Elena V. Vladimirova, M. Simonov, Tatiana Yu. Tretyakova
{"title":"Phenomenological methods for nuclear mass evaluation at the limits of the nuclear chart","authors":"Elena V. Vladimirova, M. Simonov, Tatiana Yu. Tretyakova","doi":"10.1063/5.0063340","DOIUrl":null,"url":null,"abstract":"Evaluations of nuclear binding energies are obtained, and proton and neutron drip lines are localized using method of local mass relations. The formula describing the residual neutron-proton interaction is approximated and used to obtain estimates. Results based on several compilations of experimental data are obtained to confirm the robustness of the method. In addition, results for binding energies of superheavy nuclei are obtained by machine learning method based on support vector regression. Binding energies of several neighbouring nuclei are used as input parameters. Comparison with other works estimates shows reliable accuracy of the results.","PeriodicalId":296008,"journal":{"name":"PROCEEDINGS OF THE 24TH INTERNATIONAL SCIENTIFIC CONFERENCE OF YOUNG SCIENTISTS AND SPECIALISTS (AYSS-2020)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PROCEEDINGS OF THE 24TH INTERNATIONAL SCIENTIFIC CONFERENCE OF YOUNG SCIENTISTS AND SPECIALISTS (AYSS-2020)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1063/5.0063340","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5
Abstract
Evaluations of nuclear binding energies are obtained, and proton and neutron drip lines are localized using method of local mass relations. The formula describing the residual neutron-proton interaction is approximated and used to obtain estimates. Results based on several compilations of experimental data are obtained to confirm the robustness of the method. In addition, results for binding energies of superheavy nuclei are obtained by machine learning method based on support vector regression. Binding energies of several neighbouring nuclei are used as input parameters. Comparison with other works estimates shows reliable accuracy of the results.