An assay-based background projection for the MAJORANA DEMONSTRATOR using Monte Carlo Uncertainty Propagation

TL;DR

This paper introduces a Bayesian Monte Carlo method to accurately project background levels in neutrinoless double-beta decay experiments, specifically applied to the MAJORANA DEMONSTRATOR, improving background estimation precision.

Contribution

A novel Bayesian framework utilizing Monte Carlo uncertainty propagation for background projection in $0 uetaeta$ experiments, integrating assay data and efficiencies.

Findings

Projected background index of (8.95 ± 0.36) × 10^{-4} cts/(keV kg yr) for the DEMONSTRATOR.

Unified approach to combine assay-specific activities from multiple sources.

Enhanced accuracy in background estimation for neutrinoless double-beta decay experiments.

Abstract

The background index is an important quantity which is used in projecting and calculating the half-life sensitivity of neutrinoless double-beta decay ($0\nu\beta\beta$) experiments. A novel analysis framework is presented to calculate the background index using the specific activities, masses and simulated efficiencies of an experiment's components as distributions. This Bayesian framework includes a unified approach to combine specific activities from assay. Monte Carlo uncertainty propagation is used to build a background index distribution from the specific activity, mass and efficiency distributions. This analysis method is applied to the MAJORANA DEMONSTRATOR, which deployed arrays of high-purity Ge detectors enriched in $^{76}$Ge to search for $0\nu\beta\beta$. The framework projects a mean background index of $\left[8.95 \pm 0.36\right] \times 10^{-4}$cts/(keV kg yr) from $^{232}$Th and $^{238}$U in the DEMONSTRATOR's components.