Machine learning in nuclear physics at low and intermediate energies

He, W.; Li, Qingfeng; Ma, Yugang; Niu, Zhong-Ming; Pei, Jian; Zhang, Yingxun

doi:10.1007/s11433-023-2116-0

Cited by 55 publications

(7 citation statements)

References 157 publications

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“…The combination of machine learning in physics has produced many valuable research results in the study of particle physics [32][33][34], condensed matter physics [35,36], and astrophysics [37,38]. In nuclear physics, machine learning methods are also widely used to study various nuclear properties [39], such as nuclear mass [40][41][42][43], αdecay [44,45], β-decay half-life [46,47], low-lying excitation spectra [48][49][50], and fission yield [51,52], etc. Various machine learning methods including artificial neural networks [53,54], Bayesian neural networks [55][56][57], naive Bayesian probability classifiers [58], kernel ridge regression [59], and convolutional neural networks [60] have also been used to predict nuclear charge radii [61,62].…”

Section: Introductionmentioning

confidence: 99%

Nuclear charge radius predictions based on eXtreme Gradient Boosting

Li,

Zhang,

Fang

2024

Phys. Scr.

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Nuclear charge radii with 8 ≤ Z ≤ 100 are studied based on the eXtreme Gradient Boosting (XGBoost) method. Besides the proton, neutron, and mass numbers, the physical quantities related to the isospin, shell, and pairing eﬀects are important to improve the performance of the XGBoost method by including them as the input variables. The XGBoost method describes the nuclear charge radii better than the Skyrme-Hartree-Fock-Bogoliubov (HFB)-21 model, especially for odd-Z nuclei. The root-mean-square deviation with respect to the experimental data is reduced from 0.025 fm of the HFB-21 model to 0.012 fm of the XGBoost method in the learning set. It is found that the XGBoost method has reliable extrapolation ability at least for the nuclei not far from the learning region, which is veriﬁed by comparison with the data in the newly measured experimental data. When extrapolated to the unknown region, the XGBoost predictions of charge radii are close to the HFB-21 results, and the deviations between them are generally less than 0.1 fm even within about 20 steps from the known region.20 steps from the known region.

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Section: Introductionmentioning

confidence: 99%

Nuclear charge radius predictions based on eXtreme Gradient Boosting

Li,

Zhang,

Fang

2024

Phys. Scr.

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“…The recently developed machine-learning (ML) or artificial intelligence (AI)-driven technologies provide a way out. The ML methods have already earned credit in the fields of big data analysis due to their advantages of efficiency and adaptivity (Krizhevsky et al 2012;Jordan & Mitchell 2015;LeCun et al 2016;Li et al 2019b), and they have already been applied in many different fields of physics, e.g., Abraham et al (2018), Mehta et al (2019), Niu et al (2019), Gu et al (2020), Brady et al (2021), Boehnlein et al (2022), He et al (2023aHe et al ( , 2023b, Oala et al (2023), and references therein.…”

Section: Introductionmentioning

confidence: 99%

Insights into Neutron Star Equation of State by Machine Learning

Guo,

Xiong,

et al. 2024

ApJ

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Due to its powerful capability and high efficiency in big data analysis, machine learning has been applied in various fields. We construct a neural network platform to constrain the behaviors of the equation of state of nuclear matter with respect to the properties of nuclear matter at saturation density and the properties of neutron stars. It is found that the neural network is able to give reasonable predictions of parameter space and provide new hints into the constraints of hadron interactions. As a specific example, we take the relativistic mean field approximation in a widely accepted Walecka-type model to illustrate the feasibility and efficiency of the platform. The results show that the neural network can indeed estimate the parameters of the model at a certain precision such that both the properties of nuclear matter around saturation density and global properties of neutron stars can be saturated. The optimization of the present modularly designed neural network and extension to other effective models is straightforward.

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“…Recently, machine learning (ML) has been widely used in physics and nuclear physics [28][29][30][31]. Due to the special importance of nuclear mass, many ML approaches have been employed to improve its description, such as the kernel ridge regression (KRR) [32][33][34], the radial basis function (RBF) [35,36], the Bayesian neural network (BNN) [37][38][39], the Gaussian process regression [40,41], the principal component analysis [42], etc.…”

Section: Introductionmentioning

confidence: 99%

Studies of different kernel functions in nuclear mass predictions with kernel ridge regression

Xin-rong

2023

Front. Phys.

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The kernel ridge regression (KRR) approach has been successfully applied in nuclear mass predictions. Kernel function plays an important role in the KRR approach. In this work, the performances of different kernel functions in nuclear mass predictions are carefully explored. The performances are illustrated by comparing the accuracies of describing experimentally known nuclei and the extrapolation abilities. It is found that the accuracies of describing experimentally known nuclei in the KRR approaches with most of the adopted kernels can reach the same level around 195 keV, and the performance of the Gaussian kernel is slightly better than other ones in the extrapolation validation for the whole range of the extrapolation distances.

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Machine learning in nuclear physics at low and intermediate energies

Cited by 55 publications

References 157 publications

Nuclear charge radius predictions based on eXtreme Gradient Boosting

Nuclear charge radius predictions based on eXtreme Gradient Boosting

Insights into Neutron Star Equation of State by Machine Learning

Studies of different kernel functions in nuclear mass predictions with kernel ridge regression

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