Analysis and packaging of radiochemical solar neutrino data. 1. Bayesian approach

TL;DR

This paper proposes a Bayesian method for analyzing radiochemical solar neutrino data, addressing issues with traditional maximum-likelihood estimates such as negative flux values and inconsistent global estimates.

Contribution

It introduces a Bayesian approach to improve data packaging and analysis, overcoming limitations of conventional maximum-likelihood procedures in solar neutrino experiments.

Findings

Bayesian method avoids negative flux estimates.

Provides consistent global flux estimates.

Enhances data summary accuracy.

Abstract

According to current practice, the results of each run of a radiochemical solar neutrino experiment comprise an estimate of the flux and upper and lower error estimates. These estimates are derived by a maximum-likelihood procedure from the times of decay events in the analysis chamber. This procedure has the following shortcomings: (a) Published results sometimes include negative flux estimates. (b) Even if the flux estimate is non-negative, the probability distribution function implied by the flux and error estimates will extend into negative territory; and (c) The overall flux estimate derived from the results of a sequence of runs may differ substantially from an estimate made by a global analysis of all of the timing data taken together. These defects indicate that the usual packaging of data in radiochemical solar neutrino experiments provides an inadequate summary of the data, which implies a loss of information. This article reviews this problem from a Bayesian perspective, and suggests an alternative scheme for the packaging of radiochemical solar neutrino data, which is we believe free from the above objections.