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 encod...
| Authors | Jiezhong Wu, Jack O'Brien, Jennifer Li et al. |
|---|---|
| Primary category | astro-ph.IM |
| Published | 4 March 2026 |
| Last updated at source | 4 March 2026 |
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