Exploring Late Stellar Evolution in the Era of Large Surveys: Machine Learning Prospects for Hot Subdwarfs and White Dwarfs
astro-ph.SR
Submitted: 2026-08-26
Updated: 2026-08-26
Comments: Accepted for publication in MDPI Universe; 15 pages, 3 figures, 3 tables
License: http://creativecommons.org/licenses/by/4.0/
The gist: The rapid growth of large-scale astronomical surveys and advances in data-driven analysis techniques have transformed the study of late-stage stellar evolution.
Terminology
Abstract
The rapid growth of large-scale astronomical surveys and advances in data-driven analysis techniques have transformed the study of late-stage stellar evolution. Modern facilities are producing large volumes of photometric, spectroscopic, and astrometric data, enabling systematic investigations of compact stellar populations across the Milky Way. Among the most important tracers of these advanced evolutionary phases are hot subdwarfs and white dwarfs: hot subdwarfs are core-helium-burning tracers of late, binary-driven stellar evolution, while white dwarfs represent the final evolutionary endpoint of low- and intermediate-mass stars. These compact objects provide important laboratories for studying stellar interiors, binary evolution, and the long-term fate of planetary systems. This paper explores how recent advances in machine learning are being applied to the detection, characterization, and, when combined with follow-up spectroscopy and modeling, the physical interpretation of hot subdwarfs and white dwarfs. By combining photometric, spectroscopic, and time-domain observations with these computational tools, it is now possible to efficiently discover rare objects, detect stellar variability, and probe the internal structure and evolutionary pathways of compact stars. Ultimately, these developments highlight the growing role of advanced algorithms in supporting the study of the final stages of stellar evolution, provided their outputs are validated against physical observables.
Sources
- SDSS-V: Pioneering Panoptic Spectroscopy
- Hot Subdwarf Stars
- The LSST Data Management System
- Gaia Data Release 3. Summary of the variability processing and analysis
- Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- Generative Adversarial Networks
- Attention Is All You Need
- Auto-Encoding Variational Bayes
- Identifying highly magnetized white dwarfs: A dimensionality reduction framework for estimating magnetic fields
- Disentangling the Galactic binary zoo: Machine learning classification of stellar remnant binaries in LISA data
- Leveraging Multimodality for Real-Time Classification of Transients and Variables found by the Zwicky Transient Facility
- Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid
- Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
- A hands-on introduction to Physics-Informed Neural Networks for solving partial differential equations with benchmark tests taken from astrophysics and plasma physics
- Stellar archaeology with Gaia: the Galactic white dwarf population
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