SeaStar-TS: Self-Supervised Light-Curve Representations with Semantic Adaptation

Astronomical surveys collect irregular time series of brightness measurements, called light curves, for far more variable stars and transient events than can receive detailed follow-up. Hand-crafted features remain strong baselines for classifying these light curves: they summarize periodicity, amplitude, and shape even when observations are irregular and noisy. We present SeaStar-TS, which learns representations directly from photometry by predicting masked observations while accounting for measurement uncertainty. Across ELAsTiCC, MACHO, StarEmbed, and LEAVES, we evaluate the approach under different label budgets and observing conditions. It surpasses hand-crafted-feature baselines on ELAsTiCC and LEAVES. On StarEmbed, its linear and MLP readouts exceed the corresponding feature-based readouts, and on MACHO it has the highest listed scores at two label budgets. As an additional use of the learned representation, alignment with class names and descriptions enables top-k recognition of classes omitted from supervised training. These results show that raw-photometry representations can compete with engineered features and place held-out classes among the top-ranked labels.