Efficient machine learning approach for optimizing the timing resolution of a high purity germanium detector

Gladen, R. W.; Chirayath, V. A.; Fairchild, A. J.; Manry, M.T.; Köymen, A. R.; Weiss, A. H.

doi:10.1016/j.nima.2020.164505

Cited by 12 publications

(7 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Gamma-ray spectra may be used for isotope identification and fundamental nuclear structure studies. Timing resolution in high-purity germanium detectors has been optimized by fully connected CNN architectures (Gladen et al, 2020). Deep, fully connected neural network architectures have been shown to successfully identify isotopes (Abdel-Aal and Al-Haddad, 1997;Medhat, 2012;Kamuda, Stinnett, and Sullivan, 2017;Jhung et al, 2020) and fit peaks (Abdel-Aal, 2002) in gamma spectra.…”

Section: Spectroscopymentioning

confidence: 99%

Colloquium: Machine learning in nuclear physics

et al. 2022

View full text Add to dashboard Cite

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

show abstract

Section: Spectroscopymentioning

confidence: 99%

Colloquium: Machine learning in nuclear physics

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Gladen et al developed a machine learning method, referred to as self-organized map (SOM) algorithm, for estimating the arrival time of annihilation photons in a high purity germanium detector (HPGe). SOM was able to cluster the TOF bins based on the signal shape and its raising edge [22].…”

Section: Instrumentation and Image Acquisition/formationmentioning

confidence: 99%

The promise of artificial intelligence and deep learning in PET and SPECT imaging

et al. 2021

View full text Add to dashboard Cite

This review sets out to discuss the foremost applications of artificial intelligence (AI), particularly deep learning (DL) algorithms, in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging. To this end, the underlying limitations/challenges of these imaging modalities are briefly discussed followed by a description of AI-based solutions proposed to address these challenges. This review will focus on mainstream generic fields, including instrumentation, image acquisition/formation, image reconstruction and low-dose/fast scanning, quantitative imaging, image interpretation (computer-aided detection/ diagnosis/prognosis), as well as internal radiation dosimetry. A brief description of deep learning algorithms and the fundamental architectures used for these applications is also provided. Finally, the challenges, opportunities, and barriers to full-scale validation and adoption of AI-based solutions for improvement of image quality and quantitative accuracy of PET and SPECT images in the clinic are discussed.

show abstract

“…To provide accurate time references for real-world nuclear detectors, additional timing instruments or specific experimental schemes are needed. For example, In [11], the timing resolution of a high purity germanium detector was optimized based on the timing reference of a BaF 2 scintillation detector. In [12], the timing resolution and depth-of-interaction of monolithic scintillators were studied with a collimator crystal placed on the opposite side of the radioactive source.…”

Section: Introductionmentioning

confidence: 99%

Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning

Ai,

Xiao,

Deng

et al. 2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized. In this paper, we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labelling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on two experimental datasets based on silicon photomultipliers (SiPM) as main transducers. In the toy experiment, the neural network model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. In the electromagnetic calorimeter experiment, several neural network models (FC, CNN and LSTM) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely.

show abstract

Efficient machine learning approach for optimizing the timing resolution of a high purity germanium detector

Cited by 12 publications

References 44 publications

Colloquium: Machine learning in nuclear physics

Colloquium: Machine learning in nuclear physics

The promise of artificial intelligence and deep learning in PET and SPECT imaging

Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning

Contact Info

Product

Resources

About