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arXiv · 2602.00141

Comparison of Image Processing Models in Quark Gluon Jet Classification

Daeun Kim·Jiwon Lee·Wonjun Jeong·Hyeongwoo Noh·Giyeong Kim·Jaeyoon Cho·Geonhee Kwak·Seunghwan Yang·MinJung Kweon
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Abstract

We present a comprehensive comparison of convolutional and transformer-based models for distinguishing quark and gluon jets using simulated jet images from Pythia 8. By encoding jet substructure into a three-channel representation of particle kinematics, we evaluate the performance of convolutional neural networks (CNNs), Vision Transformers (ViTs), and Swin Transformers (Swin-Tiny) under both supervised and self-supervised learning setups. Our results show that fine-tuning only the final two transformer blocks of the Swin-Tiny model achieves the best trade-off between efficiency and accuracy, reaching 81.4% accuracy and an AUC (area under the ROC curve) of 88.9%. Self-supervised pretraining with Momentum Contrast (MoCo) further enhances feature robustness and reduces the number of trainable parameters. These findings highlight the potential of hierarchical attention-based models for jet substructure studies and for domain transfer to real collision data.

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Daeun Kim, Jiwon Lee, Wonjun Jeong, Hyeongwoo Noh, Giyeong Kim, Jaeyoon Cho, Geonhee Kwak, Seunghwan Yang, MinJung Kweon. 2026-01-29. Comparison of Image Processing Models in Quark Gluon Jet Classification. https://arxiv.org/abs/2602.00141

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