The main sequence-white dwarf valley

We used the t-distributed stochastic neighbour embedding (t-SNE) algorithm to identify variability classes, their subtypes, and spurious variability induced by instrumental effects. Feature importance was evaluated using SHapley Additive exPlanations (SHAP) values to identify the most influential parameters associated with each cluster. The clustering results revealed distinct groups, including hot subdwarfs, cataclysmic variables (CVs), eclipsing binaries, and objects in crowded fields, such as those in the Andromeda (M31) field.

Several potential stellar subtypes also emerged within these clusters, such as pulsating hot subdwarfs exhibiting pure or hybrid (pressure and/or gravity) modes within the hot subdwarf cluster. Magnetic CVs and dwarf novae appeared in the CVs cluster. Feature evaluation further enabled the identification of a cluster dominated purely by photometric variability, as well as clusters associated with instrumental effects and crowded fields.

Notably, objects previously labelled as RR Lyrae were found in an unexpected region of the CMD, potentially due to either unreliable astrometric measurements (e.g. due to binarity) or alternative evolutionary pathways. This study emphasises the robustness of the proposed method in finding variable objects in a large region of the Gaia CMD, including variable hot subdwarfs and CVs, while demonstrating its efficiency in detecting variability in extended stellar populations.

The proposed unsupervised learning framework demonstrates scalability to large datasets and yields promising results in identifying stellar subclasses.