{"title":"Revisiting Macro-microscopic Mass Formula using Atomic Mass Evaluation-2020 Data","authors":"S. ., O. Sastri","doi":"10.15415/jnp.2022.92028","DOIUrl":null,"url":null,"abstract":"Background: The macro-microscopic model has been succesful in nuclear mass predictionsand in obtaining various other properties of nuclear and nucleon matter. The present statusof generalised liquid drop model (GLDM) has been based on atomic mass evaluation (AME)-2003 data.Purpose: In this work, the co-efficients of most efficient mass formulae from Royer et.al.,have been re-optimised for 2451 selected nuclei from AME-2020 data.Methods: The root mean squared deviation (RMS) is minimized to optimize seven modelparameters that correspond to various terms in the nuclear binding energy that come inpowers of mass number A and square of relative neutron excess I = N −Z/A .Results: The RMS between the theoretical and experimental binding energies has beenobtained as 0.65 using both the formulae.Conclusions: The best possible formula for nuclear binding energy has been obtained usingAME-2020 data and it needs to be seen how this would effect the various nuclear propertiesand predictions.","PeriodicalId":16534,"journal":{"name":"Journal of Nuclear Physics, Material Sciences, Radiation and Applications","volume":"38 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Nuclear Physics, Material Sciences, Radiation and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15415/jnp.2022.92028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: The macro-microscopic model has been succesful in nuclear mass predictionsand in obtaining various other properties of nuclear and nucleon matter. The present statusof generalised liquid drop model (GLDM) has been based on atomic mass evaluation (AME)-2003 data.Purpose: In this work, the co-efficients of most efficient mass formulae from Royer et.al.,have been re-optimised for 2451 selected nuclei from AME-2020 data.Methods: The root mean squared deviation (RMS) is minimized to optimize seven modelparameters that correspond to various terms in the nuclear binding energy that come inpowers of mass number A and square of relative neutron excess I = N −Z/A .Results: The RMS between the theoretical and experimental binding energies has beenobtained as 0.65 using both the formulae.Conclusions: The best possible formula for nuclear binding energy has been obtained usingAME-2020 data and it needs to be seen how this would effect the various nuclear propertiesand predictions.