Showing papers by "V. A. Belov published in 2023"
02 Mar 2023
TL;DR: In this article , a deep learning-based convolutional neural network is used to discriminate signal from background events, and a new search for two-neutrino double-beta ($2
u\beta\beta$) decay of Xe$ to the $0^+_1$ excited state of Ba$ is performed with the full EXO-200 dataset.
Abstract: A new search for two-neutrino double-beta ($2
u\beta\beta$) decay of $^{136}\rm Xe$ to the $0^+_1$ excited state of $^{136}\rm Ba$ is performed with the full EXO-200 dataset. A deep learning-based convolutional neural network is used to discriminate signal from background events. Signal detection efficiency is increased relative to previous searches by EXO-200 by more than a factor of two. With the addition of the Phase II dataset taken with an upgraded detector, the median 90$\%$ confidence level half-life sensitivity of $2
u\beta\beta$ decay to the $0^+_1$ state of $^{136}\rm Ba$ is $2.9 \times 10^{24}~\rm yr$ using a total $^{136}\rm Xe$ exposure of $234.1~\rm kg~yr$. No statistically significant evidence for $2
u\beta\beta$ decay to the $0^+_1$ state is observed, leading to a lower limit of $T^{2
u}_{1/2}(0^+ \rightarrow 0^+_1)>1.4\times10^{24}~\rm yr$ at 90$\%$ confidence level, a factor of $1.7$ improvement over the current world's best constraint.
1 citations
TL;DR: In this paper , the authors developed a Materials Database Application for the nEXO experiment to manage and make use of the radioassay screening data to quantitatively assess detector design options.
Abstract: Large-scale low-background detectors are increasingly used in rare-event searches as experimental collaborations push for enhanced sensitivity. However, building such detectors, in practice, creates an abundance of radioassay data especially during the conceptual phase of an experiment when hundreds of materials are screened for radiopurity. A tool is needed to manage and make use of the radioassay screening data to quantitatively assess detector design options. We have developed a Materials Database Application for the nEXO experiment to serve this purpose. This paper describes this database application, explains how it functions, and discusses how it streamlines the design of the experiment.
14 Jul 2023
TL;DR: Akimov et al. as discussed by the authors published a data release from the measurements of the CsI[Na] response to low energy nuclear recoils by the COHERENT collaboration.
Abstract: Description of the data release 10.13139/OLCF/1969085 (https://doi.ccs.ornl.gov/ui/doi/426) from the measurements of the CsI[Na] response to low energy nuclear recoils by the COHERENT collaboration. The release corresponds to the results published in"D. Akimov et al 2022 JINST 17 P10034". We share the data in the form of raw ADC waveforms, provide benchmark values, and share plots to enhance the transparency and reproducibility of our results. This document describes the contents of the data release as well as guidance on the use of the data.
01 Apr 2023
TL;DR: In this paper , a Wasserstein Generative Adversarial Network (GAN) is trained on real calibration data using raw scintillation waveforms as input to produce high-quality simulated waveforms.
Abstract: Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network — a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted.
31 May 2023
TL;DR: Using an 185-kg NaI[Tl] array, COHERENT has measured the inclusive electron-neutrino charged-current cross section on Iodine with neutrinos produced by the Spallation Neutron Source at Oak Ridge National Laboratory as discussed by the authors .
Abstract: Using an 185-kg NaI[Tl] array, COHERENT has measured the inclusive electron-neutrino charged-current cross section on ${}^{127}$I with neutrinos produced by the Spallation Neutron Source at Oak Ridge National Laboratory. Iodine is one the heaviest targets for which low-energy ($\leq 50$ MeV) inelastic neutrino-nucleus processes have been measured, and this is the first measurement of its inclusive cross section. After a five-year detector exposure, COHERENT reports a flux-averaged cross section for electron neutrinos produced at a pion decay-at-rest source of $9.2^{+2.1}_{-1.8}\times10^{-40}$ cm$^2$. This corresponds to a value that is $\sim$41% lower than predicted using the MARLEY event generator with a measured Gamow-Teller strength distribution. In addition, the observed visible spectrum from charged-current scattering on ${}^{127}$I has been measured between 10 and 55 MeV, and the exclusive zero-neutron and one-or-more-neutron emission cross sections are measured to be $5.12_{-3.1}^{+3.4} \times 10^{-40}\mbox{ cm}^2$ and $2.2_{-0.5}^{+0.4} \times 10^{-40}\mbox{ cm}^2$, respectively.