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Register a new userDeep 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.
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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 channels in the presence of correlated noise. The implementation of these techniques improves the energy resolution of the detector at the neutrinoless double beta decay Q-value from $left[1.9641pm 0.0039right]%$ to $left[1.5820pm 0.0044right]%$.
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. We report on studies of various {beta}- and {gamma}-backgrounds in the liquid- xenon-based EXO-200 0{ u}{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.
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 chamber capable of simultaneous detection of ionization and scintillation. This paper describes the EXO-200 detector with particular attention to the most innovative aspects of the design that revolve around the reduction of backgrounds, the efficient use of the expensive isotopically enriched xenon, and the optimization of the energy resolution in a relatively large volume.
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 ionization and scintillation detection and readout. This paper describes the design and performance of the various support systems necessary for detector operation, including cryogenics, xenon handling, and controls. Novel features of the system were driven by the need to protect the thin-walled detector chamber containing the liquid xenon, to achieve high chemical purity of the Xe, and to maintain thermal uniformity across the detector.
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 collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.
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