arXiv subjects

Santiago Torres Gil

Publications and source records attributed to Santiago Torres Gil.

3 recordsLinked to original sources

arXiv2026

Influence of mass-transfer stability on the formation of post-common-envelope binaries

Post-common-envelope binaries are the natural laboratories for constraining the physics of common envelope evolution, which is one of the most uncertain phases in binary stellar evolution. Traditional binary population synthesis models, adopting mass transfer stability criteria based on polytropic stellar models, systematically overpredict the number of post-common-envelope binaries with solar-type main-sequence companions. In this work, we present an updated binary population synthesis model using the rapid binary evolution code \textit{Binary Star Evolution}, incorporating a physically motivated mass transfer stability criterion and a self-consistent envelope binding energy prescription. We compile a comprehensive sample of classic white dwarf + main sequence post-common-envelope binaries with well-measured parameters, hosting both M-dwarf and A/F/G/K- stars. We find that the enhanced mass transfer stability is an additional mechanism responsible for the observed dearth of post-common-envelope binaries with solar-type main sequence companions; neither magnetic braking nor selection effects alone can fully account for this deficit, and a combination of all three processes is most likely required. Models with inefficient common envelope evolution ($α_{\rm CE}=0.25$) provide the best overall match to the observed population. These results highlight the critical role of MT stability in shaping the observed post-common-envelope binaries population and provide new constraints on common envelope evolution.

astro-ph.SR↗

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

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↗