49 citations · 116 across the 10 of their papers we have counts for
4 papers · 1 filter
TURBO: The Swiss Knife of Auto-Encoders
Guillaume Quétant, Yury Belousov, Vitaliy Kinakh +1
We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods. We start by examining the principles o…
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
Erik Buhmann, Cedric Ewen, Darius A. Faroughy +7
Jets at the LHC, typically consisting of a large number of highly correlated particles, are a fascinating laboratory for deep generative modeling. In this paper, we present two nov…
PC-Droid: Faster diffusion and improved quality for particle cloud generation
Matthew Leigh, Debajyoti Sengupta, John Andrew Raine +2
Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved diffusion model for the generation of jet particle clouds. By leveraging a new diffusion formulat…
PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
Matthew Leigh, Debajyoti Sengupta, Guillaume Quétant +3
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 tra…