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

arXiv: https://arxiv.org/abs/2002.09436 · HTML: https://mlsanity.com/paper/2002.09436/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2020.

## Built on by (most cited first, 3 of 3)

- [Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning](https://mlsanity.com/paper/2507.23297/) (2025)
- [ASBI: Leveraging Informative Real-World Data for Active Black-Box Simulator Tuning](https://mlsanity.com/paper/2510.15331/) (2025)
- [Neutrino Oscillation Parameter Estimation Using Structured Hierarchical Transformers](https://mlsanity.com/paper/2603.22342/) (2026)

## Abstract (arXiv)

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.