Jet tagging is an essential categorization problem in high energy physics. In recent times, Deep Learning has not only risen to the challenge of jet tagging but also significantly improved its performance. In this article, we propose an idea of a new architecture, Particle Multi-Axis transformer (ParMAT) which is a modified version of Particle transformer (ParT). ParMAT contains local and global spatial interactions within a single unit which improves its ability to handle various input lengths. We trained our model on JETCLASS, a publicly available large dataset that contains 100M jets of 10 different classes of particles. By integrating a parallel attention mechanism and pairwise interactions of particles in the attention mechanism,ParMAT achieves robustness and higher accuracy over the ParT and ParticleNet. The scalability of the model to huge datasets and its ability to automatically extract essential features demonstrate its potential for enhancing jet tagging.

我们提出了一个新架构的想法，Particle Multi-Axis transformer (ParMAT)，它是Particle transformer (ParT)的改进版本，通过在单个单元内包含局部和全局空间交互，提高处理各种输入长度的能力，为了实现对大规模数据集的可扩展性和自动提取关键特征的能力，我们在JETCLASS数据集上训练了模型，并通过集成平行注意机制和注意机制中的粒子成对交互，ParMAT相对于ParT和ParticleNet表现出更好的稳健性和准确性。

粒子多轴变压器用于喷射标记