@article{9cac0314ad2f4e8694365f6cd3fcdbe7,
title = "Set-conditional set generation for particle physics",
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.",
author = "Nathalie Soybelman and Nilotpal Kakati and Lukas Heinrich and {Di Bello}, {Francesco Armando} and Etienne Dreyer and Sanmay Ganguly and Eilam Gross and Marumi Kado and Jonathan Shlomi",
note = "The authors would like to thank Kyle Cranmer for the fruitful discussion and comments on the manuscript. E D is supported by the Zuckerman STEM Leadership Program. S G is partially supported by the Institute of AI and Beyond for the University of Tokyo. E G is supported by the Israel Science Foundation (ISF), Grant No. 2871/19 Centers of Excellence. L H is supported by the Excellence Cluster ORIGINS, which is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy-EXC-2094-390783311.",
year = "2023",
month = dec,
day = "1",
doi = "10.1088/2632-2153/ad035b",
language = "الإنجليزيّة",
volume = "4",
journal = "Machine Learning: Science and Technology",
issn = "2632-2153",
publisher = "Institute of Physics Publishing",
number = "4",
}