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

TL;DR

This paper introduces a GAN-based data augmentation method to generate synthetic astronomical light curves, significantly improving the classification accuracy of variable stars in imbalanced datasets.

Contribution

It presents a novel GAN-based augmentation technique with a resampling method and evaluation metric tailored for unbalanced astronomical datasets.

Findings

Synthetic data improves classification accuracy.

Proposed metrics detect GAN overfitting.

Method tested on Catalina and Zwicky datasets.

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 Fr\'echet 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.