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Abstract A new search for two-neutrino double-beta (2νββ) decay of136Xe to theexcited state of136Ba 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 2νββdecay to thestate of136Ba isyr using a total136Xe exposure of 234.1 kg yr. No statistically significant evidence for 2νββdecay to thestate is observed, leading to a lower limit ofyr at 90% confidence level, improved by 70% relative to the current world's best constraint.
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JUNO sensitivity on proton decay p → ν K + searches*
Abstract The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in themode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay viais 36.9% ± 4.9% with a background level ofevents after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure isyears, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies.
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- Award ID(s):
- 2111546
- PAR ID:
- 10541993
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- Institute of Physics
- Date Published:
- Journal Name:
- Chinese Physics C
- Volume:
- 47
- Issue:
- 11
- ISSN:
- 1674-1137
- Page Range / eLocation ID:
- 113002
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1674-1137/ace9c6
