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Search for Neutrinoless Double-Beta Decay with the Complete EXO-200 Dataset

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arxiv 1906.02723 v3 pith:5TLETNK2 submitted 2019-06-06 hep-ex nucl-ex

classification hep-exnucl-ex
keywords betabeencdotconfidencedatasetdecaydetectordouble-beta
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

A search for neutrinoless double-beta decay ($0\nu\beta\beta$) in $^{136}$Xe is performed with the full EXO-200 dataset using a deep neural network to discriminate between $0\nu\beta\beta$ and background events. Relative to previous analyses, the signal detection efficiency has been raised from 80.8% to 96.4$\pm$3.0% and the energy resolution of the detector at the Q-value of $^{136}$Xe $0\nu\beta\beta$ has been improved from $\sigma/E=1.23\%$ to $1.15\pm0.02\%$ with the upgraded detector. Accounting for the new data, the median 90% confidence level $0\nu\beta\beta$ half-life sensitivity for this analysis is $5.0 \cdot 10^{25}$ yr with a total $^{136}$Xe exposure of 234.1 kg$\cdot$yr. No statistically significant evidence for $0\nu\beta\beta$ is observed, leading to a lower limit on the $0\nu\beta\beta$ half-life of $3.5\cdot10^{25}$ yr at the 90% confidence level.

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Forward citations

Cited by 4 Pith papers

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    Renormalization-group mixing lets heavy-quark dimension-seven operators feed neutrinoless double-beta decay, giving some of the strongest current bounds on these new-physics operators.

  3. Sensitivity of nEXO to $^{136}$Xe Charged-Current Interactions: Background-free Searches for Solar Neutrinos and Fermionic Dark Matter

    nucl-ex 2025-06 conditional novelty 6.0 of 10

    A new delayed-coincidence tag from 136Cs isomers could let the nEXO detector reject backgrounds down to 10^-9 and measure CNO solar neutrinos, the 7Be neutrino energy, and sub-MeV fermionic dark matter.

  4. Right-handed neutrinos: seesaw models and signatures

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    A pedagogical review that explains how adding right-handed neutrinos can generate small neutrino masses through seesaw mechanisms and what experimental signatures such models predict.

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