Data-driven background model for the CUORE experiment
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 and 0.1 nBq cm, 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.
Cite
@article{arxiv.2405.17937,
title = {Data-driven background model for the CUORE experiment},
author = {CUORE Collaboration and D. Q. Adams and C. Alduino and K. Alfonso and F. T. Avignone and O. Azzolini and G. Bari and F. Bellini and G. Benato and M. Beretta and M. Biassoni and A. Branca and C. Brofferio and C. Bucci and J. Camilleri and A. Caminata and A. Campani and J. Cao and S. Capelli and C. Capelli and L. Cappelli and L. Cardani and P. Carniti and N. Casali and E. Celi and D. Chiesa and M. Clemenza and O. Cremonesi and R. J. Creswick and A. D'Addabbo and I. Dafinei and F. Del Corso and S. Dell'Oro and S. Di Domizio and S. Di Lorenzo and T. Dixon and V. Dompè and D. Q. Fang and G. Fantini and M. Faverzani and E. Ferri and F. Ferroni and E. Fiorini and M. A. Franceschi and S. J. Freedman and S. H. Fu and B. K. Fujikawa and S. Ghislandi and A. Giachero and M. Girola and L. Gironi and A. Giuliani and P. Gorla and C. Gotti and P. V. Guillaumon and T. D. Gutierrez and K. Han and E. V. Hansen and K. M. Heeger and D. L. Helis and H. Z. Huang and G. Keppel and Yu. G. Kolomensky and R. Kowalski and R. Liu and L. Ma and Y. G. Ma and L. Marini and R. H. Maruyama and D. Mayer and Y. Mei and M. N. Moore and T. Napolitano and M. Nastasi and C. Nones and E. B. Norman and A. Nucciotti and I. Nutini and T. O'Donnell and M. Olmi and B. T. Oregui and J. L. Ouellet and S. Pagan and C. E. Pagliarone and L. Pagnanini and M. Pallavicini and L. Pattavina and M. Pavan and G. Pessina and V. Pettinacci and C. Pira and S. Pirro and I. Ponce and E. G. Pottebaum and S. Pozzi and E. Previtali and A. Puiu and S. Quitadamo and A. Ressa and C. Rosenfeld and B. Schmidt and V. Sharma and V. Singh and M. Sisti and D. Speller and P. T. Surukuchi and L. Taffarello and C. Tomei and J. A Torres and K. J. Vetter and M. Vignati and S. L. Wagaarachchi and B. Welliver and J. Wilson and K. Wilson and L. A. Winslow and S. Zimmermann and S. Zucchelli},
journal= {arXiv preprint arXiv:2405.17937},
year = {2024}
}