s17lc

This is a repository for the radio light curve program used in Sarbadhicary et al 2017. Please cite the paper if you use the code for your project. Here is the BibteX citation:

   author = {{Sarbadhicary}, S.~K. and {Badenes}, C. and {Chomiuk}, L. and 
	{Caprioli}, D. and {Huizenga}, D.},
    title = "{Supernova remnants in the Local Group - I. A model for the radio luminosity function and visibility times of supernova remnants}",
  journal = {\mnras},
archivePrefix = "arXiv",
   eprint = {1605.04923},
 primaryClass = "astro-ph.HE",
 keywords = {acceleration of particles, ISM: supernova remnants, Local Group, radio continuum: ISM},
     year = 2017,
    month = jan,
   volume = 464,
    pages = {2326-2340},
      doi = {10.1093/mnras/stw2566},
   adsurl = {http://adsabs.harvard.edu/abs/2017MNRAS.464.2326S},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Installation

You can do the following :-

1. If you have git installed, you can directly clone this repository using git clone https://github.com/sks67/s17lc.git into your computer
2. Copy paste the s17lc.py into a blank .py file directly in your computer and use it accordingly.

Description

The program s17lc.py is the coded up version of the equations A1-A11 in the paper. There are two main modules :-

1. radius_velocity(): This provides the radius and velocity of the supernova remnant for a given age, based on the dimensional variables and self similar solutions in Truelove & Mckee 1999.

2. luminosity(): This provides the luminosity of the supernova remnant, given the radius, velocity, density and other parameters.

Usage

You can use these to create light curves or estimate values at a particular age. I have included a Jupyter notebook, test_s17lc.ipynb to show how to generate radius, velocity, and light curves with the code.

Miscellaneous Notes

The code is still being developed, so please periodically check back at the Github page.

Please also check the Erratum to the Paper: Sarbadhicary et al (2019) for corrections to some typographical errors in the paper.

The code is written with Python 2.7, although it should work with Python 3+. It should only require numpy and matplotlib installed.

The constants c1, c5, and c6 have a small dependence on the spectral index. I'll update this in the next iteration.