The BD_wrapper is a Python wrapper for the Bayesian Distance Calculator (BDC) tool presented in Reid et al. 2016 and Reid et al. 2019. The BDC uses a Bayesian approach to establish distances to sources within the Milky Way given Galactic coordinates (GLON, GLAT) and a line of sight velocity (VLSR). The BDC is a data product from the The Bar And Spiral Structure Legacy (BeSSeL) Survey and can be accessed from its webpage. Currently, the BD_wrapper supports both v1 and v2.4 of the BDC.
The BD_wrapper provides a Python implementation for bulk distance assignments for a large number of input sources. Moreover, it contains two new priors that help to solve for the kinematic distance ambiguity (KDA). The first new prior considers literature solutions for the KDA by automatically matching the source coordinates with literature sources. The second new prior requires additional information about the velocity dispersion of the source and uses a size-linewidth relationship to solve for the KDA.
For a detailed description about the new priors and results of tests performed with the BD_wrapper on decomposition results from the Galactic Ring Survey (Riener et al. 2020a) please see:
Riener et al. 2020b (in prep.)
All credit for the (BDC) and its original Fortran implementation is due to Reid et al. 2016 and Reid et al. 2019 and should be acknowledged as such.
For tips on how to get started with the BD_wrapper see the section Getting started further below. See also the notebook Parameter_settings.ipynb for an overview about the parameter settings.
The currently recommended version of the BD_wrapper is v0.1. See the BD_wrapper Changelog for an overview of the major changes and improvements introduced by newer versions currently in development.
New updates to the code are first tested and developed in the dev branch. Users cloning the dev branch should beware that these versions are not guaranteed to be stable.
The BD_wrapper already includes the necessary files to run the BDC tool. However, you need to be able to execute Fortran (.f) files on your operating system. Moreover, you will need the following packages to run the BD_wrapper. We list the version of each package which we know to be compatible with the BD_wrapper.
If you do not already have Python 3.5, you can install the Anaconda Scientific Python distribution, which comes pre-loaded with numpy.
Download the BD_wrapper using git $ git clone https://github.com/mriener/BD_wrapper.git
Install pip for easy installation of python packages:
sudo apt-get install python-pipThen install the required python packages:
sudo pip install astropy matplotlib numpy tqdmInstall pip for easy installation of python packages:
sudo easy_install pipThen install the required python packages:
sudo pip install astropy matplotlib numpy tqdmThe notebook Parameter_settings.ipynb gives an overview about parameter settings for the BD_wrapper.
The Tutorial-batch_distance_estimation.ipynb notebook guides users through the process of using the BD_wrapper to estimate distances for a large table of input sources.
The Tutorial-plot_distance_pdf.ipynb notebook shows how to plot the distance probability density results obtained by the BDC and how to retain temporary files that can be important for debugging and obtaining diagnostics of the distance calculation.
The BD_wrapper uses literature distance results to inform the P_far prior that helps resolve the kinematic distance ambiguity (KDA). The BD_wrapper currently contains twelve catalogues (called KDA info tables) that mostly cover regions in the first Galactic quadrant. In the Example-KDA_info_table.ipynb we show how to easily create a new KDA info table for a catalogue that is not yet included in the KDA_info directory.
If you make use of this package in a publication, please consider the following acknowledgements:
@ARTICLE{2019ApJ...885..131R,
author = {{Reid}, M.~J. and {Menten}, K.~M. and {Brunthaler}, A. and
{Zheng}, X.~W. and {Dame}, T.~M. and {Xu}, Y. and {Li}, J. and
{Sakai}, N. and {Wu}, Y. and {Immer}, K. and {Zhang}, B. and
{Sanna}, A. and {Moscadelli}, L. and {Rygl}, K.~L.~J. and
{Bartkiewicz}, A. and {Hu}, B. and {Quiroga-Nu{\~n}ez}, L.~H. and
{van Langevelde}, H.~J.},
title = "{Trigonometric Parallaxes of High-mass Star-forming Regions: Our View of the Milky Way}",
journal = {\apj},
keywords = {Milky Way, Milky Way dynamics, Milky Way rotation, Trigonometric parallax, Star formation, Gravitational wave sources, Astrophysics - Astrophysics of Galaxies},
year = 2019,
month = nov,
volume = {885},
number = {2},
eid = {131},
pages = {131},
doi = {10.3847/1538-4357/ab4a11},
archivePrefix = {arXiv},
eprint = {1910.03357},
primaryClass = {astro-ph.GA},
adsurl = {https://ui.adsabs.harvard.edu/abs/2019ApJ...885..131R},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@ARTICLE{2016ApJ...823...77R,
author = {{Reid}, M.~J. and {Dame}, T.~M. and {Menten}, K.~M. and {Brunthaler}, A.},
title = "{A Parallax-based Distance Estimator for Spiral Arm Sources}",
journal = {\apj},
keywords = {Galaxy: structure, parallaxes, stars: formation, Astrophysics - Astrophysics of Galaxies},
year = 2016,
month = jun,
volume = {823},
number = {2},
eid = {77},
pages = {77},
doi = {10.3847/0004-637X/823/2/77},
archivePrefix = {arXiv},
eprint = {1604.02433},
primaryClass = {astro-ph.GA},
adsurl = {https://ui.adsabs.harvard.edu/abs/2016ApJ...823...77R},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Citation courtesy of ADS.
Please also consider acknowledgements to the required dependencies in your work.
If you should find that the BD_wrapper does not perform as intended for your dataset or if you should come across bugs or have suggestions for improvement, please get into contact with us or open a new Issue or Pull request.
To contribute to the BD_wrapper, see Contributing to the BD_wrapper
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent