95 research outputs found

    Search for Neutrinoless Double-Beta Decay in 136^{136}Xe with EXO-200

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    We report on a search for neutrinoless double-beta decay of 136^{136}Xe with EXO-200. No signal is observed for an exposure of 32.5 kg-yr, with a background of ~1.5 x 10^{-3} /(kg yr keV) in the ±1σ\pm 1\sigma region of interest. This sets a lower limit on the half-life of the neutrinoless double-beta decay T1/20νββT_{1/2}^{0\nu\beta\beta}(136^{136}Xe) > 1.6 x 1025^{25} yr (90% CL), corresponding to effective Majorana masses of less than 140-380 meV, depending on the matrix element calculation

    Investigation of radioactivity-induced backgrounds in EXO-200

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    The search for neutrinoless double-beta decay (0{\nu}{\beta}{\beta}) requires extremely low background and a good understanding of their sources and their influence on the rate in the region of parameter space relevant to the 0{\nu}{\beta}{\beta} signal. We report on studies of various {\beta}- and {\gamma}-backgrounds in the liquid- xenon-based EXO-200 0{\nu}{\beta}{\beta} experiment. With this work we try to better understand the location and strength of specific background sources and compare the conclusions to radioassay results taken before and during detector construction. Finally, we discuss the implications of these studies for EXO-200 as well as for the next-generation, tonne-scale nEXO detector.Comment: 9 pages, 7 figures, 3 table

    Deep Neural Networks for Energy and Position Reconstruction in EXO-200

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    We apply deep neural networks (DNN) to data from the EXO-200 experiment. In the studied cases, the DNN is able to reconstruct the relevant parameters - total energy and position - directly from raw digitized waveforms, with minimal exceptions. For the first time, the developed algorithms are evaluated on real detector calibration data. The accuracy of reconstruction either reaches or exceeds what was achieved by the conventional approaches developed by EXO-200 over the course of the experiment. Most existing DNN approaches to event reconstruction and classification in particle physics are trained on Monte Carlo simulated events. Such algorithms are inherently limited by the accuracy of the simulation. We describe a unique approach that, in an experiment such as EXO-200, allows to successfully perform certain reconstruction and analysis tasks by training the network on waveforms from experimental data, either reducing or eliminating the reliance on the Monte Carlo.Comment: Accepted version. 33 pages, 28 figure

    Search for nucleon decays with EXO-200

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    A search for instability of nucleons bound in 136^{136}Xe nuclei is reported with 223 kg⋅\cdotyr exposure of 136^{136}Xe in the EXO-200 experiment. Lifetime limits of 3.3×1023\times 10^{23} and 1.9×1023\times 10^{23} yrs are established for nucleon decay to 133^{133}Sb and 133^{133}Te, respectively. These are the most stringent to date, exceeding the prior decay limits by a factor of 9 and 7, respectively
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