https://doi.org/10.1140/epjc/s10052-018-6511-8
Regular Article - Theoretical Physics
Machine learning uncertainties with adversarial neural networks
1
SUPA, School of Physics and Astronomy, University of Glasgow, Glasgow, G12 8QQ, UK
2
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
3
Department of Physics, Institute for Particle Physics Phenomenology, Durham University, Durham, DH1 3LE, UK
* e-mail: christoph.englert@glasgow.ac.uk
Received:
17
August
2018
Accepted:
9
December
2018
Published online:
3
January
2019
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.
© The Author(s), 2018
