The discovery rate of optical transients will explode to 10 million public alerts per night once the Vera C. Rubin Observatory's Legacy Survey of Space and Time comes online, overwhelming the traditional physics-based inference pipelines. A continuous-time forecasting AI model is of interest because it can deliver millisecond-scale inference for thousands of objects per day, whereas legacy MCMC codes need hours per object. In this paper, we propose SELDON, a new continuous-time variational autoencoder for panels of sparse and irregularly time-sampled (gappy) astrophysical light curves that are nonstationary, heteroscedastic, and inherently dependent. SELDON combines a masked GRU-ODE encoder with a latent neural ODE propagator and an interpretable Gaussian-basis decoder. The encoder learns to summarize panels of imbalanced and correlated data even when only a handful of points are observed. The neural ODE then integrates this hidden state forward in continuous time, extrapolating to future unseen epochs. This extrapolated time series is further encoded by deep sets to a latent distribution that is decoded to a weighted sum of Gaussian basis functions, the parameters of which are physically meaningful. Such parameters (e.g., rise time, decay rate, peak flux) directly drive downstream prioritization of spectroscopic follow-up for astrophysical surveys. Beyond astronomy, the architecture of SELDON offers a generic recipe for interpretable and continuous-time sequence modeling in any time domain where data are multivariate, sparse, heteroscedastic, and irregularly spaced.

翻译：随着薇拉·C·鲁宾天文台时空遗产巡天项目的上线，光学瞬变源的发现率将激增至每晚千万级公共警报量，这将使传统基于物理的推断流程不堪重负。连续时间预测人工智能模型因其能实现毫秒级推理速度（每日可处理数千个天体），而传统MCMC代码对单个天体即需数小时计算，因而备受关注。本文提出SELDON——一种面向稀疏且非均匀时间采样（存在间隔）的非平稳、异方差、具内在依赖性的天体物理光变曲线面板数据的连续时间变分自编码器。SELDON融合了掩码GRU-ODE编码器、潜在神经ODE传播器与可解释的高斯基解码器。该编码器能够从仅含少量观测点的非平衡相关数据面板中学习特征表示。神经ODE随后在连续时间维度上对该隐藏状态进行积分推演，实现对未来未观测时段的预测。通过深度集合网络将此外推时间序列进一步编码为潜在分布，最终解码为具有物理意义的高斯基函数加权和。所得参数（如上升时间、衰减率、峰值流量）可直接驱动天体物理巡天中光谱后续观测的优先级判定。在天文学之外，SELDON的架构为多元、稀疏、异方差且非均匀采样的任意时间域序列建模提供了可解释的连续时间通用解决方案。