PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics

TL;DR

PC-JeDi introduces a diffusion-based transformer method for efficient, conditional particle cloud jet generation in high energy physics, achieving competitive quality with faster-than-traditional simulation but slower than other models.

Contribution

It is the first to apply score-based diffusion models with transformers for particle cloud generation, enabling conditional control over jet properties.

Findings

Achieves competitive jet quality metrics.

Faster than traditional detailed simulation.

Enables conditional generation of jets with specific properties.

Abstract

In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with transformers which are well suited to the task of generating jets as particle clouds due to their permutation equivariance. PC-JeDi achieves competitive performance with current state-of-the-art methods across several metrics that evaluate the quality of the generated jets. Although slower than other models, due to the large number of forward passes required by diffusion models, it is still substantially faster than traditional detailed simulation. Furthermore, PC-JeDi uses conditional generation to produce jets with a desired mass and transverse momentum for two different particles, top quarks and gluons.