Jul 6 – 8, 2021
Europe/Zurich timezone

Invertible Networks or Partons to Detector and Back Again

Jul 6, 2021, 3:00 PM
20m

Speaker

Anja Butter

Description

For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects and QCD radiation to a pre-defined hard process, again with a per-event probabilistic interpretation over parton-level phase space.

Affiliation ITP Heidelberg
Academic Rank Postdoc

Authors

Anja Butter Gregor Kasieczka (Hamburg University (DE)) Marco Bellagente (Universität Heidelberg) Ramon Winterhalder (Universität Heidelberg) Tilman Plehn Ullrich Köthe

Presentation materials