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

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. 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. 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. 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. 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.