Results from a search for neutrinoless double-beta decay ($0\nu \beta \beta$) of ${}^{136}\mathrm{Xe}$ are presented using the first year of data taken with the upgraded EXO-200 detector. Relative to previous searches by EXO-200, the energy resolution of the detector has been improved to $\sigma /E=1.23\%$, the electric field in the drift region has been raised by 50%, and a system to suppress radon in the volume between the cryostat and lead shielding has been implemented. In addition, analysis techniques that improve topological discrimination between $0\nu \beta \beta$ and background events have been developed. Incorporating these hardware and analysis improvements, the median 90% confidence level $0\nu \beta \beta$ half-life sensitivity after combining with the full data set acquired before the upgrade has increased twofold to $3.7\times {10}^{25}\mathrm{yr}$. 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 $1.8\times {10}^{25}\mathrm{yr}$ at the 90% confidence level.

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.

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

Searches for double beta decay of ${}^{134}\mathrm{Xe}$ were performed with EXO-200, a single-phase liquid xenon detector designed to search for neutrinoless double beta decay of ${}^{136}\mathrm{Xe}$. Using an exposure of $29.6\mathrm{kg}·\mathrm{yr}$, the lower limits of ${\mathrm{T}}_{1/2}^{2\nu \beta \beta }>8.7\times {10}^{20}\mathrm{yr}$ and ${\mathrm{T}}_{1/2}^{0\nu \beta \beta }>1.1\times {10}^{23}\mathrm{yr}$ at 90% confidence level were derived, with corresponding half-life sensitivities of $1.2\times {10}^{21}\mathrm{yr}$ and $1.9\times {10}^{23}\mathrm{yr}$. These limits exceed those in the literature for ${}^{134}\mathrm{Xe}$, improving by factors of nearly $10^5$ and 2 for the two antineutrino and neutrinoless modes, respectively.

Objective. This study presents and evaluates a robust Monte Carlo-based scatter correction (SC) method for long axial field of view (FOV) and total-body positron emission tomography (PET) using the uEXPLORER total-body PET/CT scanner. Approach. Our algorithm utilizes the Monte Carlo (MC) tool SimSET to compute SC factors in between individual image reconstruction iterations within our in-house list-mode and time-of-flight-based image reconstruction framework. We also introduced a unique scatter scaling technique at the detector block-level for optimal estimation of the scatter contribution in each line of response. First image evaluations were derived from phantom data spanning the entire axial FOV along with image data from a human subject with a large body mass index. Data was evaluated based on qualitative inspections, and contrast recovery, background variability, residual scatter removal from cold regions, biases and axial uniformity were quantified and compared to non-scatter-corrected images. Main results. All reconstructed images demonstrated qualitative and quantitative improvements compared to non-scatter-corrected images: contrast recovery coefficients improved by up to 17.2% and background variability was reduced by up to 34.3%, and the residual lung error was between 1.26% and 2.08%. Low biases throughout the axial FOV indicate high quantitative accuracy and axial uniformity of the corrections. Up to 99% of residual activity in cold areas in the human subject was removed, and the reliability of the method was demonstrated in challenging body regions like in the proximity of a highly attenuating knee prosthesis. Significance. The MC SC method employed was demonstrated to be accurate and robust in TB-PET. The results of this study can serve as a benchmark for optimizing the quantitative performance of future SC techniques.