Eur. Phys. J. C (2024) 84: 651
https://doi.org/10.1140/epjc/s10052-024-12980-7

Regular Article - Experimental Physics

Generative models for simulation of KamLAND-Zen

¹ Laboratory of Nuclear Science, Massachusetts Institute of Technology, 77 Massachusetts Ave, 02139, Cambridge, MA, USA
² Department of Physics, Boston University, 590 Commonwealth Ave, 02215, Boston, MA, USA
³ Department of Physics, University of Warwick, CV4 7AL, Coventry, UK
⁴ Halıcıoğlu Data Science Institute, University of California San Diego, 9500 Gilman Dr, 92093, La Jolla, CA, USA
⁵ Department of Physics, University of California San Diego, 9500 Gilman Dr, 92093, La Jolla, CA, USA

^a fuzh@mit.edu
^d liaobo77@ucsd.edu

Received: 2 January 2024
Accepted: 3 June 2024
Published online: 27 June 2024

Abstract

The next generation of searches for neutrinoless double beta decay ($0\nu \beta \beta$) are poised to answer deep questions on the nature of neutrinos and the source of the Universe’s matter–antimatter asymmetry. They will be looking for event rates of less than one event per ton of instrumented isotope per year. To claim discovery, accurate and efficient simulations of detector events that mimic $0\nu \beta \beta$ is critical. Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. This work describes the performance of generative models that we designed for monolithic liquid scintillator detectors like KamLAND to produce accurate simulation data without a predefined physics model. We present their current ability to recover low-level features and perform interpolation. In the future, the results of these generative models can be used to improve event classification and background rejection by providing high-quality abundant generated data.