Related papers: Particle Multi-Axis Transformer for Jet Tagging

Particle Multi-Axis Transformer for Jet Tagging

URL: http://arxiv.org/abs/2406.06638v2
Date: Tue, 16 Jul 2024 07:39:49 GMT
Title: Particle Multi-Axis Transformer for Jet Tagging
Authors: Muhammad Usman, M Husnain Shahid, Maheen Ejaz, Ummay Hani, Nayab Fatima, Abdul Rehman Khan, Asifullah Khan, Nasir Majid Mirza,
Abstract summary: In this article, we propose an idea of a new architecture, Particle Multi-Axis transformer (ParMAT) 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.
Score: 0.774199694856838
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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