We present a deep learning approach to classify fast radio bursts (FRBs) based purely on morphology as encoded on recorded dynamic spectrum from CHIME/FRB Catalog 2. We implemented transfer learning with a pretrained ConvNext architecture, exploiting its powerful feature extraction ability. ConvNext was adapted to classify dedispersed dynamic spectra (which we treat as images) of the FRBs into one of the two sub-classes, i.e., repeater and non-repeater, based on their various temporal and spectral properties and relation between the sub-pulse structures. Additionally, we also used mathematical model representation of the total intensity data to interpret the deep learning model. Upon fine-tuning the pretrained ConvNext on the FRB spectrograms, we were able to achieve high classification metrics while substantially reducing training time and computing power as compared to training a deep learning model from scratch with random weights and biases without any feature extraction ability. Importantly, our results suggest that the morphological differences between CHIME repeating and non-repeating events persist in Catalog 2 and the deep learning model leveraged these differences for classification. The fine-tuned deep learning model can be used for inference, which enables us to predict whether an FRB's morphology resembles that of repeaters or non-repeaters. Such inferences may become increasingly significant when trained on larger data sets that will exist in the near future.

翻译：本文提出了一种基于形态特征的深度学习分类方法，用于区分快速射电暴（FRBs）。该方法仅利用CHIME/FRB Catalog 2记录的动态频谱中编码的形态信息。我们通过迁移学习策略，采用预训练的ConvNext架构，充分利用其强大的特征提取能力。ConvNext被调整为根据FRB在去色散动态频谱（我们将其视为图像）中表现出的时频特性及子脉冲结构间关系，将其分类为重复暴与非重复暴两个子类。此外，我们还通过总强度数据的数学模型表示来解析深度学习模型的内在机制。在FRB频谱图上对预训练的ConvNext进行微调后，我们不仅获得了较高的分类评价指标，而且与从随机初始权重开始训练、不具备特征提取能力的深度学习模型相比，显著减少了训练时间与计算资源消耗。重要的是，我们的结果表明：CHIME观测中重复暴与非重复暴事件的形态差异在Catalog 2数据中持续存在，而深度学习模型有效利用了这些差异进行分类。微调后的深度学习模型可用于推理预测，使我们能够判断某个FRB的形态特征更接近重复暴还是非重复暴。当未来在更大规模数据集上进行训练时，此类推理预测的重要性将日益凸显。