Set-conditional set generation for particle physics - Nathalie Soybelman; Nilotpal Kakati; Lukas Heinrich; Francesco Armando Di Bello; Etienne Dreyer; Sanmay Ganguly; Eilam Gross; Marumi Kado; Jonathan Shlomi (Machine Learning: Science and Technology, 2023)
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.