# Generative Models for Simulation of KamLAND-Zen

arXiv: https://arxiv.org/abs/2312.14372 · HTML: https://mlsanity.com/paper/2312.14372/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 7 references that are in the map.
- First posted on arXiv 22 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [PMT Waveform Simulation and Reconstruction with Conditional Diffusion Network](https://mlsanity.com/paper/2602.05767/) (2026)

## Abstract (arXiv)

The next generation of searches for neutrinoless double beta decay (0nubetabeta) 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 0nubetabeta is critical. Traditional Monte Carlo (MC) simulations can be supplemented by machine-learning-based generative models. In this work, we describe the performance of generative models designed for monolithic liquid scintillator detectors like KamLAND to produce highly accurate simulation data without a predefined physics model. We demonstrate its 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.