From stellar light to astrophysical insight: automating variable star research with machine learning
Name
10509_2025_Article_4460.pdf
Size
1.88 MB
Format
Adobe PDF
Checksum (MD5)
551b57fd5ddd29442e6dbc22d7a37b2c
Author(s)
Audenaert, Jeroen
Date Issued
July 24, 2025
Journal
Astrophysics and Space Science
Publisher
Springer Netherlands
Citation
Audenaert, J. From stellar light to astrophysical insight: automating variable star research with machine learning. Astrophys Space Sci 370, 72 (2025).
Version
Final published version
Abstract
Large-scale photometric surveys are revolutionizing astronomy by delivering unprecedented amounts of data. The rich data sets from missions such as the NASA Kepler and TESS satellites, and the upcoming ESA PLATO mission, are a treasure trove for stellar variability, asteroseismology and exoplanet studies. In order to unlock the full scientific potential of these massive data sets, automated data-driven methods are needed. In this review, I illustrate how machine learning is bringing asteroseismology toward an era of automated scientific discovery, covering the full cycle from data cleaning to variability classification and parameter inference, while highlighting the recent advances in representation learning, multimodal datasets and foundation models. This invited review offers a guide to the challenges and opportunities machine learning brings for stellar variability research and how it could help unlock new frontiers in time-domain astronomy.
MIT Department
MIT Kavli Institute for Astrophysics and Space Research
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10509-025-04460-5
