Improving cosmological reach of a gravitational wave observatory using Deep Loop Shaping
Jonas Buchli, Brendan Tracey, Tomislav Andric, Christopher Wipf, Yu Him Justin Chiu, Matthias Lochbrunner, Craig Donner, Rana X. Adhikari, Jan Harms, Iain Barr, Roland Hafner, Andrea Huber, Abbas Abdolmaleki, Charlie Beattie, Joseph Betzwieser, Serkan Cabi, Jonas Degrave, Yuzhu Dong, Leslie Fritz, Anchal Gupta
- Year
- 2025
- Access
- Open access
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.
Keywords
Related papers
The Organization of Behavior
D. O. Hebb
2005
Fractional Brownian Motions, Fractional Noises and Applications
Benoît B. Mandelbrot, John W. Van Ness
1968
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi, Jinglan Zhang, Amjad J. Humaidi +7 more
2021
A guide to deep learning in healthcare
Andre Esteva, Alexandre Robicquet, Bharath Ramsundar +7 more
2018