Machine Learning for Real Time Classification of Transient Events: a Recurrent Neural Network Auto-Encoder and Gradient Boosting Classifier

Abstract

The rapid advancement of astronomical survey technologies, such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), is expected to generate millions of transient events annually, posing significant challenges in processing large volumes of unlabeled data. To address this, a deep learning model was developed, combining a Recurrent Neural Network Variational Autoencoder (RNN-VAE) for dimensionality reduction with a Gradient Boosting Classifier for real-time classification of transient events. This model efficiently classifies galactic and extragalactic transients without the need for labeled data. Using the PLAsTiCC dataset, the model achieved an AUC-ROC score of 0.94 and F-1 score of 0.89, demonstrating strong performance in distinguishing between various transient classes, including rare events. This approach offers a scalable solution for real-time astronomical surveys, enhancing both classification accuracy and resource allocation in future data-rich environments.