Deep Learning From Four Vectors
High Energy Physics - Experiment
2022-03-08 v1
Abstract
An early example of the ability of deep networks to improve the statistical power of data collected in particle physics experiments was the demonstration that such networks operating on lists of particle momenta (four-vectors) could outperform shallow networks using features engineered with domain knowledge. A benchmark case is described, with extensions to parameterized networks. A discussion of data handling and architecture is presented, as well as a description of how to incorporate physics knowledge into the network architecture.
Cite
@article{arxiv.2203.03067,
title = {Deep Learning From Four Vectors},
author = {Pierre Baldi and Peter Sadowski and Daniel Whiteson},
journal= {arXiv preprint arXiv:2203.03067},
year = {2022}
}
Comments
To appear in Artificial Intelligence for High Energy Physics, World Scientific Publishing