Bulletin of the American Physical Society
Fall 2024 Joint Meeting of the Texas Section of the APS, Texas Section of the AAPT & Zone 13 of the SPS
Thursday–Saturday, October 17–19, 2024; Southern Methodist University, Dallas, Texas
Session L04: Astrophysics VI
9:00 AM–10:12 AM,
Saturday, October 19, 2024
Southern Methodist University
Room: Ballroom C
Chair: Lei Yang, South Methodist University
Abstract: L04.00006 : Implementation of a model of the neutron star crust with a consistent determination of bulk and surface energies.
10:00 AM–10:12 AM
Presenter:
Savannah Wright
(Texas A&M University Commerce)
Author:
Savannah Wright
(Texas A&M University Commerce)
gravitational pull up to 1012 m/s2. Due to this gravitational pull, the
mass of the neutron star (comparable to that of our sun), is compressed
into an object with a radius of approximately 10 kilometers. Thus,
neutron stars consist of the densest matter in the known universe,
making them optimal for studying fundamental forces in extreme
environments. Neutron stars have a solid crust made of nuclei arranged
in a crystalline structure and a liquid core comprised primarily of
neutrons. Of particular interest is the inner crust which has neutrons
external to the nuclei in the lattice. In order to describe matter here,
one uses the Compressible Liquid-Drop Model (CLDM) which models the
nuclei and neutrons as uniform density nuclear matter separated by a
nuclear surface with a certain surface energy which must be specified
independently. In this work, we implement a method to determine the
surface energy consistently with the bulk energy of nuclear matter. We
take different models of nuclear matter characterized by different
values of the symmetry energy - the energy cost of adding neutrons to
the system - and fit the surface energy parameters in the CLDM to
measurements of nuclear masses. We then use this to assess the
importance of such a consistent calculation for the accurate inference
of properties of the neutron star crust from experimental and
astrophysical data.
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