Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction

Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to characteristically sparse and unevenly sampled multi-wavelength time series (light curves), severely limiting the performance on downstream tasks. Interpolating light curves help in mitigating this problem, where Gaussian Processes (GPs) are the current standard. However, GPs struggle with cross-wavelength correlations, require *a priori* kernel specification, and cannot handle ensembles of diverse transient and variable classes. Furthermore, GPs and other existing models (*e.g.*, Bayesian neural networks), scale poorly and must be fit to each light curve individually, transferring no information between light curves. We introduce Attentive Neural Processes (ANPs) for light curve reconstruction, combining the probabilistic framework of GPs with the scalability of deep learning, and we demonstrate their use as a fast, data-driven, universal light curve interpolator. Unlike prior processes tailored to single object classes, our approach is morphologically agnostic. By meta-learning on a diverse population of simulated transients, ANPs shift the computation cost to the training phase, enabling amortized inference - particularly important at the eve of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), which will produce massive datasets for transient astrophysics. We evaluate our models on realistic LSST cadences for its 6-filters across 14 transient classes. The ANP outperforms all our benchmark models, including a suite of GP and neural-network based models, on all regression quality, feature recovery, and rigorous probabilistic calibration metrics. Our ANP performs probabilistic interpolation on all filters in $\sim 10^{-6} $ seconds -- over four orders of magnitude faster than the next-best neural benchmark and over five orders of magnitude faster than GPs -- making them suitable for on-line use on the LSST alert stream estimated to carry $\sim10^{7}$ events/night. The ANP avoids neural networks' overconfidence and GPs' underconfidence, delivering sharp, highly calibrated uncertainties. This work establishes the neural process family as a scalable, probabilistic foundation for real-time transient science in the LSST era.