Auswahl der wissenschaftlichen Literatur zum Thema „Discrimination double-Beta“

We report the development of a CaMoO4crystal low temperature detector for the AMoRE neutrinoless double beta decay(0νββ)search experiment. The prototype detector cell was composed of a 216 g CaMoO4crystal and a metallic magnetic calorimeter. An overground measurement demonstrated FWHM resolution of 6–11 keV for full absorption gamma peaks. Pulse shape discrimination was clearly demonstrated in the phonon signals, and 7.6 σof discrimination power was found for theαandβ/γseparation. The phonon signals showed rise-times of about 1 ms. It is expected that the relatively fast rise-time will increase the rejection efficiency of two-neutrino double beta decay pile-up events which can be one of the major background sources in0νββsearches.

The dynamical behavior of the double-chain deoxyribonucleic acid (DNA) system holds significant implications for advancing the understanding of DNA transmission laws in the realms of biology and medicine. This study delves into the investigation of chaos patterns and solitary wave solutions for the (2+1) Beta-fractional double-chain DNA system, employing the theory of planar dynamical systems and the method of complete discrimination system for polynomials (CDSP). The results demonstrate a diverse spectrum of solitary wave solutions, sensitivity to perturbations, and manifestations of chaotic behavior within the system. Through the utilization of the complete discrimination system for polynomials, a multitude of novel solitary wave solutions, encompassing periodic, solitary wave, and Jacobian elliptic function solutions, were systematically constructed. The influence of Beta derivatives on the solutions was elucidated through parameter comparison analysis, emphasizing the innovative nature of this study. These findings underscore the potential of this system in unraveling various biologically significant DNA transmission mechanisms.

Abstract Experiments to search for neutrinoless double-beta (0νββ) decay of 76Ge using a high-purity germanium (HPGe) detector rely heavily on background suppression technologies to enhance their sensitivities. In this work, we proposed a pulse-shape analysis method based on a neural network (NN) and a light gradient boosting machine (lightGBM; LGB) to discriminate single-electron (background) and double-electrons (0νββ signal) events in a multi-electrode HPGe detector. In this paper, we describe a multi-electrode HPGe detector system, a data-processing system, and pulse-shape simulation procedures. We built a fully connected (FC) neural network and an LGB model to classify the single- and double-electron events. The FC network is trained with simulated single- and double-electron-induced pulses and tested in an independent dataset generated by the pulse-shape simulation. The discrimination efficiency of the FC neural network in the test set for the 0νββ double-electron events signal was 77.4%, the precision was 57.7%, and the training time was 430 min. The discrimination efficiency of LGB model was 73.1%, the precision was 64.0%, and the training time was 1.5 min. This study demonstrated that it is feasible to realize single- and double-electron discrimination on multi-electrode HPGe detectors using an FC neural network and LGB model. These results can be used as a reference for future 76Ge 0νββ experiments.

The GERmanium Detector Array, GERDA, is designed to search for neutrinoless double-beta (0νββ) decay of 76 Ge and it is installed in the Laboratori Nazionali del Gran Sasso (LNGS) of INFN, Italy. In this review, the detection principle and detector setup of GERDA are described. Also, the main physics results by GERDA Phase I, are discussed. They include the measurement of the half-life of 2νββ decay, the background decomposition of the energy spectrum and the techniques for the discrimination of the background, based on the pulse shape of the signal. In the last part of this review, the estimation of a limit on the half-life of 0νββ ([Formula: see text] at 90% C.L.) and the comparison with previous results are discussed. GERDA data from Phase I strongly disfavor the recent claim of 0νββ discovery, based on data from the Heidelberg–Moscow experiment.

An observation of neutrinoless double beta ([Formula: see text]) decay would allow to shed light onto the nature of neutrinos. Gerda (GERmanium Detector Array) aims to discover this process in a background-free search using [Formula: see text]Ge. The experiment is located at the Laboratori Nazionali del Gran Sasso (LNGS) of the Istituto Nazionale di Fisica Nucleare (INFN) in Italy. Bare, isotopically enriched, high purity germanium detectors are operated in liquid argon. Gerda follows a staged approach. In Phase II 35.6 kg of enriched germanium detectors are operated since December 2015. The application of active background rejection methods, such as a liquid argon scintillation light read-out and pulse shape discrimination of germanium detector signals, allows to reduce the background index to the intended level of [Formula: see text] cts/(keV⋅kg⋅yr). No evidence for the [Formula: see text] decay has been found in 23.2 kg⋅yr of Phase II data, and together with data from Phase I the up-to-date most stringent half-life limit for this process in [Formula: see text]Ge has been established, at a median sensitivity of 5.8⋅10[Formula: see text][Formula: see text]yr the 90[Formula: see text]% C.L. lower limit is 8.0⋅10[Formula: see text][Formula: see text]yr.

Abstract The GERDA experiment searched for the lepton number violating neutrinoless double-beta 0υββ decay of 76 Ge. Observation of this decay would provide answers to fundamental problems in particle physics and cosmology, including the origin of neutrino masses and baryon asymmetry in the universe. The GERDA experiment achieved the most stringent lower limit on the half-life of the 0υββ decay of 1.8 • 1026 yr at 90% C.L. (which coincides with the sensitivity) by operating high-purity germanium (HPGe) detectors enriched in 76Ge submerged in liquid argon (LAr). The collaboration could achieve this breakthrough by reducing the background event rate to 5.2 • 10–4 counts/(keV kg yr) at the end-point energy. This unprecedented background index could be achieved by developing unique technologies like utilizing the scintillation light of the LAr to reject efficiently background events that deposit energy simultaneously in the HPGe detectors and in LAr, and the pulse shape discrimination which exploits specific event topologies of backgrounds and signal candidates. Due to the ultra-low background approach the GERDA data is also suited for other rare event searches beyond the 0υββ decay like the search for super-WIMPs.

Abstract The CROSS experiment is proposing to use a new technology of surface sensitive bolometers for low-background neutrinoless double beta decay searches. Efficient rejection of surface α and β events will allow to reach background in the region of interest below than 10−4 cnts/keV/kg/yr. The isotopes of interest, which are 130Te and 100Mo, are investigated with TeO2 and Li2MoO4 bolometers. The surface sensitivity is achieved thanks to the evaporation of thin metallic film on the crystal surface that modifies the pulse shape of near-surface events. An investigation of various pulse shape parameters was performed. The analysis shows that one of the best parameters for discrimination is the integrated area of the raw signal both for TeO2 and Li2MoO4 with Pd-Al (10 nm - 100 nm) bi-layer.

La recherche de la désintégration double bêta sans neutrino (0νββ) est cruciale pour faire progresser notre compréhension de la physique et explorer la physique au-delà du modèle standard. Cependant, cette recherche est incroyablement difficile en raison de l'extrême rareté de la désintégration, qui nécessite une interprétation approfondie et une dépendance aux contraintes expérimentales et aux modèles nucléaires théoriques. L'expérience PandaX-III est dédiée à la recherche de 0νββ dans 136-Xe. Il s'agit d'une chambre de projection temporelle (TPC) gazeuse à haute pression équipée de détecteurs Micromegas. Ce choix a été fait pour maximiser la capacité de détection des traces de particules et minimiser les fluctuations statistiques dans la résolution énergétique. L'un des principaux défis de la recherche d'événements 0νββ est la discrimination entre le signal et les événements de bruit de fond, qui contaminent la région d'intérêt (ROI). Le système de lecture par pistes des détecteurs Micromegas (une combinaison de 52 détecteurs forme un plan de lecture) permet la reconstruction 2D précise des trajectoires d'ionisation avec les informations de charge et de temps. Cela permet d'étudier l'énergie et la topologie des trajectoires d'électrons et, en conséquence, de distinguer le signal du bruit de fond. Pour supprimer la scintillation et ne se baser que sur le signal d'ionisation, le 136-Xe gazeux enrichi à 90% est mélangé avec 1% de triméthylamine (TMA) qui joue le rôle de "quencher". La résolution énergétique actuelle de l'expérience PandaX-III est de 3% pour l'énergie de 2457 keV de la désintégration de 136-Xe 0νββ, et devrait être améliorée à 1%. Cependant, plusieurs facteurs peuvent dégrader la résolution en énergie, tels que la présence de canaux morts, les inhomogénéités de gain dans les détecteurs Micromegas ou l'attachement des électrons dans la TPC. Ce travail de doctorat présente une étude de l'impact des canaux manquants sur les reconstructions d'énergie et de topologie dans l'expérience PandaX-III. Les résultats de la détermination de la charge du blob n'offrent pas la possibilité souhaitée de reconstituer la partie de son énergie qui aurait été perdue en raison des canaux manquants dans XZ à partir des projections YZ des traces d'événements reconstruites et vice versa. Cependant, l'étude a montré qu'il est possible d'utiliser des algorithmes d'apprentissage automatique pour atténuer l'impact des canaux manquants sur ls reconstruction de l'énergie et de la topologie. Un modèle de réseau neuronal convolutif (CNN) a été développé pour prédire l'énergie réelle des électrons à partir des données simulées collectées par les Micromegas avec des canaux manquants. Les résultats finaux montrent que le modèle CNN prédit l'énergie réelle des événements enregistrés par les Micromegas avec des canaux manquants avec une grande efficacité. Nous observons une amélioration de l'efficacité de détection du signal de Monte Carlo dans la ROI, qui passe de 69% à 89% après l'application du modèle CNN, par rapport à l'approche directe consistant à additionner les amplitudes des signaux provenant des Micromegas dont les canaux sont manquants. Un autre modèle CNN a également été utilisé pour classer les événements à deux électrons des événements à un seul électron dans les données de Monte Carlo affectées par des canaux manquants. Le modèle est capable de rejeter 99% des événements de bruit de fond tout en conservant une efficacité de 26% pour les signaux 0νββ dans la ROI. Les résultats de ce travail sont prometteurs et ouvrent la voie à d'autres études visant à améliorer la résolution en énergie et le rejet du bruit de fond dans l'expérience PandaX-III
The search for neutrinoless double-beta decay (0νββ) is crucial for advancing our understanding of physics and exploring physics beyond the Standard Model. However, this pursuit is incredibly challenging due to the decay's extreme rarity, requiring profound interpretation and reliance on experimental constraints and theoretical nuclear models. The PandaX-III experiment is dedicated to the search for 0νββ in 136-Xe. It is a high-pressure gaseous Time Projection Chamber (TPC) with Micromegas detectors. This design choice is made to maximize the particle track detection and discrimination 0νββ signal vs. gamma background capabilities. One of the main challenges of the 0νββ search is the discrimination between the signal and background events, which contaminate the region of interest (ROI). The strip readout system of the Micromegas detectors (a combination of 52 of them form a readout plane) allows for the precise 2D reconstruction of the ionization tracks together with the charge and time information. This allows for studying the electron tracks' energy and topology and ultimately discriminating the signal from the background. To suppress the scintillation light and rely only on the ionization signal, a 90% enriched 136-Xe is mixed with a 1% trimethylamine (TMA) quencher. The current energy resolution of the PandaX-III experiment is 3% for the 2457 keV energy of the 136-Xe 0νββ decay, envisioned to be improved to 1%. However, several factors can degrade the energy resolution, such as the presence of dead channels, gain inhomogeneities in the Micromegas detectors, or electron attachment in the TPC. This Ph.D work presents a study on the impact of missing channels on the energy and topology reconstructions in the PandaX-III experiment. The results of the Blob charge determination do not provide the desired possibility of reconstituting the part of the blob energy that would have been lost due to missing channels in XZ from YZ projections of reconstructed event tracks and vice versa. However, the study gave insight into employing machine learning (ML) algorithms to mitigate the impact of missing channels on energy and topology reconstructions. A Convolutional Neural Network (CNN) model was developed to predict the true energy of the electrons from the simulated data collected by the Micromegas with missing channels. The final results show that the CNN model predicts the true energy of the events recorded by the Micromegas with missing channels with a good energy resolution. We observe an improvement in the detection efficiency of the Monte Carlo 0νββ signal in the ROI from 69% to 89% after applying the CNN model, in comparison to the direct approach of directly summing amplitudes of the signals from the Micromegas with missing channels. Another CNN model was also used to classify the two-electron events from the single-electron events in the Monte Carlo data affected by missing channels. The model is capable of rejecting 99% of the background events while maintaining a 26% efficiency for the 0νββ signal in the ROI. The results of this work are promising and pave the way for further studies to improve the energy resolution and background rejection in the PandaX-III experiment

Dissertação de Mestrado em Astrofísica e Instrumentação para o Espaço apresentada à Faculdade de Ciências e Tecnologia
A detecção do decaimento beta duplo sem neutrinos (0νββ) é um objectivo científico com implicações significativas nas áreas da física de neutrinos e cosmologia, e uma nova geração de detectores de grande escala está em desenvolvimento para sondar meias-vidas de 0νββ na gama dos 10²⁵ - 10²⁷ anos. A experiência de matéria escura LUX-ZEPLIN (LZ) usará uma câmara de projecção temporal (TPC) de xénon líquido com especificações competitivas para a procura de eventos de 0νββ do ¹³⁶Xe. Com os cortes correntemente aplicados, a taxa de eventos de fundo projectada para o LZ é de 6.8 x 10⁻³ contagens/keV/kg/ano na região de energia de interesse para este decaimento, quase toda devido a eventos com a emissão de um só electrão primário. Os dois electrões primários dos eventos 0νββ produzem uma topologia de deposição de energia diferente da dos fundos, motivando o desenvolvimento de um corte baseado em algoritmos de auto-aprendizagem que explore essa diferença topológica. Neste trabalho foi examinada a viabilidade e o potencial de diferentes algoritmos de auto-aprendizagem aplicados a este problema. Foi desenvolvida uma infraestrutura expansível e modular para a extracção de propriedades e redução dinâmica de dimensionalidade em sinais temporais simulados deste tipo de detectores. Esta infraestrutura foi aplicada à discriminação das duas classes de evento, com quatro classificadores binários diferentes (k-nearest neighbors, support vector machines, Gaussian process e random forests), usando dados simulados (com balanço igual entre as duas classes) para este propósito. Para identificar a melhor configuração, foi desenvolvido um método rápido de comparação e avaliação de desempenho, capaz de extrapolar para balanços diferentes dos utilizados. Em suma, foi concebido um procedimento de avaliação de viabilidade sistemático para abordagens de classificação binária. Com o auxílio do Instituto Nacional de Computação Distribuída, e usando uma versão customizada do pacote ANTS2 com integração do Geant4, foram simulados dois conjuntos de dados com ~10⁴ pontos cada: um em que todos os electrões primários de cada classe tinham a direcção inicial de emissão mais favorável (vertical); e um em que a direcção de emissão inicial era isotrópica. Foi modelada uma TPC de LZ miniaturizada, simulando a física relevante para a topologia: deriva e difusão dos electrões de ionização; electroluminescência; e geração dos pulsos na matriz de PMTs superior. Verficou-se que o classificador por Gaussian process tinha o melhor desempeho dos quatro seleccionados. Dada uma sensibilidade-alvo a 0νββ de 80%, o classificador reduziu o fundo devido a electrões singulares a ~22% da sua taxa inicial para emissões verticais, e a 37% da sua taxa inicial para emissões isotrópicas. No entanto, para emissões isotrópicas não foi prevista uma melhoria na sensibilidade do LZ. A principal causa de dificuldade na discriminação determinou-se ser a difusão durante a deriva, com os métodos de desconvolução Gaussiana a serem uma via promissora para investigação futura.
Detection of neutrinoless double beta decay (0νββ) is a scientific goal with significant implications for neutrino physics and cosmology, and a new generation of large-scale detectors is underway for probing 0νββ half-lives in the 10²⁵ - 10²⁷ yr range. The LUX-ZEPLIN (LZ) dark matter experiment will use a liquid xenon time projection chamber (TPC) with competitive specifications for the search of 0νββ events of ¹³⁶Xe. With the current background rejection analysis, the projected LZ 0νββ background rate is 6.8 x 10⁻³ counts/keV/kg/year in the relevant energy region for this decay, with almost all of the background occurring due to events with a single primary electron. The two primary electrons of 0νββ events produce a different energy deposition topology than these backgrounds, motivating the investigation of topology-based discrimination techniques. In this work, the viability of a discrimination cut based on machine learning algorithms was examined. A modular and expandable framework was developed for feature extraction and dynamic dimensionality reduction on simulated time-series data, for the purpose of binary classification. This framework was applied to discrimination of the two classes of event, with four different binary classifiers (k-nearest neighbors, support vector machines, Gaussian process, and random forests), using data simulated (with equal balancing of the two classes) for this purpose. To identify the best configuration, a fast performance comparison and assessment method was developed, capable of extrapolating to balancings other than the one employed. In effect, a versatile procedure for systematic viability assessment of binary classification approaches was conceived.With the aid of the Instituto Nacional de Computação Distribuída, and using a custom version of the ANTS2 package with Geant4 integration, two datasets with ~10⁴ datapoints each were simulated: one where all the primary electrons of the two classes had the most favorable initial emission direction for this particular analysis (vertical); and one where the initial emission direction was isotropic. A miniaturized LZ TPC was modeled, recreating topology transport physics: drift diffusion; electroluminescence; and pulse generation in the upper PMT array. The Gaussian process classifier was seen to perform best out of the four selected. At the optimal configuration in each scenario, for a target 0νββ sensitivity of 80%, it reduced the single electron background to ~22% of its initial rate for vertical emission, and to 37% of the original rate for isotropic emission. However, for ISO there was no predicted improvement to LZ sensitivity. The main cause of the difficulty in discrimination was determined to be drift diffusion, with a promising avenue of investigation being Gaussian deconvolution methods.