夸克胶子喷注分类中的图像处理模型比较
Comparison of Image Processing Models in Quark Gluon Jet Classification
AI总结：
本文比较了卷积和Transformer模型在夸克胶子喷注分类中的性能，发现Swin-Tiny模型在效率与准确性之间取得最佳平衡，同时自监督预训练提升了特征鲁棒性。
摘要：
我们展示了卷积和基于Transformer的模型在区分夸克和胶子喷注方面的综合比较，使用Pythia 8模拟的喷注图像。通过将喷注子结构编码为粒子动力学的三通道表示，我们评估了卷积神经网络（CNNs）、视觉Transformer（ViTs）和Swin Transformer（Swin-Tiny）在监督和自监督学习设置下的性能。我们的结果表明，仅微调Swin-Tiny模型的最后两个Transformer块在效率和准确性之间达到了最佳平衡，达到了81.4%的准确率和88.9%的AUC（ROC曲线下的面积）。使用Momentum Contrast（MoCo）进行自监督预训练进一步增强了特征鲁棒性并减少了可训练参数的数量。这些发现突显了基于层次注意力模型在喷注子结构研究以及向真实碰撞数据域转移的潜力。
英文摘要：
Quark-gluon discrimination provides a useful test case for studying how different machine-learning architectures learn the spatial structure of QCD radiation. In this work, we compare convolutional neural network (CNN), Vision Transformers (ViT), and hierarchical Swin Transformers using the same three-channel jet-image representation, consisting of charged-particle momentum, neutral-particle momentum, and charged-particle multiplicity from PYTHIA 8 jets. We study their performance for different training-set sizes and fine-tuning configurations, with particular attention to the role of local and global information in the jet images. CNN and Swin models consistently perform better than ViT in the cases studied. Since both CNN and Swin retain a strong local component in their architectures, this suggests that local jet substructure plays an important role in quark-gluon discrimination. The performance of the hierarchical Swin model also suggests that combining local features over larger spatial scales is useful. Block-wise fine-tuning improves the performance of the Transformer models, although the improvement becomes smaller and the training less stable as more blocks are unfrozen. We also find that self-supervised Momentum Contrast (MoCo) pretraining improves the model initialization, particularly when the amount of labeled training data is limited. Based on these observations, we developed a smaller Swin model adopted to the jet-image representation used in this study. It achieves comparable performance with substantially fewer parameters. The results show that it is important to adapt the model architecture and training procedure to the specific input characteristics of High Energy Physics (HEP) data when applying vision models in HEP.
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