English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Nuclear masses are of great importance in nuclear physics and astrophysics.Descriptive experimental data on nuclear masses and the prediction of unknownmasses based on residual proton-neutron interactions are a focus in nuclearphysics. The accuracy of the residual interaction determines the accuracy ofthe nuclear mass values, so the study of residual interactions is essential.Before we carry out this study, there are many papers using artificial neuralnetworks in nuclear physics. But no one uses BP neural network to studyresidual interactions. In this paper, we obtained a description and predictionmodel for residual interactions based on BP neural network. By combiningexperimental values with residual interactions model, we successfully calculatethe nuclear masses of $A\geq100$. Results demonstrate that the differencesbetween our calculated values and experimental values (AME2003, AME2012 andAME2016) show that the root-mean-squared deviations (RMSDs) are small(comparing with AME2003, the odd-A nuclei RMSD and the even-A nuclei RMSD are112 keV and 128 keV; comparing with AME2012, the odd-A nuclei RMSD and theeven-A nuclei RMSD are 103 keV and 121 keV; comparing with AME2016, the RMSD ofodd-A nuclei and even-A nuclei are 106 keV and 122 keV, respectively). Inaddition, we obtained some predicted masses based on AME2003 and AME2012, thepredicted values have good accuracy and compared well with experimental values(AME2012 and AME2016). The results show that the study of residual interactionsusing the proposed BP neural network method is feasible and accurate. Thismethod is helpful for analyzing and extracting useful information from a largenumber of experimental values and then providing a reference for discoveringphysical laws and support for physical experiments.

Nuclear binding energy predictions based on BP neural network