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September 10, 2025Astronomy and Astrophysics

Learning novel representations of variable sources from multi-modal data via autoencoders

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Authors

PHP. HuijseJRJ. De RidderLEL. Eyer

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Overview

Machine learning uncovers variability in millions of stellar and quasar sources, highlighting synergies in data products.

Key Points

  • The learned representations enhance classification tasks, effectively identifying variability classes among sources.
  • Training included 4 million sources using three variational autoencoders, significantly improving data compression and analysis.
  • Unsupervised classification showcased the value of combining light curves and spectral data for discovery.
  • Strong correlations were observed between latent variables and astrophysical properties, indicating potential for further analysis.

Cite This Study

Huijse et al. (2025) studied this question.

synapsesocial.com/papers/68c19f9c54b1d3bfb60db3f1https://doi.org/10.1051/0004-6361/202554025
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