arXiv subjects

Alberto Rebassa Mansergas

Publications and source records attributed to Alberto Rebassa Mansergas.

3 recordsLinked to original sources

arXiv2026

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

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.

astro-ph.SR↗

arXiv2025

ESO Expanding Horizons White Paper: Electromagnetic characterisation of millihertz gravitational-wave sources in the Milky Way

The millihertz band is densely populated by continuous gravitational-wave signals from Galactic compact binaries, dominated by double white dwarfs (DWDs; binaries of two white dwarfs) with contributions from systems containing neutron stars and black holes (Amaro-Seoane et al. 2023). As these binaries inspiral due to gravitational-wave radiation, they can reach contact and begin mass transfer in the millihertz band. Gravitational-wave detectors like LISA will survey such compact binaries across the Milky Way, yielding samples numbering in the tens of thousands, with essentially complete sensitivity to orbital periods shorter than ~10-20 min (e.g. Lamberts et al. 2019). Assessing the nature of the binary components - and deriving masses, temperatures and compositions - requires systematic electromagnetic characterisation that breaks gravitational-wave degeneracies and enables full atmospheric and orbital solutions. At present, no dedicated facility or coordinated survey is planned to deliver electromagnetic follow-up at the scale necessary to maximise the science return of the millihertz gravitational-wave data; this white paper discusses the need and requirements of such a capability.

astro-ph.IM↗

arXiv2025

A Random Forest spectral classification of the Gaia 500-pc white dwarf population

The third Gaia Data Release has provided the astronomical community with astrometric data of more than 1.8 billion sources, and low resolution spectra for 220 million. Such a large amount of data is difficult to handle by means of visual inspection. In this work, we present a spectral analysis of the Gaia white dwarf population up to 500 pc from the Sun based on artificial intelligence algorithms to classify the sample into their main spectral types and subtypes. In order to classify the sample, which consists of 78 920 white dwarfs with available Gaia spectra, we have applied a Random Forest algorithm to the Gaia spectral coefficients. We used the Montreal White Dwarf Database of already labeled objects as our training sample. The classified sample is compared with other already published catalogs and with our own higher resolution Gran Telescopio Canarias (GTC) spectra, enabling the construction of a golden sample of well-classified objects. The Random Forest spectral classification of the 500-pc white dwarf population achieves an excellent global accuracy of 0.91 and an F1-score of 0.88 for the DA versus non-DA classification. In addition, we obtain a very high accuracy of 0.76 and a global F1-score of 0.62 for the non-DA subtype classification. In particular, our classification shows an excellent recall for DAs, DBs and DCs and a very good precision for DQs, DZs and DOs. The use of machine learning techniques, particularly the Random Forest algorithm, has enabled us to spectrally classify 78,920 white dwarfs with reasonable accuracy. Having an estimate of the spectral type for the vast majority of white dwarfs up to 500 pc provides the possibility of making better estimates of cooling ages, star formation rates, and stellar evolution processes, among other fundamental aspects for the study of the white dwarf population.

astro-ph.SR↗