  Jet tagging is a critical yet challenging classification task in particle
physics. While deep learning has transformed jet tagging and significantly
improved performance, the lack of a large-scale public dataset impedes further
enhancement. In this work, we present JetClass, a new comprehensive dataset for
jet tagging. The JetClass dataset consists of 100 M jets, about two orders of
magnitude larger than existing public datasets. A total of 10 types of jets are
simulated, including several types unexplored for tagging so far. Based on the
large dataset, we propose a new Transformer-based architecture for jet tagging,
called Particle Transformer (ParT). By incorporating pairwise particle
interactions in the attention mechanism, ParT achieves higher tagging
performance than a plain Transformer and surpasses the previous
state-of-the-art, ParticleNet, by a large margin. The pre-trained ParT models,
once fine-tuned, also substantially enhance the performance on two widely
adopted jet tagging benchmarks. The dataset, code and models are publicly
available at https://github.com/jet-universe/particle_transformer.
