wdtools

Documentation | Read the Paper

Installation

Simply clone the repository to your local machine and run the following code from the base directory:

python setup.py install

This will install the code into your current Python environment, and you should be able to simply import wdtools into your work. If you use conda, ensure that you have activated the environment within which you wish you use this code before running setup.py. This code is writen and tested in Python 3.7 and 3.8 only.

Example Usage

A full demo is presented in this Jupyter Notebook.

Citing This Work

If you use wdtools for your research, we would appreciate if you cite the Zenodo repository linked above, as well as our paper describing the package. A BibTeX reference is reproduced below for convenience.

@ARTICLE{Chandra2020,
  author = {{Chandra}, Vedant and {Hwang}, Hsiang-Chih and {Zakamska}, Nadia L. and {Budav{\'a}ri}, Tam{\'a}s},
  title = "{Computational tools for the spectroscopic analysis of white dwarfs}",
  journal = {\mnras},
  year = 2020,
  month = jul,
  volume = {497},
  number = {3},
  pages = {2688-2698},
  doi = {10.1093/mnras/staa2165}
}

@MISC{wdtools, 
  title={wdtools: Computational Tools for the Spectroscopic Analysis of White Dwarfs}, 
  DOI={10.5281/zenodo.3828007}, 
  publisher={Zenodo}, 
  author={Vedant Chandra}, 
  year={2020}
}

Other References

You may also be interested in https://github.com/SihaoCheng/WD_models, which provides tools to simulate and fit white dwarf spectral energy distributions (SEDs) and photometry using synthetic color tables. Another relevant tool is https://github.com/gnarayan/WDmodel, which provides the ability to fit non-LTE white dwarf models (for higher temperatures) to spectroscopy and photometry, and has a more sophisticated treatment of extinction.

If using the pre-trained generative neural network for white dwarf model atmospheres, kindly cite the original paper that describes these models:

Koester (2010)

These models also incorporate physics from the following papers (this list is not exhaustive):

Fontaine (2001)

Tremblay & Bergeron (2009)

Tassoul et al. (1990)

The random forest regression model is trained using labelled spectra from the Sloan Digital Sky Survey and Tremblay et al. (2019)