Prediction of nuclear charge density distribution with feedback neural network

Shang, Tian-Shuai; Li, Jian; Niu, Zhong-Ming

doi:10.1007/s41365-022-01140-9

Cited by 21 publications

(5 citation statements)

References 89 publications

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“…In Ref. [30], ANN is used to predict the parameters c and z of a two-parameter Fermi(2pF) distribution, which is assumed for the nuclear charge distributions. Two kinds of inputs (Z, N, Z 1/3 ) and (Z, N, Z 1/3 , A 1/3 ) are used.…”

Section: Machine Learning For Nuclear Charge Radiimentioning

confidence: 99%

“…As a fundamental observable in nuclear theory, it is necessary to be able to accurately calculate the charge radii of nuclei that have not been measured, which is called data mining based on ML. There have been several ML models that are effectively applied to depict and predict nuclear charge radii [24][25][26][27][28][29][30][31]. These applications are also not limited to reducing the RMS deviation of the data set, but also to reproducing and extrapolating the charge radii of isotopic chains whose increasing trends with the neutron number could reflect some underlying physical information.…”

Section: Introductionmentioning

confidence: 99%

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Progress of Machine Learning Studies on the Nuclear Charge Radii

Fang

2023

Symmetry

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The charge radius is a fundamental physical quantity that describes the size of one nucleus, but contains rich information about the nuclear structure. There are already many machine learning (ML) studies on charge radii. After reviewing the relevant works in detail, the convolutional neural networks (CNNs) are established to reproduce the latest experimental values of charge radii. The extrapolating and interpolating abilities in terms of two CNN structures partnering two inputting matrix forms are discussed, and a testing root-mean-square (RMS) error 0.015 fm is achieved. The shell effect on charge radii of both isotones and isotopes are predicted successfully, and the CNN method works well when predicting the charge radii of a whole isotopic chain.

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Section: Machine Learning For Nuclear Charge Radiimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Progress of Machine Learning Studies on the Nuclear Charge Radii

Fang

2023

Symmetry

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“…The obtained strength functions are smeared with the Lorentzian function with a 1.0 MeV width. We note that ∆R n and α D for 48 [33,48,[63][64][65][66] that beyond-mean-field effects may be indispensable to calculate properties of 40 Ca and 48 Ca consistently, while the mean-field calculation is used in this paper. Hence, in this study, we use properties of 132 Sn and 208 Pb obtained by the RPA calculation.…”

Section: Eos For Neutron Star Matter and Experimental Variablesmentioning

confidence: 99%

Empirical neutron star mass formula based on experimental observables

Sotani

Naito

2023

Phys. Rev. C

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We derive the empirical formulae expressing the mass and gravitational redshift of a neutron star, whose central density is less than threefold the nuclear saturation density, as a function of the neutron-skin thickness or the dipole polarizability of 208 Pb or 132 Sn, especially focusing on the 8 Skyrme-type effective interactions. The neutron star mass and its gravitational redshift can be estimated within ≈ 10 % errors with our formulae, while the neutron star radius is also expected within a few % errors by combining the derived formulae. Owing to the resultant empirical formulae, we find that the neutron star mass and radius are more sensitive to the neutron-skin thickness of 208 Pb than the dipole polarizability of 208 Pb or 132 Sn.

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“…值 [8−11] 进行对比, 图中的实心圆点(Dev1)和实心五角形(Dev2)分别表示基于经验公式( 7)和神经网络模型 Ⅰ得到 37 K, 48 基于AME2020 [52] 和CR2013 [4,5] 少, 分别为σ = 0.018 fm 和σ = 0.014 fm, 且得到的预言值近几年测得的实验值 [8−11] 也较接近. 研究结果表明中子因子 1 N 修正和中子壳层效应 [30] 修正的添加可以较好地描述核电荷半径, 而且本文提出的关系式具有一定的简便性和可操作性, 虽然有些核电荷半径的计算值和预言值误差较大, 这也是不可避免的, 毕竟复杂的核电荷半径问题不可能通过一个简单公式进行精确的全局性描述和预言.…”

Section: 原子核密度的研究unclassified

Nucleus density based new relationship of nuclear charge radius

Bao-Bao¹

2023

Acta Phys. Sin.

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This paper we obtained the description and evaluation value of the nuclear charge radii by analyzes the relationship between nuclear masses and nuclear charge radii. We got 884 nuclei (Z, N ≥ 8) with known mass and charge radii by combining AME2020 database with CR2013 database, and then obtained the quality density ρm of 884 nuclear. Our purpose is to describe an empirical formula with one constant for ρm of nuclear masses that can be useful in describing and predicting nuclear charge radius with Z ≥ 8 and N ≥ 8. With the empirical formula and AME2020 database, the root-mean-square deviation (RMSD) of the nuclear charge radius that we have successfully obtained σ = 0.093 fm. Considering the impact of neutron numbers on ρm, we use the neutron factor 1/N to correct the empirical formula, and then the RMSD is reduced to σ = 0.047 fm (the accuracy is increased by about 50%). The second correction is shell effect of neutrons, results show that the RMSD of nuclear charge radius was reduced about 0.013 fm based on shell effect of neutrons. We used the empirical formula with corrections to predict nuclear charge radius (1573 nuclear charge radius with Z, N ≥ 8) that is difficult to measure experimentally. The difference between our predicted values based on AME2020 database and experimental values measured in recent years is in the allowable range of deviation. The result shows that the new relation for nuclear charge radius has simplicity and reliability. In addition, we deleted some nuclei with special shell effect and isotope chains, which the RMSD of the calculation value for 791 nuclei is reduced to σ = 0.032 fm. These results show that the new relation proposed in this paper can be comparable to A1/3 and Z1/3 formulas with corrections. Moreover, we study the 884 and 791 nuclear quality density by using L-M neural network method to build description and prediction models. Then obtained the calculated values and predicted values of nuclear charge radius. Comparing with CR2013, the RMSDs of nuclear charge radius are σ = 0.018 fm and σ = 0.014 fm, respectively. The RMSDs are reduced by about 50% than the empirical formula with corrections, and the predicted values are closer to the experimental values measured in recent years.

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Prediction of nuclear charge density distribution with feedback neural network

Cited by 21 publications

References 89 publications

Progress of Machine Learning Studies on the Nuclear Charge Radii

Progress of Machine Learning Studies on the Nuclear Charge Radii

Empirical neutron star mass formula based on experimental observables

Nucleus density based new relationship of nuclear charge radius

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