Speaker
Nina Elmer
(Heidelberg University)
Description
Neural networks for LHC physics must be accurate, reliable, and well-controlled. This requires them to provide both precise predictions and reliable quantification of uncertainties - including those arising from the network itself or the training data. Bayesian networks or (repulsive) ensembles provide frameworks that enable learning systematic and statistical uncertainties. We investigate different aspects of repulsive ensembles: the dependence on the repulsive kernel, biases for small training datasets, and systematic uncertainty estimation for the overall ensemble.
Authors
Henning Bahl
Nina Elmer
(Heidelberg University)
Ramon Winterhalder
(Università degli Studi di Milano)
Tilman Plehn
(Heidelberg University)