The SALT survey of helium-rich hot subdwarfs: unsupervised classification and kinematic analysis
Asish Philip Monai, C. Simon Jeffery

TL;DR
This study uses unsupervised machine learning techniques on spectroscopic data from SALT to classify helium-rich hot subdwarfs, revealing new spectral groupings and their kinematic properties, thus advancing understanding of their evolution.
Contribution
Introduces a PCA and spectral clustering-based classification system for helium-rich hot subdwarfs, uncovering new spectral groups and refining existing classification schemes.
Findings
Identified 6 major spectral clusters corresponding to traditional classes.
Revised classification for very hot sdOs based on PCA results.
Found higher halo star fractions in He-sdOs and Hot-sdOs.
Abstract
Hydrogen-deficient stars form sequences across the HR diagram from cool R CrB stars to helium-rich hot subdwarfs and PG1159 stars, tracing multiple evolution pathways. The Southern African Large Telescope (SALT) has been used to conduct a medium-resolution spectroscopic survey of 697 helium-rich hot subdwarfs and related stars. Using 587 stars from the full SALT sample, we define an unsupervised data-led classification system based on Principal Components Analysis (PCA) and Spectral Clustering (SC), designed as a data-discovery tool to identify possible new groups and sequences within the data. Using the first 3 principal components (PCs), we identify 6 major clusters of hot subdwarf spectra, identified with the traditional spectral classes of classical sdB and sdO stars, helium-rich He-sdO stars and very hot subdwarfs. Two clusters covering large volumes of PC space include a) He-sdB…
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Taxonomy
TopicsStellar, planetary, and galactic studies · Astronomy and Astrophysical Research · Scientific Research and Discoveries
