Uncertainty estimation for time series classification: Exploring predictive uncertainty in transformer-based models for variable stars
Published in submitted A&A, 2015
Classifying variable stars is key for understanding stellar evolution and galactic dynamics. With the demands of large astronomical surveys, machine learning models, especially attention-based neural networks, have become the state-of-the-art. While achieving high accuracy is crucial, enhancing model interpretability and uncertainty estimation is equally important to ensure that insights are both reliable and comprehensible. We aim to enhance transformer-based models for classifying astronomical light curves by incorporating uncertainty estimation techniques to detect misclassified instances. We tested our methods on labeled datasets from MACHO, OGLE-III, and ATLAS, introducing a framework that significantly improves the reliability of automated classification for the next-generation surveys. We used Astromer, a transformer-based encoder designed for capturing representations of single-band light curves.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).
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