Identifying new high-confidence polluted white dwarf candidates using Gaia XP spectra and Self-Organizing Maps

TL;DR

This paper presents an unsupervised machine learning approach using Self-Organizing Maps to identify new polluted white dwarf candidates with metal-rich atmospheres from Gaia XP spectra, enabling efficient discovery of potential planetary system remnants.

Contribution

The study introduces a novel unsupervised AI methodology to classify white dwarfs and identify metal-polluted candidates in Gaia data, achieving comparable precision to supervised methods.

Findings

Identified 143 new polluted white dwarf candidates.

Detected metallic lines such as Ca, Mg, Na, Li, and K in spectra.

Achieved high classification accuracy with an unsupervised approach.

Abstract

The identification of new white dwarfs (WDs) polluted with heavy elements is important since they provide a valuable tool for inferring chemical properties of putative planetary systems accreting material on their surfaces. The Gaia space mission has provided us with an unprecedented amount of astrometric, photometric, and low resolution (XP) spectroscopic data for millions of newly discovered stellar sources, among them thousands of WDs. In order to find WDs among this data and to identify which ones have metals in their atmospheres, we propose a methodology based on an unsupervised artificial intelligence technique called Self-Organizing Maps (SOM). In our approach a nonlinear high-dimensional dataset is projected on a 2D grid map where similar elements fall into the same neuron. By applying this method, we obtained a clean sample of 66,337 WDs. We performed an automatic spectral classification analysis to them, obtaining 143 bona fide polluted WD candidates not previously classified in the literature. The majority of them are cool WDs and we identify in their XP spectra several metallic lines such as Ca, Mg, Na, Li, and K. The fact that we obtain similar precision metrics than those achieved with recent supervised techniques highlights the power of our unsupervised approach to mine the Gaia archives for hidden treasures to follow-up spectroscopically with higher resolution.