We compare feature-based machine learning and end-to-end deep learning for variable star classification using the Zwicky Transient Facility (ZTF) StarEmbed dataset
We compare feature-based machine learning and end-to-end deep learning for variable star classification using the Zwicky Transient Facility (ZTF) StarEmbed dataset, which contains about 40,000 objects across seven imbalanced classes.
Key Findings
- Classical models trained on physically motivated features consistently outperform deep networks trained directly on raw light curves.
- We find that limited labeled data contributes to the performance gap, while the multivariate, irregularly sampled, and morphologically diverse nature of light curves poses additional challenges for deep learning models.
- While deep learning holds promise for learning representations directly from observations, our results indicate that current models fail to generalize with the available training set size.
- These findings motivate further investigation of the benefits of scaling labeled datasets and self-supervised pretraining on large unlabeled surveys.
Conclusion
This work establishes a quantitative baseline for future research and highlights the need for further innovations to unlock the potential of deep learning in astronomical time series analysis.
These findings motivate further investigation of the benefits of scaling labeled datasets and self-supervised pretraining on large unlabeled surveys.
# Dataset
Source: Zwicky Transient Facility (ZTF) StarEmbed
Size: ~40,000 objects
Classes: 7 (imbalanced)
# Reproduce
See repository README for training/evaluation commands.