用对比学习统一不同望远镜的恒星光谱，实现跨仪器精准分析。
SpecCLIP: Aligning and Translating Spectroscopic Measurements for Stars
- 基于对比学习对多源光谱数据进行联合训练，构建跨仪器对齐框架。
- 在中等规模标注数据上微调后，恒星参数估计精度显著提升。
- 适合天体物理研究者用于光谱校准、异常检测与多源数据融合。
近年来，大规模语言模型通过海量数据和参数化实现了自然语言理解的突破。受此启发，我们提出SpecCLIP，一种将语言模型方法拓展至恒星光谱分析的基础模型框架。恒星光谱蕴含丰富的物理与化学信息，类似结构化语言。通过在大规模光谱数据集上预训练，目标是学习鲁棒且信息丰富的嵌入表示，支持多样下游应用。作为概念验证，SpecCLIP在两组光谱类型（LAMOST低分辨光谱与Gaia XP）上进行预训练，并采用适配后的对比语言-图像预训练（CLIP）框架实现跨仪器光谱对齐。该对齐过程辅以辅助解码器，保留光谱特异性信息并实现光谱类型间的翻译（预测），前者通过最大化嵌入与输入光谱之间的互信息实现。结果是一个可实现内在校准、灵活应用于不同仪器的跨光谱框架。实验表明，在中等规模标注数据上微调后，模型在恒星参数估计与元素丰度测定任务中的适应性显著增强；与外部巡天数据比对，参数估计的准确性和精确度均获提升。此外，其相似性搜索与跨光谱预测能力为异常检测提供可能。结果表明，经对比训练并引入光谱感知解码器的基础模型可推动高精度恒星光谱学发展。代码已公开于 https://github.com/Xiaosheng-Zhao/SpecCLIP。
原文摘要 · Abstract (English)
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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