Speaker
Lorenz Vogel
(Institute for Theoretical Physics (ITP), Heidelberg University, Germany)
Description
Calibrated learned uncertainties are a key requirement also for generative neural networks in LHC physics. For a toy model with an explicit likelihood we show how a heteroscedastic and a Bayesian normalizing flow learn the systematic and statistical uncertainties on the underlying phase space density. Without an explicit likelihood we train the heteroscedastic loss on a classifier-reweighted approximate generative network. We illustrate our comprehensive approach for top pair events and show how a conditional heteroscedastic flow propagates calibrated uncertainties to all phase space directions.
Authors
Anja Butter
(LPNHE, Sorbonne Université, Université Paris Cité, CNRS/IN2P3, Paris, France, Institut für Theoretische Physik, Universität Heidelberg, Germany)
Sascha Diefenbacher
(ITP Heidelberg)
Tilman Plehn
(Institut für Theoretische Physik, Universität Heidelberg; Interdisciplinary Center for Scientific Computing (IWR), Universität Heidelberg)
Lorenz Vogel
(Institute for Theoretical Physics (ITP), Heidelberg University, Germany)