Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
Data Analysis, Statistics and Probability
2021-02-02 v4 Machine Learning
High Energy Physics - Experiment
High Energy Physics - Phenomenology
Computational Physics
Abstract
We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit images and jets of particles in proton-proton collisions like those at the LHC. We find the model successfully generates sparse MNIST digits and particle jet data. We quantify agreement between real and generated data with a graph-based Fr\'echet Inception distance, and the particle and jet feature-level 1-Wasserstein distance for the MNIST and jet datasets respectively.
Keywords
Cite
@article{arxiv.2012.00173,
title = {Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics},
author = {Raghav Kansal and Javier Duarte and Breno Orzari and Thiago Tomei and Maurizio Pierini and Mary Touranakou and Jean-Roch Vlimant and Dimitrios Gunopulos},
journal= {arXiv preprint arXiv:2012.00173},
year = {2021}
}
Comments
9 pages, 4 figures, 4 tables, To appear in Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)