Jul 6 – 8, 2021
Europe/Zurich timezone

Uncertainties Associated with GAN Generated Datasets

Speaker

Prasanth Shyamsundar (Fermi National Accelerator Laboratory)

Description

Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets at a reduced computational cost. In this talk we will present an argument cautioning against the usage of this method to meet the simulation requirements of an experiment, namely that data generated by a GAN cannot statistically be better than the data it was trained on.

We will also state and prove a theorem that limits the ability of GANs to replace traditional simulators in collider physics.

Affiliation Fermi National Accelerator Laboratory
Academic Rank Postdoctoral researcher

Primary authors

Alex Roman (University of Florida) Konstantin Matchev (University of Florida (US)) Prasanth Shyamsundar (Fermi National Accelerator Laboratory)

Presentation materials