Set-Conditional Set Generation for Particle Physics
High Energy Physics - Experiment
2024-11-07 v2
Abstract
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued data is generated conditional on a set of incoming particles. To accelerate this task, we present a novel generative model based on a graph neural network and slot-attention components, which exceeds the performance of pre-existing baselines.
Cite
@article{arxiv.2211.06406,
title = {Set-Conditional Set Generation for Particle Physics},
author = {Francesco Armando Di Bello and Etienne Dreyer and Sanmay Ganguly and Eilam Gross and Lukas Heinrich and Marumi Kado and Nilotpal Kakati and Jonathan Shlomi and Nathalie Soybelman},
journal= {arXiv preprint arXiv:2211.06406},
year = {2024}
}
Comments
10 pages, 9 figures