Predictions of nuclear charge radii and physical interpretations based on the naive Bayesian probability classifier

Ma, Yunfei; Su, Chen; Liu, Jian; Zhang, Ren; Xu, Chang; Gao, Yonghao

doi:10.1103/physrevc.101.014304

Cited by 64 publications

(39 citation statements)

References 52 publications

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“…In Ref. [21], a statistical method is introduced to study nuclear charge radii by combining sophisticated nuclear models with the naive Bayesian probability (NBP) classifier. This method predicts a rapid increase of charge radii beyond N = 28.…”

Section: Introductionmentioning

confidence: 99%

Charge radii of potassium isotopes in the RMF(BCS)* approach

An,

Zhang,

Geng

et al. 2021

Preprint

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We apply the recently proposed RMF(BCS)* ansatz to study the charge radii of the potassium isotopic chain up to 52 K. It is shown that the experimental data can be reproduced rather well, qualitatively similar to the Fayans nuclear density functional theory, but with a slightly better description of the odd-even staggerings (OES). Nonetheless, both methods fail for 50 K and to a lesser extent for 48,52 K. It is shown that if these nuclei are deformed with a β20 ≈ −0.2, then one can obtain results consistent with experiments for both charge radii and spin-parities. We argue that beyond mean field studies are needed to properly describe the charge radii of these three nuclei, particularly for 50 K.

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Section: Introductionmentioning

confidence: 99%

Charge radii of potassium isotopes in the RMF(BCS)* approach

An,

Zhang,

Geng

et al. 2021

Preprint

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“…Although these algebraic expressions provide a simple method for estimating nuclear charge radii, the fine details of nuclear structure cannot be covered well. However, as a prior constraint relation, a available formula is necessary in the simulations of machine learning process [41,50].…”

Section: Theoretical Approachesmentioning

confidence: 99%

Local variations of charge radii for nuclei with even $Z$ from 84 to 120

An¹,

Dong²,

Li³

et al. 2021

Preprint

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In this work, the evolution of nuclear charge radii along even Z = 98 − 120 isotopes are systematically investigated by various approaches. The new parametrization sets based on liquid drop model (LDM) formulas are proposed by covering the shell closure effect at N = 126 and odd-even staggering (OES) in calibration procedure. In comparison with available database, highorder neutron-proton asymmetry may be indispensable in fitting protocol. The fitted formula within theoretical uncertainty below 0.02 fm is used to make extrapolative calculations. Results obtained by the deduced relationship associating to α-decay properties and the modified charge radius formula in relativistic mean field (RMF) model are also employed to make reinforce. The latter shows that the abrupt change of nuclear charge radii emerges apparently across N = 184 shell closure. In addition, the abrupt jumps at N = 146 and 200 are also disclosed remarkably due to the large deformation. These different methods give comparable predictions of nuclear charge radii for 286,288 Fl, 290,292 Lv and 288−310 120 isotopes.

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“…Neural network, an algorithm of machine learning, has been widely used in different fields such as artificial intelligence(AI), medical treatment, and physics of complex systems. There are many successful applications of machine learning in nuclear physics, for examples, predictions of the nuclear mass [36][37][38], charge radii [39,40], dripline locations [41,42], β-decay half-lives T 1/2 [43], the fission product yields [44], and the isotopic cross-sections in proton induced spallation reactions [45]. Very recently, a multilayer neural network was applied to predict the ground-state and excited energies with high accuracy [46].…”

Section: Introductionmentioning

confidence: 99%

Study of Charge Radii with Neural Networks

Wu,

Bai,

Sagawa

et al. 2020

Preprint

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Predictions of nuclear charge radii and physical interpretations based on the naive Bayesian probability classifier

Cited by 64 publications

References 52 publications

Charge radii of potassium isotopes in the RMF(BCS)* approach

Charge radii of potassium isotopes in the RMF(BCS)* approach

Local variations of charge radii for nuclei with even $Z$ from 84 to 120

Study of Charge Radii with Neural Networks

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