Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 2 of its 24 references that are in the map. First posted on arXiv 17 Sep 2025.
Builds on
2 of 24 references.
References · 2
- 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 641
- 2017A Rewriting System for Convex Optimization Problemscited by 179
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026gwBenchmarks: Stress-Testing LLM Agents on High-Precision Gravitational Wave Astronomy
- 2025Deep Learning in Astrophysics
- 2024Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach
- 2023Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Offline Reinforcement Learning
- 2022Inference-optimized AI and high performance computing for gravitational wave detection at scale
- 2026Continuous Delayed-Memory Stochastic Gradient Descent and Continuous-Time Reinforcement Learning from History of Astrophysical Time Series Studies
- 2026Discovery of Interpretable Surrogates via Agentic AI: Application to Gravitational Waves
- 2026On-sky demonstration of reinforcement learning for adaptive optics control