@article{._Sastri_2022, title={Revisiting Macro-microscopic Mass Formula using Atomic Mass Evaluation-2020 Data}, volume={9}, url={https://jnp.chitkara.edu.in/index.php/jnp/article/view/320}, DOI={10.15415/jnp.2022.92028}, abstractNote={<p><strong>Background:</strong> The macro-microscopic model has been succesful in nuclear mass predictions<br />and in obtaining various other properties of nuclear and nucleon matter. The present status<br />of generalised liquid drop model (GLDM) has been based on atomic mass evaluation (AME)-<br />2003 data.<br /><strong>Purpose:</strong> In this work, the co-efficients of most efficient mass formulae from Royer et.al.,<br />have been re-optimised for 2451 selected nuclei from AME-2020 data.<br /><strong>Methods:</strong> The root mean squared deviation (RMS) is minimized to optimize seven model<br />parameters that correspond to various terms in the nuclear binding energy that come in<br />powers of mass number A and square of relative neutron excess I = N −Z/A .<br /><strong>Results:</strong> The RMS between the theoretical and experimental binding energies has been<br />obtained as 0.65 using both the formulae.<br /><strong>Conclusions:</strong> The best possible formula for nuclear binding energy has been obtained using<br />AME-2020 data and it needs to be seen how this would effect the various nuclear properties<br />and predictions.</p>}, number={2}, journal={Journal of Nuclear Physics, Material Sciences, Radiation and Applications}, author={., Swapna and Sastri, O.S.K.S.}, year={2022}, month={Jun.}, pages={193–196} }