Jul 6 – 8, 2021
Europe/Zurich timezone

Equivariant energy flow networks for jet tagging

Jul 6, 2021, 9:20 AM
20m

Speaker

Ayodele Ore (The University of Melbourne)

Description

The Energy Flow Network (EFN) is a neural network architecture that represents jets as point clouds and enforces infrared and collinear (IRC) safety on its outputs. In this talk, I will introduce a new variant of the EFN architecture based on the Deep Sets formalism, incorporating permutation-equivariant layers. I will discuss the conditions under which IRC safety can be maintained in the new architecture and showcase the performance of these networks on the canonical example of W-boson tagging. The equivariant EFNs have similar performance to Particle Flow Networks, which are superior to standard EFNs. Finally I will comment on how the equivariant networks sculpt the jet mass compared to unaugmented EFNs.

Affiliation The University of Melbourne
Academic Rank PhD Student

Primary authors

Ayodele Ore (The University of Melbourne) Matthew Dolan (University of Melbourne)

Presentation materials