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 |
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Academic Rank | Postdoctoral researcher |
Authors
Alex Roman
(University of Florida)
Konstantin Matchev
(University of Florida (US))
Prasanth Shyamsundar
(Fermi National Accelerator Laboratory)