SELDON: Supernova Explosions Learned by Deep ODE Networks

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.

Bootstrapped Control Limits for Score-Based Concept Drift Control Charts

Monitoring for changes in a predictive relationship represented by a fitted supervised learning model (aka concept drift detection) is a widespread problem, e.g., for retrospective analysis to determine whether the predictive relationship was stable over the training data, for prospective analysis to determine when it is time to update the predictive model, for quality control of processes whose behavior can be characterized by a predictive relationship, etc. A general and powerful Fisher score-based concept drift approach has recently been proposed, in which concept drift detection reduces to detecting changes in the mean of the model's score vector using a multivariate exponentially weighted moving average (MEWMA). To implement the approach, the initial data must be split into two subsets. The first subset serves as the training sample to which the model is fit, and the second subset serves as an out-of-sample test set from which the MEWMA control limit (CL) is determined. In this paper, we develop a novel bootstrap procedure for computing the CL. Our bootstrap CL provides much more accurate control of false-alarm rate, especially when the sample size and/or false-alarm rate is small. It also allows the entire initial sample to be used for training, resulting in a more accurate fitted supervised learning model. We show that a standard nested bootstrap (inner loop accounting for future data variability and outer loop accounting for training sample variability) substantially underestimates variability and develop a 632-like correction that appropriately accounts for this. We demonstrate the advantages with numerical examples.