Machine learning uncertainties with adversarial neural networks
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10052_2018_Article_6511.pdf
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Author(s)
Englert, Christoph
Galler, Peter
Spannowsky, Michael
Harris, Philip Coleman
Date Issued
January 2019
Journal
The European Physical Journal C
Publisher
Springer Berlin Heidelberg
Citation
Englert, Christoph, et al. “Machine Learning Uncertainties with Adversarial Neural Networks.” The European Physical Journal C, vol. 79, no. 1, Jan. 2019. © 2018 The Authors
Version
Final published version
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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1140/epjc/s10052-018-6511-8
