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Hunting for Polluted White Dwarfs and Other Treasures with Gaia XP Spectra and Unsupervised Machine Learning

Malia L. Kao, Keith Hawkins, Laura K. Rogers, Amy Bonsor, Bart H. Dunlap, Jason L. Sanders, M. H. Montgomery, D. E. Winget

TL;DR

A 2D map is constructed using an unsupervised machine-learning technique called Uniform Manifold Approximation and Projection (UMAP) to organize the WDs into identifiable spectral regions and it is shown that this selection method could potentially increase the number of known WDs with five or more metal species in their atmospheres by an order of magnitude.

Abstract

White dwarfs (WDs) polluted by exoplanetary material provide the unprecedented opportunity to directly observe the interiors of exoplanets. However, spectroscopic surveys are often limited by brightness constraints, and WDs tend to be very faint, making detections of large populations of polluted WDs difficult. In this paper, we aim to increase considerably the number of WDs with multiple metals in their atmospheres. Using 96,134 WDs with Gaia DR3 BP/RP (XP) spectra, we constructed a 2D map using an unsupervised machine learning technique called Uniform Manifold Approximation and Projection (UMAP) to organize the WDs into identifiable spectral regions. The polluted WDs are among the distinct spectral groups identified in our map. We have shown that this selection method could potentially increase the number of known WDs with 5 or more metal species in their atmospheres by an order of magnitude. Such systems are essential for characterizing exoplanet diversity and geology.

Hunting for Polluted White Dwarfs and Other Treasures with Gaia XP Spectra and Unsupervised Machine Learning

TL;DR

A 2D map is constructed using an unsupervised machine-learning technique called Uniform Manifold Approximation and Projection (UMAP) to organize the WDs into identifiable spectral regions and it is shown that this selection method could potentially increase the number of known WDs with five or more metal species in their atmospheres by an order of magnitude.

Abstract

White dwarfs (WDs) polluted by exoplanetary material provide the unprecedented opportunity to directly observe the interiors of exoplanets. However, spectroscopic surveys are often limited by brightness constraints, and WDs tend to be very faint, making detections of large populations of polluted WDs difficult. In this paper, we aim to increase considerably the number of WDs with multiple metals in their atmospheres. Using 96,134 WDs with Gaia DR3 BP/RP (XP) spectra, we constructed a 2D map using an unsupervised machine learning technique called Uniform Manifold Approximation and Projection (UMAP) to organize the WDs into identifiable spectral regions. The polluted WDs are among the distinct spectral groups identified in our map. We have shown that this selection method could potentially increase the number of known WDs with 5 or more metal species in their atmospheres by an order of magnitude. Such systems are essential for characterizing exoplanet diversity and geology.
Paper Structure (9 sections, 2 equations, 2 figures)

This paper contains 9 sections, 2 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Data
  3. The Gentile-Fusillo WD Catalog
  4. The Gaia XP Spectra
  5. Sample Preparation
  6. The Montreal White Dwarf Database
  7. Methods
  8. Categorizing Gaia DR3 WDs with UMAP
  9. Results

Figures (2)

  • Figure 1: About 1 million sources in Gaia DR3 are plotted in grey along with our sample of 96,134 WDs in red in a color-magnitude diagram (CMD). The x- and y-axes correspond to BP-RP color and absolute G magnitude respectively. The main sequence stars are located within the large diagonal region in the CMD in grey. The WDs appear at $M_G \lesssim$ 7.5 and at BP-RP color $\lesssim$ 1.5. Our sample in red represents the WDs in Gaia after applying our parallax, astrometry, and flux error cuts.
  • Figure 2: The resulting map when passing the XP coefficients for 96,134 WDs through UMAP. The axes are unitless and only represent the 2D projection of the manifold as discussed in Section \ref{['section:methods']}. The map is colored by BP-RP color, where low (and/or negative) values of BP-RP represent bluer and therefore hotter WDs, while high values of BP-RP represent redder and cooler WDs.