Search Results for author:
Found 6 papers, 3 papers with code
Masked Particle Modeling on Sets: Towards Self-Supervised High Energy Physics Foundation Models
We propose masked particle modeling (MPM) as a self-supervised method for learning generic, transferable, and reusable representations on unordered sets of inputs for use in high energy physics (HEP) scientific data.
Improving new physics searches with diffusion models for event observables and jet constituents
We introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC.
EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusion
In addition, we introduce \epcfm, the first permutation equivariant continuous normalizing flow (CNF) for particle cloud generation.
PC-Droid: Faster diffusion and improved quality for particle cloud generation
Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved diffusion model for the generation of jet particle clouds.
$ν^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows
In this work we introduce $\nu^2$-Flows, an extension of the $\nu$-Flows method to final states containing multiple neutrinos.
PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics
In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi.
