Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows
High Energy Physics - Phenomenology
2020-07-01 v3 Machine Learning
High Energy Physics - Experiment
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.
Cite
@article{arxiv.2002.09436,
title = {Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows},
author = {Sebastian Pina-Otey and Federico Sánchez and Vicens Gaitan and Thorsten Lux},
journal= {arXiv preprint arXiv:2002.09436},
year = {2020}
}
Comments
10 pages, 3 figures