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Register a new userTrace radioactive impurities in final construction materials for EXO-200
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We report results from a systematic measurement campaign conducted to identify low radioactivity materials for the construction of the EXO-200 double beta decay experiment. Partial results from this campaign have already been reported in a 2008 paper by the EXO collaboration. Here we release the remaining data, collected since 2007, to the public. The data reported were obtained using a variety of analytic techniques. The measurement sensitivities are among the best in the field. Construction of the EXO-200 detector has been concluded, and Phase-I data was taken from 2011 to 2014. The detectors extremely low background implicitly verifies the measurements and the analysis assumptions made during construction and reported in this paper.
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The Enriched Xenon Observatory (EXO) will search for double beta decays of 136Xe. We report the results of a systematic study of trace concentrations of radioactive impurities in a wide range of raw materials and finished parts considered for use in the construction of EXO-200, the first stage of the EXO experimental program. Analysis techniques employed, and described here, include direct gamma counting, alpha counting, neutron activation analysis, and high-sensitivity mass spectrometry.
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.
We describe purity measurements of the natural and enriched xenon stockpiles used by the EXO-200 double beta decay experiment based on a mass spectrometry technique. The sensitivity of the spectrometer is enhanced by several orders of magnitude by the presence of a liquid nitrogen cold trap, and many impurity species of interest can be detected at the level of one part-per-billion or better. We have used the technique to screen the EXO-200 xenon before, during, and after its use in our detector, and these measurements have proven useful. This is the first application of the cold trap mass spectrometry technique to an operating physics experiment.
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.
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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