  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 proposed
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.
