Computational Tools for the Spectroscopic Analysis of White Dwarfs

TL;DR

This paper introduces two machine learning-based methods for extracting physical parameters from white dwarf spectra, providing an accessible, open-source tool that matches previous accuracy levels and aids in identifying unusual stellar systems.

Contribution

It presents a generative neural network and a random forest model for white dwarf spectral analysis, packaged as an open-source Python tool, improving accessibility and efficiency.

Findings

Achieved comparable accuracy to existing methods

Developed an open-source Python package 'wdtools'

Enabled identification of outlier white dwarf systems

Abstract

The spectroscopic features of white dwarfs are formed in the thin upper layer of their stellar photosphere. These features carry information about the white dwarf's surface temperature, surface gravity, and chemical composition (hereafter 'labels'). Existing methods to determine these labels rely on complex ab-initio theoretical models which are not always publicly available. Here we present two techniques to determine atmospheric labels from white dwarf spectra: a generative fitting pipeline that interpolates theoretical spectra with artificial neural networks, and a random forest regression model using parameters derived from absorption line features. We test and compare our methods using a large catalog of white dwarfs from the Sloan Digital Sky Survey (SDSS), achieving the same accuracy and negligible bias compared to previous studies. We package our techniques into an open-source Python module 'wdtools' that provides a computationally inexpensive way to determine stellar labels from white dwarf spectra observed from any facility. We will actively develop and update our tool as more theoretical models become publicly available. We discuss applications of our tool in its present form to identify interesting outlier white dwarf systems including those with magnetic fields, helium-rich atmospheres, and double-degenerate binaries.