Machine learning uncertainties with adversarial neural networks

Englert, Christoph (Aff1, 0000 0001 2193 314X, SUPA, School of Physics and Astronomy, University of Glasgow, G12 8QQ, Glasgow, UK) ; Galler, Peter (Aff1, 0000 0001 2193 314X, SUPA, School of Physics and Astronomy, University of Glasgow, G12 8QQ, Glasgow, UK) ; Harris, Philip (Aff2, 0000 0001 2341 2786, Department of Physics, Massachusetts Institute of Technology, 02139, Cambridge, MA, USA) ; Spannowsky, Michael (Aff3, 0000 0000 8700 0572, Department of Physics, Institute for Particle Physics Phenomenology, Durham University, DH1 3LE, Durham, UK)

03 January 2019

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.


Published in: EPJC 79 (2019) 4
Published by: Springer/Società Italiana di Fisica
DOI: 10.1140/epjc/s10052-018-6511-8
License: CC-BY-3.0



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