Skip to main content

Research Repository

Advanced Search

Machine learning uncertainties with adversarial neural networks

Englert, Christoph; Galler, Peter; Harris, Philip; Spannowsky, Michael

Machine learning uncertainties with adversarial neural networks Thumbnail


Christoph Englert

Peter Galler

Philip Harris


Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics of a theoretical model are not fully understood. Using adversarial networks, we include a priori known sources of systematic and theoretical uncertainties during the training. This paves the way to a more reliable event classification on an event-by-event basis, as well as novel approaches to perform parameter fits of particle physics data. We demonstrate the benefits of the method explicitly in an example considering effective field theory extensions of Higgs boson production in association with jets.


Englert, C., Galler, P., Harris, P., & Spannowsky, M. (2019). Machine learning uncertainties with adversarial neural networks. The European Physical Journal C, 79(1), Article 4.

Journal Article Type Article
Acceptance Date Dec 9, 2018
Online Publication Date Jan 3, 2019
Publication Date Jan 3, 2019
Deposit Date Jan 16, 2019
Publicly Available Date Jan 16, 2019
Journal European Physical Journal C: Particles and Fields
Print ISSN 1434-6044
Electronic ISSN 1434-6052
Publisher SpringerOpen
Peer Reviewed Peer Reviewed
Volume 79
Issue 1
Article Number 4


Published Journal Article (1.1 Mb)

Publisher Licence URL

Copyright Statement
© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

You might also like

Downloadable Citations