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.
Primary authors
Marco Bellagente
(Universität Heidelberg)
Anja Butter
Gregor Kasieczka
(Hamburg University (DE))
Tilman Plehn
Armand Rousselot
Ramon Winterhalder
(Universität Heidelberg)
Lynton Ardizzone
Ullrich Köthe
