用视觉模型分析天体光谱，效果超越传统方法。
Applying Vision Transformers on Spectral Analysis of Astronomical Objects
- 将光谱转为图像，用ViT捕捉局部与全局特征
- 分类准确率高于SVM和随机森林，红移估计效果接近AstroCLIP
- 首次在真实光谱数据上应用预训练视觉模型
我们将原本用于图像识别的预训练视觉变换器（ViT）应用于天文光谱数据分析。通过将传统的单维光谱转化为二维图像表示，ViT可利用空间自注意力机制捕获光谱的局部与全局特征。我们使用来自斯隆数字巡天（SDSS）和拉莫斯特（LAMOST）巡天的数百万条光谱数据（以光谱图形式呈现），对ImageNet预训练的ViT进行微调。模型在恒星分类与红移（z）估计等关键任务中表现优异，分类准确率超过支持向量机（SVM）和随机森林，红移预测的R²值与AstroCLIP的光谱编码器相当，且在不同天体类型间具有良好泛化能力。结果表明，预训练视觉模型在光谱分析中具有强大有效性。据我们所知，这是首个直接基于真实大规模光谱数据、不依赖合成输入的ViT应用。
原文摘要 · Abstract (English)
We apply pre-trained Vision Transformers (ViTs), originally developed for image recognition, to the analysis of astronomical spectral data. By converting traditional one-dimensional spectra into two-dimensional image representations, we enable ViTs to capture both local and global spectral features through spatial self-attention. We fine-tune a ViT pretrained on ImageNet using millions of spectra from the SDSS and LAMOST surveys, represented as spectral plots. Our model is evaluated on key tasks including stellar object classification and redshift ($z$) estimation, where it demonstrates strong performance and scalability. We achieve classification accuracy higher than Support Vector Machines and Random Forests, and attain $R^2$ values comparable to AstroCLIP's spectrum encoder, even when generalizing across diverse object types. These results demonstrate the effectiveness of using pretrained vision models for spectroscopic data analysis. To our knowledge, this is the first application of ViTs to large-scale, which also leverages real spectroscopic data and does not rely on synthetic inputs.
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