DETERMINATION OF THE PROPERTIES OF NUCLEAR MATTER USING THE RELATIVISTIC MEAN FIELD MODEL

• Main Author: Nabiila Kamilah, Novi
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/80623
• Institution: Institut Teknologi Bandung

The nuclear equation of state (EOS) exhibits uncertainty as it is not directly derived from the fundamental theory of strong interaction, Quantum Chromodynamics (QCD), but rather originates from phenomenological models. One model that provides a reasonably accurate description of nuclear matter is the Relativistic Mean Field (RMF) model. This model depicts the interaction between nucleons in the form of exchanging mesons. This study focuses on determining the properties of nuclear matter, particularly Symmetric Nuclear Matter (SNM) and Pure Neutron Matter (PNM), using the RMF model approach. SNM is nuclear matter with an equal proportion of protons and neutrons, while PNM consists solely of neutrons without any protons. The main objective is to investigate the properties of SNM and PNM such as binding energy, pressure, and energy density by comparing six parameter sets of the RMF model inspired by Chiral Perturbation Theory. The research methodology comprises three main stages. Firstly, conducting a literature review to establish a solid theoretical basis. Secondly, calculating the desired properties of the formulated EOS using FORTRAN programming. Thirdly, presenting the calculated data in graphical form using the Graphic Layout Engine (GLE) program. The results of this study encompass a series of graphs representing the properties of SNM and PNM, including binding energy, pressure, and energy density, using six different parameter sets. Significant differences in binding energy between SNM and PNM occur due to the role of symmetric energy in PNM. The parameter sets of the RMF model inspired by Chiral Perturbation Theory are capable of depicting the properties of nuclear matter more accurately than those of the standard RMF model parameter sets. This is due to the higher compressibility value, which enhances the accuracy of the parameter sets.