基于不同光谱表示的预训练视觉Transformer在超新星光谱分类中的性能研究
On the performance of pre-trained vision transformers for supernova spectral classification using different spectral representations
AI总结：
本研究探究预训练视觉Transformer用于超新星光谱分类的性能，对比不同光谱表示与微调策略，发现普通流量-波长线图表现更优，最佳模型vitb-p16在测试集宏F1达86.3%，可实现有竞争力的单光谱分类。
AI中文摘要：
超新星的光谱分类是时域天文学的关键组成部分，对Ia型超新星事件的识别具有重要作用。不断增长的光谱数据量与多样性推动了自动分类方法的发展。本研究探索基于Transformer的视觉模型在超新星光谱分类中的应用，重点关注将基于图像的架构应用于本质为一维的数据时，不同的光谱视觉表示及微调策略如何影响分类性能。我们使用包含4011个超新星光谱的数据集，通过数据增强和重平衡处理为三类天体物理动机的类别：正常Ia型、其他Ia型亚型和核心坍缩超新星。光谱被编码为线图图像，采用直接流量-波长可视化，以及用于强调差分光谱结构的替代流量差-波长差热力图表示。我们评估了多种预训练Transformer架构，包括Vision Transformers（ViT）、Swin Transformers和DINOv3预训练的ViT，并研究了微调深度、视觉表示和超参数选择的影响。我们发现，普通流量-波长线图的平均性能优于专用映射，但对数尺度映射在特定架构和微调组合下仍具有竞争力。在保留的测试集上，我们的最佳模型（vitb-p16）达到了宏F1分数86.3%，各类别的F1分数分别为：正常Ia型94%、其他Ia型亚型77%、核心坍缩超新星88%。主要的误分类源于Ia-91T与正常Ia型光谱的混淆。这些结果表明，基于Transformer的视觉模型可为单光谱超新星光谱分类提供具有竞争力的性能。
英文摘要：
The spectroscopic classification of supernovae is a key component of time-domain astronomy and plays an important role in the identification of Type Ia events. The increasing volume and diversity of spectroscopic data motivate the development of automated classification approaches. In this work, we explore the use of Transformer-based vision models for supernova spectral classification, focusing on how different visual representations of spectra and fine-tuning strategies affect classification performance when image-based architectures are applied to intrinsically one-dimensional data. We consider a dataset of 4,011 supernova spectra, augmented and rebalanced into three astrophysically motivated classes: normal Type Ia, other Type Ia subtypes, and core-collapse supernovae. Spectra are encoded as line-plot images using direct flux-wavelength visualizations as well as alternative flux-difference versus wavelength-difference heatmap representations designed to emphasize differential spectral structure. We evaluate several pre-trained Transformer architectures, including Vision Transformers (ViT), Swin Transformers, and a DINOv3-pretrained ViT, and examine the effects of fine-tuning depth, visual representation, and hyperparameter choices. We find that a plain flux-wavelength line plot outperforms the purpose-built maps on average, though log-scale maps remain competitive for specific architecture and fine-tuning combinations. On the held-out test set, our best model (\texttt{vitb-p16}) reaches a macro-F1 score of 86.3%, with per-class F1 scores of 94% for normal Ia, 77% for other Ia subtypes, and 88% for core-collapse supernovae. The dominant misclassification arises from confusion between Ia-91T and normal Ia spectra. These results demonstrate that Transformer-based vision models can provide competitive performance for single-spectrum supernova spectral classification.
