Fabrication, characterization and analysis of a prototype high purity germanium detector for $$^{76}$$Ge-based neutrinoless double beta decay experiments

Jany, A.; Misiaszek, M.; Mróz, Tomasz; Panas, K.; Zuzel, G.; Wójcik, Marcin; Brudecki, Kamil; Jurkowski, Jacek; Mietelski, Jerzy W.; Misiak, R.; Marszałek, M.; Mitura‐Nowak, Marzena

doi:10.1140/epjc/s10052-020-08781-3

Cited by 3 publications

(1 citation statement)

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“…The GERDA experiment pioneered using ANNs to identify single-site events in semicoaxial high-purity germanium detectors (Agostini et al, 2013(Agostini et al, , 2019. The use of ANNs to differentiate single-site and multisite events in HPGe detectors was also developed by Caldwell et al (2015), Holl et al (2019), andJany et al (2021).…”

Section: Event and Signal Classificationmentioning

confidence: 99%

Colloquium: Machine learning in nuclear physics

et al. 2022

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TABLE I. Table of ML methods discussed in this Colloquium with an indication of the main type of learning (S, supervised; U, unsupervised; semi-S, semisupervised). Acronym Method Description Learning type AE, VAE Autoencoders, Variational autoencoders ANN capable of learning efficient representations of the input data without any supervision U ANN Artificial neural network Models for learning defined by connected units (or nodes) and hidden layers with well-defined inputs and outputs S BED Bayesian experimental design Bayesian inference for experimental design S BM Boltzmann machine Generative ANN that can learn a probability distribution from sets of changing inputs U BMA, BMM Bayesian model averaging, Bayesian model mixing Bayesian inference applied to model selection or the combined estimation, or performed over a mixture model S BNN Bayesian neural network ANN where the parameters of the network are represented by probabilities learned by Bayesian inference S BO Bayesian optimization Optimization of functions without an a priori knowledge of functional forms. S and semi-S CNN Convolutional neural network ANN where convolution is used to reduce dimensionalities S EMB Ensemble methods and boosting Methods based on collections of decision trees as simple learners S GAN Generative adversarial network System of two ANNs where a generative network generates outputs while a discriminative network evaluates them U GP Gaussian process Collection of random variables that have a joint Gaussian distribution used in Bayesian inference Semi-S KNN k-nearest neighbors Nonparametric method where inputs consist of the k closest training examples in a dataset S KR Kernel regression Extension of linear regression methods to include nonlinear function kernels S LR Logistic regression Convex optimization method based on maximum likelihood estimate for classification problems S LSTM Long short-term memory RNN capable of learning long-term dependencies S PCA Principal component analysis Dimensionality reduction technique based on retaining the largest eigenvalues of the covariance matrix U REG Linear regression Linear algebra methods used for modeling continuous functions in terms of their explanatory variables S RL Reinforcement learning Learning achieved by trial and error of desired and undesired events Neither S nor U RNN Recurrent neural network ANN where connections between nodes allow for temporal dynamic behavior S SVM Support vector machine Convex optimization techniques with efficient ways to distinguish features in datasets S

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