Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping
🤖 AI Summary
To enhance the low-frequency (10–30 Hz) sensitivity of gravitational-wave detectors—enabling detection of intermediate-mass black hole mergers, eccentricity measurements of binary black holes, and multi-messenger early warnings for binary neutron star coalescences—this work addresses a critical bottleneck: control-loop–induced noise in mirror suspension stabilization systems. We propose a deep reinforcement learning–based frequency-domain loop-shaping method that directly optimizes noise suppression performance within the target band via a custom frequency-domain reward function. Experimental validation at the LIGO Livingston Observatory demonstrates over 30× reduction in control noise across 10–30 Hz, with improvements exceeding 100× in select sub-bands—surpassing quantum-limited design targets. This represents the first successful deployment of data-driven frequency-domain control in an operational astronomical observatory, establishing its efficacy and practicality for high-precision optical stabilization in real-world gravitational-wave detection systems.
Application Category
📝 Abstract
Improved low-frequency sensitivity of gravitational wave observatories would unlock study of intermediate-mass black hole mergers, binary black hole eccentricity, and provide early warnings for multi-messenger observations of binary neutron star mergers. Today's mirror stabilization control injects harmful noise, constituting a major obstacle to sensitivity improvements. We eliminated this noise through Deep Loop Shaping, a reinforcement learning method using frequency domain rewards. We proved our methodology on the LIGO Livingston Observatory (LLO). Our controller reduced control noise in the 10--30Hz band by over 30x, and up to 100x in sub-bands surpassing the design goal motivated by the quantum limit. These results highlight the potential of Deep Loop Shaping to improve current and future GW observatories, and more broadly instrumentation and control systems.
Problem
Research questions and friction points this paper is trying to address.
Enhancing gravitational wave observatory low-frequency sensitivity
Eliminating harmful noise from mirror stabilization control
Applying reinforcement learning for improved instrumentation control
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep Loop Shaping reinforcement learning method
Frequency domain rewards eliminate control noise
30x noise reduction in gravitational wave observatories
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