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arXiv · 2002.09436

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

Sebastian Pina-Otey·Federico Sánchez·Vicens Gaitan·Thorsten Lux
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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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Sebastian Pina-Otey, Federico Sánchez, Vicens Gaitan, Thorsten Lux. 2020-02-21. Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows. https://doi.org/10.1103/physrevd.101.113001

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