Machine Learning-Informed 3+1 Sterile Neutrino Global Fits using Posterior Density Estimation of Electron Disappearance Data

TL;DR

This paper applies machine learning-based posterior density estimation to perform global fits of sterile neutrino data, addressing computational challenges and providing a comprehensive Bayesian and frequentist analysis of electron neutrino disappearance experiments.

Contribution

It introduces a novel machine learning approach for global sterile neutrino fits, overcoming likelihood intractability and enabling Bayesian and frequentist interpretations.

Findings

Effective posterior density estimation for sterile neutrino parameters.

Comparison between Bayesian and frequentist inference methods.

Insights into the viability of sterile neutrinos as explanations for anomalies.

Abstract

Global analyses of particle physics data are integral for validating and scrutinizing published results of experiments. Global fits of anomalous oscillation data which search for one or more eV-scale sterile neutrinos are particularly challenging both to evaluate and to reconcile in the global picture. Fits (especially joint ones) to oscillation data suffer from significant computational burdens, such as likelihood intractability, making traditional Markov Chain-Monte Carlo all but impossible. Given evidence both supporting and challenging beyond Standard Model physics across neutrino experiments of various baselines, energies, and detection techniques, the global search for sterile neutrinos requires additional tools in order to determine whether sterile neutrinos remain a viable solution to unexplained anomalies. Furthermore, both a Bayesian and frequentist interpretation of sterile neutrino data is needed for a complete assessment of longstanding tensions in the field. Techniques from the machine learning subfield of simulation-based inference have a natural application to such a problem. In this contribution, we illustrate some of the outstanding questions of the global picture of light sterile neutrinos by focusing on experiments searching with the disappearance of electron (anti)neutrinos, and look to posterior density estimation strategies to craft answers, including comparisons to a machine-learning-based frequentist approach.