Data-driven background model for the CUORE experiment

TL;DR

This paper develops a Bayesian-based background model for the CUORE experiment, enabling precise reconstruction of radioactive backgrounds and informing future low-background experiments like CUPID.

Contribution

The paper introduces a novel Bayesian fit approach to model and analyze the spatial and temporal background variations in CUORE data.

Findings

Achieved detection sensitivity of 10 nBq kg$^{-1}$ for bulk activities.

Detected surface contamination levels as low as 0.1 nBq cm$^{-2}$.

Provided background estimates that align with prior radio-assay data.

Abstract

We present the model we developed to reconstruct the CUORE radioactive background based on the analysis of an experimental exposure of 1038.4 kg yr. The data reconstruction relies on a simultaneous Bayesian fit applied to energy spectra over a broad energy range. The high granularity of the CUORE detector, together with the large exposure and extended stable operations, allow for an in-depth exploration of both spatial and time dependence of backgrounds. We achieve high sensitivity to both bulk and surface activities of the materials of the setup, detecting levels as low as 10 nBq kg$^{-1}$ and 0.1 nBq cm$^{-2}$, respectively. We compare the contamination levels we extract from the background model with prior radio-assay data, which informs future background risk mitigation strategies. The results of this background model play a crucial role in constructing the background budget for the CUPID experiment as it will exploit the same CUORE infrastructure.