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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2024
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| Online Access: | https://hdl.handle.net/1721.1/155583 |
| _version_ | 1811082332148334592 |
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| author | Fu, Zhenghao Grant, Christopher Krawiec, Dominika M. Li, Aobo Winslow, Lindley A. |
| author_facet | 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. |
| first_indexed | 2024-09-23T12:01:27Z |
| format | Article |
| id | mit-1721.1/155583 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:01:27Z |
| publishDate | 2024 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1555832024-09-09T04:49:26Z Generative models for simulation of KamLAND-Zen Fu, Zhenghao Grant, Christopher Krawiec, Dominika M. Li, Aobo Winslow, Lindley A. 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 |
| work_keys_str_mv | AT fuzhenghao generativemodelsforsimulationofkamlandzen AT grantchristopher generativemodelsforsimulationofkamlandzen AT krawiecdominikam generativemodelsforsimulationofkamlandzen AT liaobo generativemodelsforsimulationofkamlandzen AT winslowlindleya generativemodelsforsimulationofkamlandzen |