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Machine learning uncertainties with adversarial neural networks.

Englert, Christoph and Galler, Peter and Harris, Philip and Spannowsky, Michael (2019) 'Machine learning uncertainties with adversarial neural networks.', European physical journal C., 79 (1). p. 4.

Abstract

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.

Item Type:Article
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1140/epjc/s10052-018-6511-8
Publisher statement:© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), 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.
Date accepted:09 December 2018
Date deposited:16 January 2019
Date of first online publication:03 January 2019
Date first made open access:No date available

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