文摘要：

In recent years, large language models (LLMs) have transformed natural language understanding through vast datasets and large-scale parameterization. Inspired by this success, we present SpecCLIP, a foundation model framework that extends LLM-inspired methodologies to stellar spectral analysis. Stellar spectra, akin to structured language, encode rich physical and chemical information about stars. By training foundation models on large-scale spectral datasets, our goal is to learn robust and informative embeddings that support diverse downstream applications. As a proof of concept, SpecCLIP involves pre-training on two spectral types--LAMOST low-resolution and Gaia XP--followed by contrastive alignment using the CLIP (Contrastive Language-Image Pre-training) framework, adapted to associate spectra from different instruments. This alignment is complemented by auxiliary decoders that preserve spectrum-specific information and enable translation (prediction) between spectral types, with the former achieved by maximizing mutual information between embeddings and input spectra. The result is a cross-spectrum framework enabling intrinsic calibration and flexible applications across instruments. We demonstrate that fine-tuning these models on moderate-sized labeled datasets improves adaptability to tasks such as stellar-parameter estimation and chemical-abundance determination. SpecCLIP also enhances the accuracy and precision of parameter estimates benchmarked against external survey data. Additionally, its similarity search and cross-spectrum prediction capabilities offer potential for anomaly detection. Our results suggest that contrastively trained foundation models enriched with spectrum-aware decoders can advance precision stellar spectroscopy. Our code SpecCLIP is publicly available at https://github.com/Xiaosheng-Zhao/SpecCLIP

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