Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
This addresses challenges in astronomical time series analysis for surveys like LSST, though it appears incremental as it combines existing concepts (stochastic modeling, neural networks, delay differential equations).
The authors tackled the problem of analyzing irregularly sampled and incomplete astronomical time series by introducing Neural Stochastic Delay Differential Equations (Neural SDDEs), which achieved strong classification accuracy and effective detection of novel astrophysical events on astronomical data.
Astronomical time series from large-scale surveys like LSST are often irregularly sampled and incomplete, posing challenges for classification and anomaly detection. We introduce a new framework based on Neural Stochastic Delay Differential Equations (Neural SDDEs) that combines stochastic modeling with neural networks to capture delayed temporal dynamics and handle irregular observations. Our approach integrates a delay-aware neural architecture, a numerical solver for SDDEs, and mechanisms to robustly learn from noisy, sparse sequences. Experiments on irregularly sampled astronomical data demonstrate strong classification accuracy and effective detection of novel astrophysical events, even with partial labels. This work highlights Neural SDDEs as a principled and practical tool for time series analysis under observational constraints.