Set-Conditional Set Generation for Particle Physics
arXiv:2211.06406 · doi:10.1088/2632-2153/ad035b
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.
10 pages, 9 figures
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