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This content will become publicly available on June 1, 2025

Title: Generative models for simulation of KamLAND-Zen
Abstract The next generation of searches for neutrinoless double beta decay ($$0 \nu \beta \beta $$ 0 ν β β ) 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$$0 \nu \beta \beta $$ 0 ν β β 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.  more » « less
Award ID(s):
2310130 2110720
PAR ID:
10520040
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
EPJC
Date Published:
Journal Name:
The European Physical Journal C
Volume:
84
Issue:
6
ISSN:
1434-6052
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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