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
Terminology
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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