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Deutsch Intern
  • [Translate to Englisch:] Seminar Elementarteilchenphysik
Theoretical Physics II

Prof. Dr. rer. nat. Tilman Plehn

Universität Heidelberg

O n l i n e   T a l k : 29-April-2021

Invertible and Generative Networks for LHC Theory

Abstract

LHC physics is a unique field in the sense that we compare vast and highly complex data sets with precise first-principles predictions. These predictions usually rely on Monte Carlo simulations. I will show how generative neural networks can supplement these simulations and discuss conceptional advantages of this method. I will then explain how generative networks can invert event simulations. Flow-based invertible networks allow us to invert or unfold individual detector simulations of QCD parton showers in a mathemacially consistent manner. That means that they predict calibrated probability distributions in parton-level phase space for individual observed events. Finally, I will illustrate how the same networks can infer the structure of QCD splittings forming jets.