Precision mass measurement of lightweight self-conjugate nucleus 80Zr

Hamaker, A.; Leistenschneider, E.; Jain, R.; Bollen, G.; Giuliani, Samuel A.; Lund, K.; Nazarewicz, W.; Neufcourt, Léo; Nicoloff, Catherine; Puentes, D.; Ringle, R.; Sumithrarachchi, C. S.; Yandow, I. T.

doi:10.1038/s41567-021-01395-w

Cited by 17 publications

(10 citation statements)

References 65 publications

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“…The FT-ICR signal strength for a given eigenmotion(s) is also highly dependent on which of the segmented ring electrodes in the Penning trap are used for detection or excitation [35,36]. Often this detection scheme is chosen to maximize the signal amplitude of the eigenfrequency being probed.…”

Section: Fourier Transform Ion Cyclotron Resonance Techniquementioning

confidence: 99%

Applications of Machine Learning and Neural Networks for FT-ICR Mass Measurements with SIPT

Campbell,

Bollen,

Hamaker

et al. 2023

Atoms

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The single-ion Penning trap (SIPT) at the Low-Energy Beam Ion Trapping Facility has been developed to perform precision Penning trap mass measurements of single ions, ideal for the study of exotic nuclei available only at low rates at the Facility for Rare Isotope Beams (FRIB). Single-ion signals are very weak—especially if the ion is singly charged—and the few meaningful ion signals must be disentangled from an often larger noise background. A useful approach for simulating Fourier transform ion cyclotron resonance signals is outlined and shown to be equivalent to the established yet computationally intense method. Applications of supervised machine learning algorithms for classifying background signals are discussed, and their accuracies are shown to be ≈65% for the weakest signals of interest to SIPT. Additionally, a deep neural network capable of accurately predicting important characteristics of the ions observed by their image charge signal is discussed. Signal classification on an experimental noise dataset was shown to have a false-positive classification rate of 10.5%, and 3.5% following additional filtering. The application of the deep neural network to an experimental 85Rb+ dataset is presented, suggesting that SIPT is sensitive to single-ion signals. Lastly, the implications for future experiments are discussed.

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Section: Fourier Transform Ion Cyclotron Resonance Techniquementioning

confidence: 99%

Applications of Machine Learning and Neural Networks for FT-ICR Mass Measurements with SIPT

Campbell,

Bollen,

Hamaker

et al. 2023

Atoms

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“…By considering several global models and the most recent data, one can apply the powerful techniques of Bayesian model averaging (BMA) and Bayesian model mixing (BMM) to assess model-related uncertainties in the multimodel context (Phillips et al, 2021). Examples of recent model-mixed predictions using BMA include analysis of the neutron dripline in the Ca region (Neufcourt et al, 2019), studies of proton dripline and proton radioactivity (Neufcourt et al, 2020a), quantification of the particle stability of nuclei (Neufcourt et al, 2020b), combination of models calibrated in different domains (Kejzlar et al, 2020), and assessment of the puzzling mass of 80 Zr (Hamaker et al, 2021). Figure 4 shows the posterior probability of existence for all nuclei in the nuclear landscape based on predictions of 11 global mass models, the most recent data on nuclear existence and masses, and three modelaveraging strategies to compute the BMA weights.…”

Section: Nuclear Properties With MLmentioning

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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“…The mass is one of the most fundamental properties in the study of nuclear structure [1]. Isotopes in the medium-heavy and neutron-deficient region near the N = 50 and N = Z lines are of special interest for nuclear structure studies as this region of the nuclear chart exhibits numerous unique phenomena [2,3,4,5,6]. Of these isotopes, the heaviest doubly-magic N = Z nucleus, 100 Sn, is the most desirable to study and attracts considerable attention [7,8].…”

Section: Introductionmentioning

confidence: 99%

Studying Gamow-Teller transitions and the assignment of isomeric and ground states at $N=50$

Mollaebrahimi,

Hornung,

Dickel

et al. 2022

Preprint

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Direct mass measurements of neutron-deficient nuclides around the N = 50 shell closure below 100 Sn were performed at the FRS Ion Catcher (FRS-IC) at GSI, Germany. The nuclei were produced by projectile fragmentation of 124 Xe, separated in the fragment separator FRS and delivered to the FRS-IC. The masses of 14 ground states and two isomers were measured with relative mass uncertainties down to 1×10 −7 using the multiple-reflection time-of-flight mass spectrometer of the FRS-IC, including the first direct mass measurements of 98 Cd and 97 Rh. A new Q EC = 5437 ± 67 keV was obtained for 98 Cd, resulting in a summed Gamow-Teller (GT) strength for the five observed transitions (0 + −→ 1 + ) as B(GT) = 2.94 +0.32 −0.28 . Investigation of this result in state-of-the-art shell model approaches sheds light into a better understanding of the GT transitions in even-even isotones at N = 50. The excitation energy of the long-lived isomeric state in 94 Rh was determined for the first time to be 293 ± 21 keV. This, together with the shell model calculations, allows the level ordering in 94 Rh to be understood.

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Precision mass measurement of lightweight self-conjugate nucleus 80Zr

Cited by 17 publications

References 65 publications

Applications of Machine Learning and Neural Networks for FT-ICR Mass Measurements with SIPT

Applications of Machine Learning and Neural Networks for FT-ICR Mass Measurements with SIPT

Colloquium: Machine learning in nuclear physics

Studying Gamow-Teller transitions and the assignment of isomeric and ground states at $N=50$

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