mlsanityData to 14 Oct 2026

Particle Multi-Axis Transformer for Jet Tagging

arXiv 2406.06638 ↗First posted 9 Jun 2024hep-ph cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 42 references that are in the map. First posted on arXiv 9 Jun 2024.

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References · 23

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11 more
  1. 2022Particle Transformer for Jet Taggingcited by 98
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