Direct determination of the solar neutrino fluxes from solar neutrino data

TL;DR

This paper uses a Bayesian global analysis with Markov Chain Monte Carlo to directly determine solar neutrino fluxes from experimental data, testing Standard Solar Model predictions.

Contribution

It introduces a Bayesian MCMC method to extract solar neutrino fluxes and their uncertainties directly from data, without relying solely on model predictions.

Findings

Both low and high metallicity solar models fit the data well

The Bayesian approach provides posterior distributions for fluxes and parameters

Results support current solar neutrino and solar model understanding

Abstract

We determine the solar neutrino fluxes from a global analysis of the solar and terrestrial neutrino data in the framework of three-neutrino mixing. Using a Bayesian approach we reconstruct the posterior probability distribution function for the eight normalization parameters of the solar neutrino fluxes plus the relevant masses and mixing, with and without imposing the luminosity constraint. This is done by means of a Markov Chain Monte Carlo employing the Metropolis-Hastings algorithm. We also describe how these results can be applied to test the predictions of the Standard Solar Models. Our results show that, at present, both models with low and high metallicity can describe the data with good statistical agreement.