ORACLE: Hierarchical Deep-Learning Real-Time Classifier for Astronomical Transients
ORACLE is a GRU-based recurrent neural network that classifies LSST/Rubin transient and variable sources in real time along an observationally driven taxonomy, trained with a custom hierarchical cross-entropy loss so predictions stay consistent from coarse to fine classes. It concatenates the light-curve embedding with host-galaxy context (photometric redshift, offset, ellipticity, brightness) and produces high-confidence coarse classifications from as little as one photometric epoch, matching state-of-the-art fine-grained accuracy while classifying much earlier.
ORACLE: A Real-time, Hierarchical, Deep Learning Photometric Classifier for the LSST Ved G.
The authors present ORACLE (Online Ranked Astrophysical CLass Estimator), described as the first hierarchical deep-learning model for real-time, context-aware classification of transient and variable…