Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics

by   Raghav Kansal, et al.

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 Fréchet Inception distance, and the particle and jet feature-level 1-Wasserstein distance for the MNIST and jet datasets respectively.


page 1

page 2

page 3

page 4


Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis

We provide a bridge between generative modeling in the Machine Learning ...

Particle Cloud Generation with Message Passing Generative Adversarial Networks

In high energy physics (HEP), jets are collections of correlated particl...

Particle Graph Autoencoders and Differentiable, Learned Energy Mover's Distance

Autoencoders have useful applications in high energy physics in anomaly ...

Generative Ensemble-Regression: Learning Stochastic Dynamics from Discrete Particle Ensemble Observations

We propose a new method for inferring the governing stochastic ordinary ...

A Mean-Field Theory for Kernel Alignment with Random Features in Generative Adversarial Networks

We propose a novel supervised learning method to optimize the kernel in ...

Generative Adversarial Networks with Inverse Transformation Unit

In this paper we introduce a new structure to Generative Adversarial Net...

LHC analysis-specific datasets with Generative Adversarial Networks

Using generative adversarial networks (GANs), we investigate the possibi...