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October 20, 2025Open Access

From stellar light to astrophysical insight: automating variable star research with machine learning

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Authors

JAJeroen Audenaert

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Overview

This review demonstrates how machine learning enhances data-driven methods in asteroseismology and stellar variability studies.

Key Points

  • Machine learning significantly improves the classification and inference of stellar variability data.
  • Large-scale photometric surveys, like those from Kepler and TESS, provide invaluable data for this research.
  • Recent advancements in representation learning contribute to automating data cleaning and variability classification.
  • Automated methods can unlock new discoveries in time-domain astronomy and offer a path for future exploration.

Cite This Study

Jeroen Audenaert (2025) studied this question.

synapsesocial.com/papers/68f5fcdc8d54a28a75cf2434https://doi.org/10.48550/arxiv.2507.03093
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