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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.
The energy resolution of the EXO-200 detector is limited by electronics noise in the measurement of the scintillation response. Here we present a new technique to extract optimal scintillation energy measurements for signals split across multiple cha
The search for neutrinoless double-beta decay (0{ u}{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{ u}{beta}{beta} signal. W
EXO-200 is an experiment designed to search for double beta decay of $^{136}$Xe with a single-phase, liquid xenon detector. It uses an active mass of 110 kg of xenon enriched to 80.6% in the isotope 136 in an ultra-low background time projection cham
The EXO-200 experiment searched for neutrinoless double-beta decay of $^{136}$Xe with a single-phase liquid xenon detector. It used an active mass of 110 kg of 80.6%-enriched liquid xenon in an ultra-low background time projection chamber with ioniza
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in high energy