ATLAS Detector Paper Back-Up Note: Electrons and Photons

This is the supporting note to the ATLAS Detector paper for electron and photon reconstruction with the Inner Detector. It describes the software used to produce the results presented in the ATLAS Detector paper

'Searching for a needle in a haystack;' A Ba-tagging approach for an upgraded nEXO experiment

nEXO is a proposed experiment that will search for neutrinoless double-beta decay (0$\nu\beta\beta$) in 5-tonnes of liquid xenon (LXe), isotopically enriched in $^{136}$Xe. A technique called Ba-tagging is being developed as a potential future upgrade for nEXO to detect the $^{136}$Xe double-beta decay daughter isotope, $^{136}$Ba. An efficient Ba-tagging technique has the potential to boost nEXO's 0$\nu\beta\beta$ sensitivity by essentially suppressing non-double-beta decay background events. A conceptual approach for the extraction from the detector volume, trapping, and identification of a single Ba ion from 5 tonnes of LXe is presented, along with initial results from the commissioning of one of its subsystems, a quadrupole mass filter.Comment: 4 pages, 2 figure

Investigation of radioactivity-induced backgrounds in EXO-200

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

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