Generative models for simulation of KamLAND-Zen

The next generation of searches for neutrinoless double beta decay (0𝜈𝛽𝛽 ) 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 yea...

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Main Authors: Fu, Zhenghao, Grant, Christopher, Krawiec, Dominika M., Li, Aobo, Winslow, Lindley A.
Other Authors: Massachusetts Institute of Technology. Laboratory for Nuclear Science
Format: Article
Language:English
Published: Springer Science and Business Media LLC 2024
Online Access:https://hdl.handle.net/1721.1/155583
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author Fu, Zhenghao
Grant, Christopher
Krawiec, Dominika M.
Li, Aobo
Winslow, Lindley A.
author2 Massachusetts Institute of Technology. Laboratory for Nuclear Science
author_facet Massachusetts Institute of Technology. Laboratory for Nuclear Science
Fu, Zhenghao
Grant, Christopher
Krawiec, Dominika M.
Li, Aobo
Winslow, Lindley A.
author_sort Fu, Zhenghao
collection MIT
description The next generation of searches for neutrinoless double beta decay (0𝜈𝛽𝛽 ) 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 0𝜈𝛽𝛽 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.
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spelling mit-1721.1/1555832025-01-07T04:46:18Z Generative models for simulation of KamLAND-Zen Fu, Zhenghao Grant, Christopher Krawiec, Dominika M. Li, Aobo Winslow, Lindley A. Massachusetts Institute of Technology. Laboratory for Nuclear Science The next generation of searches for neutrinoless double beta decay (0𝜈𝛽𝛽 ) 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 0𝜈𝛽𝛽 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. 2024-07-10T19:24:17Z 2024-07-10T19:24:17Z 2024-06-27 2024-06-30T03:10:44Z Article http://purl.org/eprint/type/JournalArticle 1434-6052 https://hdl.handle.net/1721.1/155583 Fu, Z., Grant, C., Krawiec, D.M. et al. Generative models for simulation of KamLAND-Zen. Eur. Phys. J. C 84, 651 (2024). PUBLISHER_CC en 10.1140/epjc/s10052-024-12980-7 The European Physical Journal C Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer Science and Business Media LLC Springer Berlin Heidelberg
spellingShingle Fu, Zhenghao
Grant, Christopher
Krawiec, Dominika M.
Li, Aobo
Winslow, Lindley A.
Generative models for simulation of KamLAND-Zen
title Generative models for simulation of KamLAND-Zen
title_full Generative models for simulation of KamLAND-Zen
title_fullStr Generative models for simulation of KamLAND-Zen
title_full_unstemmed Generative models for simulation of KamLAND-Zen
title_short Generative models for simulation of KamLAND-Zen
title_sort generative models for simulation of kamland zen
url https://hdl.handle.net/1721.1/155583
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AT krawiecdominikam generativemodelsforsimulationofkamlandzen
AT liaobo generativemodelsforsimulationofkamlandzen
AT winslowlindleya generativemodelsforsimulationofkamlandzen