Abstract
A new search for two-neutrino double-beta (2νββ) decay of 136Xe to the excited state of 136Ba is performed with the full EXO-200 dataset. A deep learning-based convolutional neural network is used to discriminate signal from background events. Signal detection efficiency is increased relative to previous searches by EXO-200 by more than a factor of two. With the addition of the Phase II dataset taken with an upgraded detector, the median 90% confidence level half-life sensitivity of 2νββ decay to the state of 136Ba is yr using a total 136Xe exposure of 234.1 kg yr. No statistically significant evidence for 2νββ decay to the state is observed, leading to a lower limit of yr at 90% confidence level, improved by 70% relative to the current world's best constraint.
Original language | English |
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Article number | 103001 |
Journal | Chinese Physics C |
Volume | 47 |
Issue number | 10 |
DOIs | |
State | Published - Oct 2023 |
Bibliographical note
Publisher Copyright:© 2023 Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Sciences and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
Funding
EXO-200 is supported by DOE and NSF in the United States, NSERC in Canada, SNF in Switzerland, IBS in Korea, DFG in Germany, and CAS in China. EXO-200 data analysis and simulation uses resources of the National Energy Research Scientific Computing Center (NERSC)
Funders | Funder number |
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National Science Foundation Arctic Social Science Program | |
U.S. Department of Energy EPSCoR | |
Natural Sciences and Engineering Research Council of Canada | |
Deutsche Forschungsgemeinschaft | |
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung | |
Chinese Academy of Sciences |
Keywords
- EXO-200 experiment
- excited state
- neutrinoless double beta decay
ASJC Scopus subject areas
- Nuclear and High Energy Physics
- Instrumentation
- Astronomy and Astrophysics