Welcome to WDmodel’s documentation!

WDmodel: Bayesian inference of white dwarf properties from spectra and photometry to establish spectrophotometric standards


Copyright 2017- Gautham Narayan (gsnarayan@gmail.com)


Github Link GPLv3 License Documentation Status

WDmodel is a DA White Dwarf model atmosphere fitting code. It fits observed spectrophotometry of DA White Dwarfs to infer intrinsic model atmosphere parameters in the presence of dust and correlated spectroscopic flux calibration errors, thereby determining full SEDs for the white dwarf. Its primary scientific purpose is to establish a network of faint (V = 16.5–19 mag) standard stars, suitable for LSST and other wide-field photometric surveys, and tied to HST and the CALSPEC system, defined by the three primary standards, GD71, GD153 and G191B2B.

Click on the badges above for code, licensing and documentation.


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The code has been tested on Python 2.7 and 3.6 on both OS X (El Capitan and Sierra) and Linux (Debian-derivatives). Send us email or open an issue if you need help!


We’re working on a publication with the results from our combined Cycle 22 and Cycle 20 data, while ramping up for Cycle 25! A full data release of Cycle 20 and 22 HST data, and ground-based imaging and spectroscopy will accompany the publication. Look for an updated link here!

You can read the first version of our analysis of four of the Cycle 20 objects here

That analysis was intended as a proof-of-concept and used custom IDL routines from Jay Holberg (U. Arizona) to infer DA intrinsic parameters and custom python code to fit the reddening parameters. This code is intended to (significantly) improve on that analysis.


This document will help get you up and running with the WDmodel package.

For the most part, you can simply execute code in grey boxes to get things up and running, and ignore the text initially. Come back to it when you need help, or to configure the fitter.

Indices and tables