Nuclear charge radii in Bayesian neural networks revisited

Dong, Xiao-Xu; An, Rong; Lu, Jun-Xu; Geng, Li-Sheng

doi:10.1016/j.physletb.2023.137726

Cited by 31 publications

(8 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In this study, 699 randomly selected data were used as training data and the remaining 234 as testing data to evaluate the performance of the models. The predictive variables were taken as in the study of Dong et al [26]. These are mass number (A), proton number (Z), isospin dependence (I 2 ), pairing term (δ), the promiscuity factor (P) related to shell closure effects [28,29], and a term related to abnormal charge radii behavior in 181,183,185 Hg (LI).…”

Section: The Data Structure and Software Resourcesmentioning

confidence: 99%

“…In the illustrations carried out on Ca and K isotope chains, it was shown that while the NP formula exhibits a linear behavior, it is in agreement with the experimental data if it is supported by a Bayesian neural network. Later, authors revisited their study [26] to reduce the rms deviation difference (%30) between the validation set and training set which can cause a possible over-fitting. For this purpose, they added new features containing physical information.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Applications of different machine learning methods on nuclear charge radius estimations

Bayram,

Yeşilkanat,

Akkoyun

2023

Phys. Scr.

View full text Add to dashboard Cite

Theoretical models come into play when the radius of nuclear charge, one of the most fundamental properties of atomic nuclei, cannot be measured using different experimental techniques. As an alternative to these models, machine learning (ML) can be considered as a different approach. In this study, ML techniques were performed using the experimental charge radius of 933 atomic nuclei (A ≥ 40 and Z ≥ 20) available in the literature. In the calculations in which eight different approaches were discussed, the obtained outcomes were compared with the experimental data, and the success of each ML approach in estimating the charge radius was revealed. As a result of the study, it was seen that the Cubist model approach was more successful than the others. It has also been observed that ML methods do not miss the different behavior in the magic numbers region.

show abstract

Section: The Data Structure and Software Resourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applications of different machine learning methods on nuclear charge radius estimations

Bayram,

Yeşilkanat,

Akkoyun

2023

Phys. Scr.

View full text Add to dashboard Cite

show abstract

“…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]. And machine learning methods can generally achieve higher prediction accuracies compared to the traditional nuclear theoretical models.…”

Section: Introductionmentioning

confidence: 99%

Nuclear charge radius predictions based on eXtreme Gradient Boosting

Li,

Zhang,

Fang

2024

Phys. Scr.

View full text Add to dashboard Cite

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.

show abstract

“…Recently developed Bayesian neural networks (BNNs) have been devoted to evaluatingthe accurate predictions of nuclear charge radii, in which the theoretical error bars can also be given [53][54][55][56][57]. The kernel ridge regression (KRR) method has also been used to learn nuclear charge radii [58].…”

Section: Introductionmentioning

confidence: 99%

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

Dong

Cao

et al. 2023

Commun. Theor. Phys.

View full text Add to dashboard Cite

Pronounced changes of nuclear charge radii provide a stringent benchmark on the theoretical models and play a vital role in recognizing various nuclear phenomena. In this work, the systematic evolutions of nuclear charge radii along even $Z$=84-120 isotopic chains are firstly investigated by the recently developed new ansatz under the covariant density functional. The calculated results show that the shell closure effects of nuclear charge radii appear remarkably at the neutron numbers $N=126$ and 184. Interestingly, the arch-like shapes of charge radii between these two strong neutron closed shells are naturally observed. Across the $N=184$ shell closure, the abrupt increase in charge radii is still evidently emerged. In addition, the rapid raise of nuclear charge radii from the neutron numbers $N=138$ to $N=144$ is disclosed clearly in superheavy regions due to the enhanced shape deformation.

show abstract

Nuclear charge radii in Bayesian neural networks revisited

Cited by 31 publications

References 33 publications

Applications of different machine learning methods on nuclear charge radius estimations

Applications of different machine learning methods on nuclear charge radius estimations

Nuclear charge radius predictions based on eXtreme Gradient Boosting

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

Contact Info

Product

Resources

About