Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows
This work addresses the challenge of likelihood-free inference in experimental neutrino physics, but it appears incremental as it adapts an existing neural density estimation algorithm to a specific domain.
The paper tackled the problem of measuring neutrino oscillation parameters in Long Baseline experiments by applying Neural Spline Flows for likelihood-free inference, specifically in the muon neutrino disappearance analysis at T2K, resulting in a developed method adapted to physics parameter inference.
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.