Classification of Transient Astronomical Object Light Curves Using LSTM Neural Networks

• URL: http://arxiv.org/abs/2511.17564v1
• Date: Thu, 13 Nov 2025 20:51:17 GMT
• Title: Classification of Transient Astronomical Object Light Curves Using LSTM Neural Networks
• Authors: Guilherme Grancho D. Fernandes, Marco A. Barroca, Mateus dos Santos, Rafael S. Oliveira,
• Abstract summary: A bidirectional LSTM network with masking layers was trained and evaluated on a test set of 19,920 objects.<n>The model achieved strong performance for S-Like and Periodic classes, with ROC area under the curve (AUC) values of 0.95 and 0.99.<n> Evaluation on partial light curve data revealed substantial performance degradation, with increased misclassification toward the S-Like class.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This study presents a bidirectional Long Short-Term Memory (LSTM) neural network for classifying transient astronomical object light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) dataset. The original fourteen object classes were reorganized into five generalized categories (S-Like, Fast, Long, Periodic, and Non-Periodic) to address class imbalance. After preprocessing with padding, temporal rescaling, and flux normalization, a bidirectional LSTM network with masking layers was trained and evaluated on a test set of 19,920 objects. The model achieved strong performance for S-Like and Periodic classes, with ROC area under the curve (AUC) values of 0.95 and 0.99, and Precision-Recall AUC values of 0.98 and 0.89, respectively. However, performance was significantly lower for Fast and Long classes (ROC AUC of 0.68 for Long class), and the model exhibited difficulty distinguishing between Periodic and Non-Periodic objects. Evaluation on partial light curve data (5, 10,and 20 days from detection) revealed substantial performance degradation, with increased misclassification toward the S-Like class. These findings indicate that class imbalance and limited temporal information are primary limitations, suggesting that class balancing strategies and preprocessing techniques focusing on detection moments could improve performance.

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