PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
This addresses the need for efficient jet generation in High Energy Physics, though it is incremental as it builds on existing diffusion and transformer approaches.
The paper tackles the problem of generating jets as particle clouds in High Energy Physics by proposing PC-JeDi, a method using score-based diffusion models with transformers, which achieves competitive performance with state-of-the-art methods across several metrics and is faster than traditional simulation.
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.