2000 character limit reached
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.