NSHeat

Nuetron star cooling and heating code.

The code solves temperature evolution of spherically symmetric and isothermal NS, along with the evolution of the imbalance among chemical potentials.

The imbalance causes the internal heating.

The code can also incorporate the heating caused by dark matter accretion.

Julia installation

See the official site.

The code is tested on Julia 1.3.1.

For mac homebrew user

$ brew cask install julia

Package installation

Please install gfortran in advance.

From GitHub

$ julia
julia> ]
(v1.3) pkg> add https://github.com/yng87/NSHeat.jl.git

Then in your julia code, you can use NSHeat by

using NSHeat

The dependent modules are automatically installed.

If the automatic installation fails, please install them manually:

pkg> add [package name]

When you encounter error in building NSHeat, try pkg> resolve.

Manual installation

If you build it by yourself, just download zip. In your julia code, specify the path explicitly:

push!(LOAD_PATH, "path/to/NSHeat")
using NSHeat

You may need to install other packages by add [package name].

How to run

Please take a look at test/test.ipynb.

Set Model parameters

One can set the parameters of a neutron star by the following ways:

1. From input cards: You can pass paramter card by

model, core, env, var = setup("path/to/ini/file/")

These four variables are basic objects in NSHeat, corresponding to the following four structs respectively:

• ModelParams: model specification such as EOS or superfluid gap models.

• StarCoreParams: T=0 quantities given by EOS such as Fermi momenta or critical temperatures of nucleons.

• EnvelopeParams: envelope paramters govering the surface photon emission

• StarVariables: temperature and chemical potentials which change by time

2. Directly in the code: See e.g., scripts/test_cool.jl

Solve ODE

Then you can solve cooling/heating ODE by

sol = cooling(model, core, env, var)

or

sol = heating(model, core, env, var)

sol stores the solution of ODE.

The results are placed in the directory you specified in model.output_dir

write_ini(sol, model) # save the model parameters

output_T(sol, model, core, env, var) # save temperature and chemical potential

output_LC(sol, model, core, env, var) # save luminosities and heat capacities

ODE Solver

Julia DifferentialEquation.jl offers a lot of solvers for an ODE problem. Among them, radau is the best for this NS evolution, in particular for late time heating problem.