Machine learning uncertainties with adversarial neural networks

Englert, C. , Galler, P. , Harris, P. and Spannowsky, M. (2018) Machine learning uncertainties with adversarial neural networks. European Physical Journal C, 79, 4. (doi: 10.1140/epjc/s10052-018-6511-8)

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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:Articles
Glasgow Author(s) Enlighten ID:Galler, Dr Peter and Englert, Professor Christoph
Authors: Englert, C., Galler, P., Harris, P., and Spannowsky, M.
College/School:College of Science and Engineering > School of Physics and Astronomy
Journal Name:European Physical Journal C
ISSN (Online):1434-6052
Copyright Holders:Copyright © 2018 The Authors
First Published:First published in European Physical Journal C 79:4
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
729782Phenomenology from Lattice QCD and Collider PhysicsChristine DaviesScience & Technology Facilities Council (STFC)ST/P000746/1S&E P&A - PHYSICS & ASTRONOMY