Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks

Abstract

Due to the latest advances in technology, telescopes with significant sky coverage will produce millions of astronomical alerts per night that must be classified both rapidly and automatically. Currently, classification consists of supervised machine learning algorithms whose performance is limited by the number of existing annotations of astronomical objects and their highly imbalanced class distributions. In this work, we propose a data augmentation methodology based on Generative Adversarial Networks (GANs) to generate a variety of synthetic light curves from variable stars. Our novel contributions, consisting of a resampling technique and an evaluation metric, can assess the quality of generative models in unbalanced datasets and identify GAN-overfitting cases that the Fréchet Inception Distance does not reveal. We applied our proposed model to two datasets taken from the Catalina and Zwicky Transient Facility surveys. The classification accuracy of variable stars is improved significantly when training with synthetic data and testing with real data with respect to the case of using only real data.

Accepted to ApJ on May 11, 2022

References in corpus (6)

• The Zwicky Transient Facility: System Overview, Performance, and First Results
• An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks
• The Catalina Surveys Periodic Variable Star Catalog
• The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker
• Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
• Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier