用变压器模型直接分析图像，不用减法就能高精度识别假信号。
Transformer-Based Neural Network for Transient Detection without Image Subtraction
- 用变压器结构对比原始图像，跳过耗时的差分成像
- 在暗能量巡天数据上达到97.4%分类准确率
- 适合大规模巡天中快速检测超新星候选体
我们提出一种基于变压器的神经网络，用于精确分类天文图像中的真实与虚假暂现源。该网络超越了传统卷积神经网络（CNN）方法，采用更适合像素级精细比对的架构，仅需分析搜索图和模板图，无需计算量巨大的差分成像，同时保持高性能。主要评估基于暗能量巡天（DES）的autoScan数据集，结果显示分类准确率达97.4%，且随着训练集规模增大，差分图像的性能贡献逐渐降低。进一步实验表明，即使输入图像未对准超新星候选体，网络仍能维持相近性能。这些发现证明该方法在提升大尺度天文巡天中超新星检测的准确性与效率方面具有显著优势。
原文摘要 · Abstract (English)
We introduce a transformer-based neural network for the accurate classification of real and bogus transient detections in astronomical images. This network advances beyond the conventional convolutional neural network (CNN) methods, widely used in image processing tasks, by adopting an architecture better suited for detailed pixel-by-pixel comparison. The architecture enables efficient analysis of search and template images only, thus removing the necessity for computationally-expensive difference imaging, while maintaining high performance. Our primary evaluation was conducted using the autoScan dataset from the Dark Energy Survey (DES), where the network achieved a classification accuracy of 97.4% and diminishing performance utility for difference image as the size of the training set grew. Further experiments with DES data confirmed that the network can operate at a similar level even when the input images are not centered on the supernova candidate. These findings highlight the network's effectiveness in enhancing both accuracy and efficiency of supernova detection in large-scale astronomical surveys.
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