Speaker
Description
Particle Transformer ( ParT) has achieved state of the art performance in jet tagging by modeling particle level information through attention. In this work, we introduce Mod-ParT, a physics aware extension of ParT that incorporates relational information into particle representations, enriches pairwise attention biases with additional physics motivated features and replaces the class token and class attention stages with a hybrid pooling module for jet representation. We trained Mod-ParT from scratch on the established top tagging reference dataset and the large scale TopTagXL dataset, using training samples of 1.2 million and 10 million jets respectively. We further trained the model on quark gluon discrimination benchmark, generated with Pythia8. Across the top tagging and quark gluon discrimination tasks, Mod-ParT achieves competitive performance in terms of accuracy, AUC and background rejection metrics. We also investigate computational performance under matched execution conditions on a single GPU considering computational complexity, training latency and peak memory consumption. The results show that Mod-ParT can substantially reduce computational and memory requirements relative to ParT and several architectures evaluated under comparable benchmark conditions while maintaining competitive tagging performance. Overall, Mod-ParT provides a favorable balance between predictive performance and lower computational cost and higher speed across distinct jet classification tasks.