Neural Langevin-type Stochastic Differential Equations for Astronomical time series Classification under Irregular Observations

Addressing the classification challenges of irregular time series data in astronomical studies like Large Synoptic Survey Telescope (LSST), this study leverages Neural Stochastic Differential Equations (Neural SDEs) to tackle data irregularity and incompleteness. We analyze a comprehensive analysis to the Neural Langevin-type SDEs' optimal initial condition, which is pivotal role in modelling continuous latent state. Three different strategies for selecting initial condition are compared under regular and irregular scenario using LSST dataset. Our empirical evaluation using Langevin-type SDEs highlights the superiority of static approach over dynamic approaches for initial condition. This discovery highlights the effectiveness of well-chosen initial values of Neural SDEs to enhance the performance of astronomical time series classification under irregular observations.