用连续时间神经网络模型，高效预测超新星光变曲线，支持快速天文观测优先级筛选。
SELDON: Supernova Explosions Learned by Deep ODE Networks
- 基于掩码GRU-ODE与神经ODE的联合架构，处理稀疏不规则观测数据。
- 可毫秒级推断数千对象，相比传统方法提速数百倍。
- 输出参数具物理意义，适合需快速决策的天文巡天项目使用。
当薇拉·C·鲁宾天文台的时空遗产巡天上线后，光学暂现源的发现率将飙升至每晚一百万条公开警报，远超传统物理推断管道的处理能力。连续时间预测人工智能模型因其可在毫秒级别完成每日数千对象的推断，而具有重要价值，相较之下，传统MCMC代码每个对象需耗时数小时。本文提出SELDON，一种针对非平稳、异方差且内在相关的稀疏不规则采样（有缺失）天体光变曲线面板的新型连续时间变分自编码器。SELDON结合掩码GRU-ODE编码器、潜在空间神经ODE传播器及可解释的高斯基解码器。编码器能从仅少数观测点中学习总结不平衡且相关的数据；神经ODE则在连续时间中推进隐藏状态，外推至未来未观测时刻。该外推序列进一步通过深度集合编码为潜在分布，并解码为高斯基函数的加权和，其参数具有明确物理意义（如上升时间、衰减速率、峰值通量），可直接用于后续光谱跟进的优先级排序。除天文学外，SELDON架构为多变量、稀疏、异方差、非均匀采样的任意时间域序列建模提供了通用可解释方案。
原文摘要 · Abstract (English)
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.
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