Modeling Irregular Astronomical Time Series with Neural Stochastic Delay Differential Equations

TL;DR

This paper presents Neural SDDEs, a novel neural stochastic delay differential equation framework, for modeling irregular astronomical time series, improving classification and anomaly detection in sparse, noisy data.

Contribution

It introduces Neural SDDEs combining stochastic modeling and neural networks to effectively analyze irregular, incomplete astronomical time series.

Findings

Achieves high classification accuracy on irregular astronomical data.

Effectively detects novel astrophysical events with partial labels.

Handles noisy, sparse sequences robustly.

Abstract

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.