Deploying AI for active optics control of the Vera C. Rubin Observatory

Abstract

The Vera C. Rubin Observatory uses an active optics system (AOS) to deliver consistently high image quality across its 9.6 deg2 field of view. The baseline forward-modeling approach provides physically interpretable wavefront fits, but challenges the cadence required for the Legacy Survey of Space and Time (LSST). We describe two AI-based methods for accelerating wavefront inference: AIDonut, a neural-network replacement for the wavefront-estimation step, and TARTS, a larger framework that incorporates donut detection and sensor-level aggregation. Both systems are trained on Rubin simulations and adapted to Rubin commissioning data through transfer learning, using supervised and unsupervised approaches. Early validation in on-sky tests shows both models converge to image quality and alignment consistent with the forward-modeling baseline. These demonstrations are an important step towards fast, AI-assisted active optics control for Rubin, with detailed physical models remaining central to training, validation, and operational monitoring.