The classification of real and bogus transients using active learning and semi-supervised learning

TL;DR

This paper introduces a novel deep-learning approach combining active and semi-supervised learning to classify real and bogus transients in astrophysics, reducing the need for extensive labeled data while maintaining high accuracy.

Contribution

It presents a new method that effectively leverages limited labeled data and unlabeled data for transient classification, improving efficiency and performance over traditional approaches.

Findings

Achieved high classification accuracy with only 1000 labeled samples.

Reduced annotation costs through active learning strategy.

Enhanced model performance by semi-supervised learning with unlabeled data.

Abstract

Deep-learning-based methods have been favored in astrophysics owing to their adaptability and remarkable performance and have been applied to the task of the classification of real and bogus transients. Different from most existing approaches which necessitate massive yet expensive annotated data, We aim to leverage training samples with only 1000 labels available to discover real sources that vary in brightness over time in the early stage of the WFST 6-year survey. Methods. We present a novel deep-learning method that combines active learning and semi-supervised learning to construct a competitive real/bogus classifier. Our method incorporates an active learning stage, where we actively select the most informative or uncertain samples for annotation. This stage aims to achieve higher model performance by leveraging fewer labeled samples, thus reducing annotation costs and improving the overall learning process efficiency. Furthermore, our approach involves a semi-supervised learning stage that exploits the unlabeled data to enhance the model's performance and achieve superior results compared to using only the limited labeled data.