Optimizing Supernova Classification with Interpretable Machine Learning Models

Abstract

Photometric classification of Type Ia supernovae (SNe Ia) is critical for cosmological studies but remains difficult due to class imbalance and observational noise. While deep learning models have been explored, they are often resource-intensive and lack interpretability. We present a computationally efficient and interpretable classification framework that maintains high performance on imbalanced datasets. We emphasize the use of PR-AUC and F1-score as more informative metrics than ROC-AUC in severely imbalanced settings. Using an XGBoost ensemble optimized via Bayesian hyperparameter tuning, we classified light curves from the Supernova Photometric Classification Challenge (SPCC), comprising 21,318 events with a 3.19 imbalance ratio (non-Ia to Ia). Our model achieved a PR-AUC of $0.993^{+0.03}_{-0.02}$, an F1-score of $0.923 \pm 0.008$, and a ROC-AUC of $0.976 \pm 0.004$, matching or exceeding deep learning performance on precision-recall trade-offs while using fewer resources. Despite slightly lower overall accuracy, our method balances false positives and false negatives, improving the efficiency of spectroscopic follow-up. We show that optimized ensemble models offer a reproducible and lightweight alternative to complex architectures, particularly for large-scale surveys such as the Legacy Survey of Space and Time (LSST) where transparency and efficiency are essential.