PIE-ADA: Physics-Informed Ensemble with Adaptive Data Augmentation for Photometric Transient Classification

Abstract

The upcoming Large Synoptic Survey Telescope (LSST) is expected to observe approximately 10 million astronomical transient events per night, creating an urgent need for automated classification systems. A key challenge is the extreme class imbalance in transient datasets, where rare event types represent less than 1% of all observations. This paper presents PIE-ADA (Physics-Informed Ensemble with Adaptive Data Augmentation), a framework that generates physically realistic synthetic light curves for underrepresented classes using astrophysically motivated transformations. PIE-ADA applies four augmentation operations, namely correlated noise injection, cosmological time dilation, wavelength-dependent dust extinction, and observation phase shifting, while enforcing physical constraints to prevent unrealistic samples. We extract 271 multi-scale features from six photometric passbands covering statistical, temporal, peak, color, and frequency-domain properties. Evaluated on the PLAsTiCC dataset (7,848 original objects augmented to 8,148 across 14 classes), five classifiers were compared using stratified 5-fold cross-validation. LightGBM achieved the best performance with a weighted log loss of 0.5763 ($\pm$0.0083) and 80.33% accuracy, improving over Random Forest, Extra Trees, and Neural Network baselines by 24-49% in log loss. The framework is computationally efficient, completing the full pipeline in under 37 minutes and classifying individual objects in less than 0.05 seconds, making it suitable for real-time LSST alert processing.