Detection of hot subdwarf binaries and He-poor hot subdwarf stars using machine learning methods and a large sample of Gaia XP spectra

¹ Institute of Theoretical Physics and Astronomy, Vilnius University, Sauletekio av. 3, 10257 Vilnius, Lithuania
² Centro de Astrobiología (CSIC-INTA), Camino Bajo del Castillo s/n, 28692 Villanueva de la Cañada, Madrid, Spain
³ Applied Physics Department, Universidade de Vigo, Campus Lagoas-Marcosende, s/n, 36310 Vigo, Spain
⁴ Centro de Investigación Mariña, Universidade de Vigo, CIM/GEOMA, 36310 Vigo, Spain
⁵ Universidade da Coruña (UDC), Department of Computer Science and Information Technologies, Campus de Elviña s/n, 15071 A Coruña, Galiza, Spain
⁶ IES de Beade, Conseller iá de Educación e Ordenación Universitaria, Camino do Outeiro 10, 36312 Vigo, Spain
⁷ Institute of Applied Mathematics, Vilnius University, 24 Naugarduko st., 03225 Vilnius, Lithuania
⁸ CIGUS CITIC – Department of Computer Science and Information Technologies, University of A Coruña, s/n, 15071 A Coruña, Spain
⁹ CIGUS CITIC – Department of Nautical Sciences and Marine Engineering, University of A Coruña, Paseo de Ronda 51, 15011 A Coruña, Spain
¹⁰ INAF – Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125 Firenze, Italy

^★ Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 27 November 2025
Accepted: 25 January 2026

Abstract

Context. Hot subdwarfs (hot sds) are compact evolved stars near the extreme horizontal branch, and they are key to understanding stellar evolution and the UV excess in galaxies. In this work, we extend our previous analysis of Gaia XP spectra of hot sd stars to a much larger sample, enabling a comprehensive study of their physical and binary properties.

Aims. Our goal is to identify patterns in Gaia XP spectra, investigate binarity, and assess the influence of parameters such as temperature, helium abundance, and variability. We analysed ~20 000 hot sd candidates selected from the literature, combining Gaia XP data with published parameters.

Methods. We applied the uniform manifold approximation and projection technique to the XP coefficients, which represent the Gaia XP spectra in a compact feature-based form, to construct a similarity map. We then used self-organising maps and convolutional neural networks (CNNs) to classify spectra as binaries or singles and as cool/He-poor or hot/He-rich. The spectra were normalised using asymmetric least squares baseline fitting to emphasise individual spectral features.

Results. We found that the BP-RP colour dominates the similarity map, with additional influence from temperature, helium abundance, and variability. Most binaries, which were identified via the Virtual Observatory SED analyser, cluster in two filaments linked to main sequence companions. The CNN classification suggests a strong correlation between variability and binarity, with binary fractions exceeding 60% for active hot sds.

Conclusions. The Gaia XP spectra combined with dimensionality reduction and machine learning effectively revealed patterns in hot sd properties. Our findings indicate that binarity and environmental density strongly shape the evolutionary paths of hot subdwarfs. We identified possible contamination by main sequence and cataclysmic variable stars in our base sample.