A Comparative Study of Machine-Learning Methods for Early Classification from Sparse Astronomical Light Curves

Abstract

The booming data volume of modern time-domain surveys demands fast, robust early classification of sparsely sampled light curves, as newly discovered transients typically have only a handful of observations. We compare classifiers for extremely sparse light curves (3–30 observations) on a benchmark of approximately 1.72 million segments spanning seven astrophysical classes from ZTF and ATLAS. Methods include handcrafted-feature approaches (XGBoost, feature-based Transformers) and end-to-end LSTM and Transformer models. A pre-trained end-to-end Transformer achieves test accuracy of 0.946 (macro F1 0.950), exceeding 90% accuracy with only seven observations, but falls to 0.513 without pre-training. XGBoost-Reduced (38 features, excluding LS descriptors) reaches 0.922, while XGBoost-Full (56 features) reaches 0.913. Reliability diagnostics confirm LS periods and false-alarm probabilities are unreliable on 3–30-point segments; restricting training and evaluation to ≥15 points does not reverse the full-scale preference for the Reduced catalog. On CPU, XGBoost runtime is dominated by feature extraction (ratio ≈ 16:1); adding LS descriptors increases total processing time by ∼7.8% (feature extraction by ∼7.0%) without a commensurate accuracy gain. A lightweight LSTM attains 0.847 accuracy with 0.2 M parameters. These results offer practical guidance for model selection in real-time survey pipelines.