READit: Rapid and Efficient Attention-based Distillation for Irregularly Sampled Light Curves

Next-generation astronomical surveys like the Vera C. Rubin Observatory (LSST) will accumulate petabyte-scale data over the next decade, producing tens of terabytes each night, yet extracting representations via self-supervised learning currently demands prohibitive pre-training compute. To address this bottleneck, we introduce *READit*, an attention-based foundation model optimized via student–teacher self-distillation for asynchronous, multi-band light curves. Pre-trained on $\sim 2.1$ million Zwicky Transient Facility (ZTF) light curves, *READit* reaches peak representational convergence in just 11 epochs ($\approx 15$ GPU-hours)—a $\sim 95\%$ compute reduction ($18\times$ speedup) over prior multi-band contrastive frameworks. This rapid optimization is achieved by coupling multi-crop self-distillation with physics-informed temporal augmentations that stabilize the learning trajectory. On downstream benchmarks, *READit* consistently outperforms existing astronomical foundation models, general time-series architectures, and classical statistical baselines on 12-class variability classification. Moreover, *READit* exhibits remarkable label efficiency in extreme low-data regimes, clusters unseen out-of-distribution classes, and demonstrates robust zero-shot and few-shot cross-survey transfer to simulated LSST observations (PLAsTiCC). Consequently, *READit* establishes a computationally sustainable, "Green AI" foundation model for survey-scale time-domain discovery.