This repository provides code that allows the calculation of relevant neutron star parameters as a function of density for a given set of Skyrme parameters t0, t1, t2, t3, t4, x0, x1, x2, x3, x4, σ, and σ2.
To determine the composition of the nuclear matter inside the neutron star core, we follow
Chamel (2008)
and solve four equations related to baryon conservation, charge neutrality, beta equilibrium and muon production rate
for a given set of Skyrme parameters. Implemented in ns_eos/equilibrium_comp.py,
this provides number densities, particle fractions, Fermi wave numbers,
Landau effective masses and dynamic effective masses related to entrainment
(see Chammel and Haensel (2006)
for details) for a given baryon density.
To determine the properties of the superfluids (specifically their coherence lengths),
we employ the gap parametrisation introduced in
Andersson et al. (2005)
with parameters for the singlet proton and triplet neutron gap given in Ho et al. (2015).
Details of the gap calculation can be found in ns_eos/gap_parametrisation.py.
These instructions will provide you with a copy of the project and help you get it up and running on your local machine. The repo contains an environment file that can be installed by running
$ conda env create -f environment.yaml
We recommend working within this environment when using the code. To install the package run
$ python setup.py install
To see if the installation has been successful, we provide an example file. Running
$ python examples/generating_eos_data.py
will create .txt files for six different sample equations of states (NRAPR, SLy4, LNS, SQMC700, Ska35s20, Skχ450)
in examples/data/, providing a range of parameters as a function of baryon density.
Note that by default, an instance of the EquationOfState class will be created based on the Skyrme parameters
of the NRAPR equation of state. If you would like to choose a different equation of state,
specify the corresponding Skyrme parameters t0, t1, t2, t3, t4,
x0, x1, x2, x3, x4, σ, and σ2
as shown in examples/generating_eos_data.py.
All methods and functions in this repository have been tested. Tests can be found here. Running
$ python setup.py test
will run the tests and output a coverage report.
This project is licensed under the MIT License - see the LICENSE.md file for details