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. Uncertainties in the training data add towards the complexity of performing machine learning tasks such as event classification. However, it has been shown that adversarial neural networks can be used to decorrelate the trained model from systematic uncertainties that affect the kinematics on an event-by-event basis. Here we show that this approach can be extended to theoretical uncertainties (e.g. renormalization and factorization scale uncertainties) that affect the event sample as a whole. The result of the adversarial training is a classifier that is insensitive to these uncertainties by having learned to avoid regions of phase space (or feature space) that are affected by the uncertainties. This paves the way to a more reliable event classification, 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.
|Preferred contribution length||20 minutes|