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Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows (2002.09436v3)

Published 21 Feb 2020 in hep-ph, cs.LG, and hep-ex

Abstract: In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression. We discuss the application of Neural Spline Flows, a neural density estimation algorithm, to the likelihood-free inference problem of the measurement of neutrino oscillation parameters in Long Baseline neutrino experiments. A method adapted to physics parameter inference is developed and applied to the case of the disappearance muon neutrino analysis at the T2K experiment.

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