Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows

02/21/2020

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by   Sebastian Pina-Otey, et al.

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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 Base Line 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.