Variability in hot sub-luminous stars and binaries: Machine-learning analysis of Gaia DR3 multi-epoch photometry

¹ Department of Astrophysics/IMAPP, Radboud University, PO Box 9010 6500 GL Nijmegen, The Netherlands
² Instituut voor Sterrenkunde, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium
³ Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Straße 1, 85741 Garching bei München, Germany
⁴ Department of Astronomy, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa
⁵ South African Astronomical Observatory, PO Box 9 Observatory 7935, South Africa
⁶ The Inter-University Institute for Data Intensive Astronomy, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa
⁷ Hamburger Sternwarte, University of Hamburg, Gojenbergsweg 112, 21029 Hamburg, Germany
⁸ Texas Tech University, Department of Physics & Astronomy, Box 41051 79409 Lubbock, TX, USA
⁹ Max Planck Institute for Astronomy, Königstuhl 17, 69117 Heidelberg, Germany

^⋆ Corresponding author; princy.ranaivomanana@ru.nl

Received: 30 September 2024
Accepted: 3 December 2024

Abstract

Context. Hot sub-luminous stars represent a population of stripped and evolved red giants that is located on the extreme horizontal branch. Since they exhibit a wide range of variability due to pulsations or binary interactions, it is crucial to unveil their intrinsic and extrinsic variability to understand the physical processes of their formation. In the Hertzsprung-Russell diagram, they overlap with interacting binaries such as cataclysmic variables (CVs).

Aims. By leveraging the most recent clustering algorithm tools, we investigate the variability of 1576 candidate hot subdwarf variables using comprehensive data from Gaia DR3 multi-epoch photometry and Transiting Exoplanet Survey Satellite (TESS) observations.

Methods. We present a novel approach that uses the t-distributed stochastic neighbour embedding and the uniform manifold approximation and projection dimensionality reduction algorithms to facilitate the identification and classification of different populations of variable hot subdwarfs and CVs in a large dataset. In addition to the publicly available Gaia time-series statistics table, we adopted additional statistical features that enhanced the performance of the algorithms.

Results. The clustering results led to the identification of 85 new hot subdwarf variables based on Gaia and TESS light curves and of 108 new variables based on Gaia light curves alone, including reflection-effect systems, HW Vir, ellipsoidal variables, and high-amplitude pulsating variables. A significant number of known CVs (140) distinctively cluster in the 2D feature space among an additional 152 objects that we consider candidates for new CVs.

Conclusions. This study paves the way for more efficient and comprehensive analyses of stellar variability from ground- and space-based observations, and for the application of machine-learning classifications of candidate variable stars in large surveys.