Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 46 references that are in the map. First posted on arXiv 24 Aug 2025.
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20 of 46 references.
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8 more
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Citing papers per year: 2026: 2.
2 papers.