Optimization of the generator coordinate method with machine-learning techniques for nuclear spectra and neutrinoless double-
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 decay: Ridge regression for nuclei with axial deformation

Zhang, X.; Lin, Weiping; Yao, J. M.; Jiao, Cuicui; Romero, A. M.; Rodrı́guez, T.; Hergert, H.

doi:10.1103/physrevc.107.024304

Cited by 9 publications

(5 citation statements)

References 84 publications

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“…应相关性质的研究 [21] , 如原子核的基态性质, 包括核质量(结合能) [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] 、电荷半径 [25,[39][40][41][42][43][44][45] 、磁矩 [46] 、分离能 [23,25,33,38] 、稳定性 [47,48] 、密度分布 [49][50][51] 、放射性核衰变分支比 [48,52] 等. 此外还有激发态 [53][54][55][56] 、 a衰变 [29,30,[57][58][59][60] 、b衰变 [52,[61][62][63]…”

Section: 目前机器学习已被广泛地应用于原子核结构和反unclassified

Prediction of ground-state spin in odd-<i>A</i> nuclei within decision tree

Wen,

Shang,

et al. 2023

Acta Phys. Sin.

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As a fundamental property of nuclei, ground-state spin has always been a hot topic in nuclear data and structure research. This paper extensively investigates the odd mass nuclei (odd-A nuclei) on the nuclide chart using decision trees, including odd proton nuclei (odd-Z nuclei) and odd neutron nuclei (odd-N nuclei), and trains ground-state spin prediction models for odd-Z nuclei and odd-N nuclei, respectively. In the case of randomly dividing the training and validation sets at a ratio of 75\% to 25\%, the accuracy rates of the training and validation sets for odd-Z nuclei reach 98.9\% and 79.3\%, respectively; The accuracy of the training set and validation set for the odd-N nuclei reach 98.6\% and 71.6\%, respectively. At the same time, through 1000 random selections of training and validation sets for repeated validation, the standard error of the accuracy obtained can be less than 5\%, further verifying the reliability and generalization performance of the decision tree; On the other hand, the accuracy of decision trees is much higher than that of theoretical models commonly used in nuclear structure research, such as Skyrme Hartree-Fock-Bogoliubov(SHFB), covariant density functional theory(CDFT), finite range droplet model(FRDM), etc. Next, using all spin-determined odd-Z and odd-N nuclei as the learning set, a total of 254 spin undetermined but recommended odd-Z and 268 corresponding odd-N nuclei were predicted for their ground-state spin values, with predicted set coincidence rates of 68.5\% and 69.0\%, respectively. Finally, four odd mass number chains, i.e., Z=59, Z=77, N=41, and N=59, are selected to compare the learning (prediction) results of the decision tree with the experimental (recommended) values of the corresponding nuclei and the results given by the three theoretical models, further demonstrating the research and application value of the decision tree in the ground-state spin of nuclei.

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Section: 目前机器学习已被广泛地应用于原子核结构和反unclassified

Prediction of ground-state spin in odd-<i>A</i> nuclei within decision tree

Wen,

Shang,

et al. 2023

Acta Phys. Sin.

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“…Zhang et al [28] introduce an algorithm which reduce the subspace and obtain optimal statistical machine learnings models. For simplicity, the polynomial ridge regression (RR) algorithm is used to learn the norm and Hamiltonian kernels of axially deformed configurations.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis of The Travelling Time According to Weather Conditions Using Machine Learning Algorithms

CANBULUT

2023

Preprint

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Nowadays, a large part of the world’s population lives in urban areas. The increase in the population living in cities makes it difficult to live in cities.Local administrators seek solutions to these problems in order to make cities more livable. In order to increase the quality of transportation, which is among the important issues for local governments, studies are carried out to establish smart transportation systems for public transportation companies. Estimating travel times is an important tool in managing transportation operations. In addition, operations managers use these forecasts to coordinate future transportation operations. The public transport travel time prediction has been a well-researched topic; various researchers have predicted travel time using mathematical, statistical, and machine learning based models. Researchers also have compared the performance of these models. It can be seen that studies in this field can be divided into statistical methods and machine learning methods. And number of researchers use one of these methods. But in this study, we use both statistical and machine learning techniques together. Also, the parameters of this study are different from the other studies in this literature. The effect of the weather on the travel time will be analyzed. In this respect it is thought that the study will contribute to the literature. In the study, in addition to model tuning, sensitivity analyzes are also carried out according to the change of the k value in the KNN algorithm and the change of the threshold values used to determine outliers. we can say that, as can be seen from the analysis, the Catboost algorithm appears to be the algorithm that gives the best results in almost every situation.

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“…As it continues to evolve, machine learning is bringing about a pivotal shift in nuclear theory. Currently, regression analysis with supervised learning is a common practice in nuclear theory [21][22][23][24][25][26][27][28][29][30][31][32][33][34]. For example, it has been employed to construct a new mass table [35].…”

Section: Introductionmentioning

confidence: 99%

Nonempirical shape dynamics of heavy nuclei with multitask deep learning

Hizawa,

Hagino

2024

Phys. Rev. C

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A microscopic description of nuclear fission represents one of the most challenging problems in nuclear theory. While phenomenological coordinates, such as multipole moments, have often been employed to describe fission, it is not obvious whether these parameters fully reflect the shape dynamics of interest. We here propose a novel method to extract collective coordinates, which are free from phenomenology, based on multitask deep learning in conjunction with density functional theory (DFT). To this end, we first introduce randomly generated external fields to a Skyrme energy density functional (EDF) and construct a set of nuclear number densities and binding energies for deformed states of 236 U around the ground state. By training a neural network on such a dataset with a combination of an autoencoder and supervised learning, we successfully identify a two-dimensional latent variables that accurately reproduce both the energies and the densities of the original Skyrme-EDF calculations, within a mean absolute error of 113 keV for the energies. In contrast, when multipole moments are used as latent variables for training in constructing the decoders, we find that the training data for the binding energies are reproduced only within 2 MeV. This implies that conventional multipole moments do not provide fully adequate variables for a shape dynamics of heavy nuclei.

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Optimization of the generator coordinate method with machine-learning techniques for nuclear spectra and neutrinoless double- β decay: Ridge regression for nuclei with axial deformation

Cited by 9 publications

References 84 publications

Prediction of ground-state spin in odd-<i>A</i> nuclei within decision tree

Prediction of ground-state spin in odd-<i>A</i> nuclei within decision tree

Analysis of The Travelling Time According to Weather Conditions Using Machine Learning Algorithms

Nonempirical shape dynamics of heavy nuclei with multitask deep learning

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