Speaker
Description
Gravitational-wave interferometers rely on hundreds of feedback control loops to stabilize mirror alignment and maintain detector sensitivity. These control systems can inject noise into the observation band, limiting low-frequency performance. Recently, the Deep Loop Shaping (DLS) approach demonstrated that reinforcement learning (RL) can substantially reduce injected control noise in the 10–30 Hz band in the LIGO Livingston Observatory, achieving up to two orders of magnitude suppression on a key alignment loop.
Motivated by these results, we investigate the application of RL-based loop shaping to the Virgo detector. We developed a Virgo-specific Lightsaber time-domain simulation of the DIFFp_TX angular control loop and implemented a distributed RL training pipeline. The environment includes realistic plant dynamics, sensing noise, and stabilization through a baseline linear controller, while the RL agent learns control policies using frequency-domain reward functions designed to suppress control noise while preserving loop stability and taming radiation-pressure effects.
We present the architecture of the training framework, the Virgo Lightsaber model, and results comparing key observables between RL-assisted control and the baseline linear controller. This work represents a crucial step towards the first deployment of reinforcement-learning-based control policies in the Virgo GW detector.