Hot DQs, magnetic and metal-polluted white dwarfs: spectroscopic insights from a Gaia machine-learning-selected 500 pc sample

TL;DR

This study validates machine-learning spectral classifications of Gaia white dwarf data through follow-up spectroscopy, revealing insights into magnetic, metal-polluted, and carbon-rich white dwarfs, including rare subtypes and their origins.

Contribution

It demonstrates the high accuracy of Random Forest classifications on Gaia spectra and characterizes various white dwarf subtypes, including magnetic and carbon-rich objects, with spectroscopic confirmation.

Findings

Machine-learning classifications are >90% accurate for spectral types.

Most 'massive DBs' are magnetic white dwarfs and warm DQs.

Identified rare white dwarf subtypes and confirmed warm DQs as merger remnants.

Abstract

The latest Gaia data release provides low-resolution spectra for approximately 100 000 white dwarfs. Though useful for pre-classification, they lack the resolution required for accurate spectral type and parameter determination, motivating spectroscopic follow-up campaigns. In this work, we assess the reliability of machine-learning spectral classifications derived from Gaia spectra through comparison with medium-resolution spectroscopy, determine the nature of objects classified as "massive helium-rich (DB)" by automated methods, and characterise the properties of warm and hot DQ (carbon-dominated) white dwarfs, magnetic and metal-polluted objects. To do this, we observed 255 white dwarfs with the Gran Telescopio Canarias equipped with the OSIRIS instrument (R ~ 1000). Spectral types were assigned through visual inspection and compared with machine-learning classifications applied to Gaia spectra. Magnetic objects were identified via Zeeman splitting, and magnetic field strengths were estimated. We find machine-learning classifications are highly accurate (> 90% for spectral types in their training sets), despite the low resolution of Gaia spectra. We show "massive DBs" to be mostly magnetic white dwarfs and warm DQs, with only 5 of 112 observed (4.46%) confirmed as genuine DBs. Warm DQs are found along the Gaia Q branch and exhibit unusually high tangential speeds. We provide spectral classifications for 255 white dwarfs, demonstrate that Random Forest algorithms reliably classify low-resolution Gaia spectra into main spectral types, determine the nature of "massive DBs", and identify a large population of magnetic white dwarfs and carbon-rich objects. Several rare subtypes are identified, including 1 DAQ, 1 DQZA, 4 hot, 29 warm DQ stars, and 63 magnetic white dwarfs. The properties of warm DQs are consistent with previous studies, supporting their proposed origin as merger remnants.