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.
Highlights
First indications of neutrino oscillations using atmospheric neutrinos were presented by the Super-Kamiokande experiment in Japan in 1998 [1]
charge current quasielastic events (CCQE) is the most probable reaction at T2K energies, and the one dominating the statistical sensitivity of the experiment, where the neutrino transforms into a muon exchanging a neutron into a proton (ν þ n → μ þ p)
III C, based on the standard histogram approach but tweaked in order to attain the limit of unbinned likelihood, closely related to what is obtained by neural spline flows
Summary
First indications of neutrino oscillations using atmospheric neutrinos were presented by the Super-Kamiokande experiment in Japan in 1998 [1]. There are, some limitations to these methods Both approaches are based as of today on a binned likelihood algorithm which might limit the sensitivity of the experiment and some of them impose a Gaussian dependency in some of the nuisance parameters affecting the precision of the results and correctness of the evaluated uncertainties. They require very intensive CPU processing time, which is a limiting factor that reduces the flexibility of the statistical analysis and checks, and introduces strong constrains on the delivery of the results. We will discuss in this paper the basic concepts of the method and show the potential with a simplified example
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