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Deep Loop Shaping for Gravitational Wave Observatories

1 post · Sep 2025

What this trail covers

Deep Loop Shaping is a novel AI method that improves control of gravitational wave observatories by reducing noise and stabilizing feedback systems. It has been proven to reduce noise levels in LIGO's mirror control loops by 30 to 100 times, enabling more detailed detection and gathering of data about cosmic events. This technology has potential applications in aerospace, robotics, and structural engineering for vibration suppression and noise cancellation.

All DeepMind trails
  1. Using AI to perceive the universe in greater depthThis post details the application of Deep Loop Shaping, a novel AI method leveraging reinforcement learning with frequency domain rewards, to reduce control noise in LIGO's gravitational wave observatory. The method achieved noise reduction of 30-100 times in the most unstable feedback loop, surpassing traditional linear control methods and eliminating controller-induced noise in the observation band. The performance was validated on both simulated and real hardware at the LIGO Livingston observatory.