Bulletin of the American Physical Society
2021 Fall Meeting of the APS Division of Nuclear Physics
Volume 66, Number 8
Monday–Thursday, October 11–14, 2021; Virtual; Eastern Daylight Time
Session QB: Mini-Symposium: Neutrinos and Nuclei XII: Double Beta Decay Analysis Techniques |
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Chair: Lindley Winslow, MIT Room: Statler |
Thursday, October 14, 2021 11:30AM - 11:42AM |
QB.00001: Event Simulation and Reconstruction for the nEXO Neutrinoless Double Beta Decay Experiment Samuele Sangiorgio The nEXO experiment plans to search for neutrinoless double-beta decay (0νββ) half-life in 136Xe with a sensitivity > 1028 yr using a time projection chamber (TPC) and 5000 kg of isotopically-enriched liquid xenon. The credibility of the sensitivity estimate hinges on detailed simulations of the physics and detector response, validated input parameters, and realistic algorithms for event reconstruction. All steps of nEXO’s simulation and reconstruction have been recently improved, including the GEANT4 geometry, the event interaction in the liquid xenon, the transport of the ionization electrons and scintillation photons, up to the signals generation and the algorithms that process these to extract the high-level quantities for the 0νββ analysis. In this talk, I will review these developments, present the resulting detector performance, and connect them to the experiment’s sensitivity reach. |
Thursday, October 14, 2021 11:42AM - 11:54AM |
QB.00002: A Kernel Density Approach to Multidimensional Neutrinoless Double Beta Decay Fits in SNO+ Benjamin J Land Analysis of SNO+ data typically involves a maximum likelihood fit of many backgrounds and signal event classes represented by multidimensional probability density functions (PDFs). However, binned histograms, which are typically used for estimating PDFs, require an impractical amount of Monte-Carlo (MC) statistics in higher dimensions to achieve an accurate PDF estimate. Position within the detector and the reconstructed energy of an interaction are important dimensions in these PDFs, and classification scores based on additional information, such as topology, can be included to improve discrimination of signal and background events. Kernel density estimation (KDE) with adaptive bandwidth can be used to provide an accurate estimate of such PDFs with fewer MC statistics than binned histograms require. This approach also provides a convenient method for including systematic uncertainties in the maximum likelihood fit. Here, a framework for using KDE accelerated on GPUs is explored as a technique for analyzing neutrinoless double beta decay data with multidimensional PDFs. |
Thursday, October 14, 2021 11:54AM - 12:06PM |
QB.00003: Multi-site Event Discrimination in the SNO+ Experiment Tereza Kroupova Large liquid scintillator detectors, such as SNO+, are powerful tools for neutrinoless double beta decay searches due to their exceptional mass scalability, good signal efficiency and low cost. One of the challenges of liquid scintillator experiments is the lack of reliable particle identification for electrons and gammas to allow for radioactive background rejection. In this talk, applying a pulse shape discrimination (PSD) technique in the SNO+ experiement will be discussed, demonstrating the potential to distinguish between single-site (purely electronic) and multi-site (containing gammas) events in a liquid scintillator. The technique is based on timing distribution differences between the two event classes and allows for good statistical separation between them. The impact of the PSD on the projected sensitivity to neutrinoless double beta decay in SNO+ will also be discussed. |
Thursday, October 14, 2021 12:06PM - 12:18PM |
QB.00004: Surface Event Pulse Shape Simulation for the Legend Experiment Kevin H Bhimani The Large Enriched Germanium Experiment for Neutrinoless double-beta Decay (LEGEND) collaboration plans to search for neutrinoless double-beta (0νββ) decay in 76Ge using modular arrays of enriched germanium detectors. Since 0νββ candidate events happen at a single site in the germanium detector, pulse shape simulations that model movement of charge carriers in the detector are key to cuts that can reject background from multi-site and surface events. Most events happening in the bulk of the detector, such as gamma-ray events, can be easily simulated by using established models of charge carriers inside Ge. However, surface events such as those caused by alpha incidents on the detector are complex since they generate a large charge cloud, and thus their signal is influenced by effects such as diffusion and self-repulsion. Only the p+ contact and passivated surfaces of the detector are sensitive to alpha events. While these events can be easily rejected using analysis cuts, their behavior before cuts, including their energy spectrum and their distribution on the detector surface, is difficult to model. In this talk we describe a novel simulation of charge carriers in germanium that incorporates diffusion and self-repulsion to model surface alpha events. We also show how such simulations can be sped up using parallel calculations on GPUs, and how they can be used to improve our modeling of alpha backgrounds in 76Ge-based 0νββ searches. |
Thursday, October 14, 2021 12:18PM - 12:30PM |
QB.00005: Machine Learning-Powered Autonomous Data Cleaning for Legend-200 Esteban A León, Julieta Gruszko, Aobo Li The Large Enriched Germanium Experiment for Neutrinoless Double-Beta Decay (LEGEND) will operate in two phases to search for neutrinoless double-beta decay (0νββ). The first (second) stage will employ 200 (1000) kg of 76Ge semiconductor detectors to achieve a half-life sensitivity of 1027 (1028) years. In this study, we present a data-driven approach to remove electronic noise, cross-talk events, and recovery from injected test pulses captured by 76Ge detectors in LEGEND powered by a novel artificial intelligence algorithm. We first de-noise and extract waveform shape information utilizing a Discrete Wavelet Transform (DWT). We then utilize an unsupervised learning clustering algorithm called Affinity Propagation (AP) to obtain a representative waveform basis for a given dataset. We demonstrate that our model is efficient at classifying events for low-background datasets, and can be used as a preliminary data cleaning filter for both low-background and calibration datasets. This method will enable for the automatic detection of background events that require significant time and human effort in traditional data cleaning. |
Thursday, October 14, 2021 12:30PM - 12:42PM |
QB.00006: Optimization of integration time and distance cut in the CUPID array. Joseph H Camilleri CUPID, a proposed next-generation neutrinoless double beta decay search, will deploy an array of approximately ~1,500 Li2MoO4 crystals (isotopically enriched in 100Mo) operated as cryogenic calorimeters in the CUORE cryostat. The experiment aims for a background index of 10-4 counts/(keV*kg*yr) and half-life sensitivity of 0νββ decay of 1.4*1027 yr with 10 years of livetime. The scintillation and thermal signals from the crystals together allow for discrimination of α and β/γ events resulting in rejection of α-induced backgrounds while maintaining a high efficiency of ββ signals. Furthermore, the highly segmented crystal array allows differences in spatial and time signatures of background-like and signal-like events to be exploited to further reduce backgrounds. In this talk, we will discuss development and validation of Monte-Carlo simulation tools to model spatial and temporal correlation of events in the array and their connection to optimizing background tagging. |
Thursday, October 14, 2021 12:42PM - 12:54PM |
QB.00007: Generative Adversarial Networks for KamLAND-Zen Zhenghao Fu Robust and sophisticated tools to generate and handle big data have become increasingly important for low-background neutrino experiments searching for extremely rare events. Both simulated data and real detector data play essential roles for such experiments. Aside from traditional methods, such as Monte Carlo simulation, event generation can be supplemented by Generative Adversarial Networks (GANs). In a spherical liquid scintillator detector, such as KamLAND-Zen, the adoption of a canonical GAN model will introduce inevitable deviation from real detector data. Networks that can deal with spherical topology are required to eliminate this deviation. In this work, we will show that the autoencoder can learn the representation for a collection of events coming from a spherical liquid scintillator detector. We will also describe how a GAN network can generate simulated events for a spherical detector with incredible precision, such that the simulated data is indistinguishable from real detector data. The realistic detector simulation provided by a GAN is shown to improve the accuracy of a neural network classifier and enhance data-MC agreement at the same time. |
Thursday, October 14, 2021 12:54PM - 1:06PM |
QB.00008: Rejecting Spallation Backgrounds in KamLAND-Zen with KamNet Hasung Song KamLAND-Zen is a liquid scintillator detector searching for neutrinoless double beta decay of Xenon-136. Recently, KamLAND-Zen set world-leading limits on this elusive process. One of the primary challenges of this search is the rejection of backgrounds from radioactive isotopes introduced by cosmic-ray spallation. We developed a state-of-the-art neural network classifier, called KamNet, to reject background events and improve detection sensitivity. However, as we rely more heavily on deep neural networks to play key roles in data analysis, it becomes increasingly important to understand exactly how they work. Here, we take a look at KamNet through the lens of network interpretability. Using Monte Carlo (MC) simulations and experimental data, we present the results of recent studies of the origin of KamNet's rejection power. We find that KamNet has the ability to discern multi-vertex events (one or more gammas in addition to a beta) from single-vertex beta events (only betas). This beta vs beta+gamma discrimination is used to help us ascertain spallation background levels. KamNet's rejection performance for key spallation backgrounds will be presented and we discuss how KamNet can inform us about the types of backgrounds it's rejecting. |
Thursday, October 14, 2021 1:06PM - 1:18PM |
QB.00009: Application of simulated data for pileup rejection studies in CUPID Mattia Beretta CUPID (CUORE Upgrade with Particle IDentification) is the proposed upgrade to the tonne-scale cryogenic bolometric experiment CUORE (Cryogenic Underground Observatory for Rare Events). CUPID is designed to search the neutrinoless double beta decay of 100Mo and a major background for this experiment will be the pile-up of multiple two neutrino double beta decay events. To address this problem, we simulated the detector response with a dedicated software tool. We produced controlled data streams with different characteristics, to select constraints on CUPID design parameters. In addition, we also tried different algorithms to find possible processing maximizing the pileup rejection. In this contribution, the latest results on this subject will be presented, outlying the needed steps to be taken towards an efficient pile-up rejection. |
Thursday, October 14, 2021 1:18PM - 1:30PM |
QB.00010: Denoising Algorithms for the CUORE Experiment Kenneth Vetter The Cryogenic Underground Observatory for Rare Events (CUORE) experiment is an ongoing search for neutrinoless double beta decay located at the Gran Sasso National Laboratory (LNGS) in Italy. Recent work has found that the CUORE calorimeters are sensitive to acoustic and seismic events originating from outside the detector at LNGS. To measure the effect of these mechanical disturbances on the calorimeter signals, diagnostic devices including microphones and accelerometers were installed around the CUORE cryostat. Here I will present how different denoising algorithms use the information from these devices to remove microphonic noise from low-temperature calorimeters, including the CUORE NTD bolometer channels. I will also show how denoising improves the energy resolution of these detectors and discuss plans for implementing robust denoising procedures in later experiments. |
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