Our novel Deep Loop Shaping method improves control of gravitational wave observatories, helping astronomers higher understand the dynamics and formation of the universe.
To assist astronomers study the universe’s strongest processes, our teams have been using AI to stabilize one of the sensitive remark instruments ever built.
In a paper published today in Science, we introduce Deep Loop Shaping, a novel AI method that may unlock next-generation gravitational-wave science. Deep Loop Shaping reduces noise and improves control in an observatory’s feedback system, helping stabilize components used for measuring gravitational waves — the tiny ripples in the material of space and time.
These waves are generated by events like neutron star collisions and black hole mergers. Our method will help astronomers gather data critical to understanding the dynamics and formation of the universe, and higher test fundamental theories of physics and cosmology.
We developed Deep Loop Shaping in collaboration with LIGO (Laser Interferometer Gravitational-Wave Observatory) operated by Caltech, and GSSI (Gran Sasso Science Institute), and proved our method on the observatory in Livingston, Louisiana.
LIGO measures the properties and origins of gravitational waves with incredible accuracy. However the slightest vibration can disrupt its measurements, even from waves crashing 100 miles away on the Gulf coast. To operate, LIGO relies on 1000’s of control systems keeping every part in near-perfect alignment, and adapts to environmental disturbances with continuous feedback.
Deep Loop Shaping reduces the noise level in essentially the most unstable and difficult feedback loop at LIGO by 30 to 100 times, improving the soundness of its highly-sensitive interferometer mirrors. Applying our method to all of LIGO’s mirror control loops could help astronomers detect and gather data about a whole lot of more events per yr, in far greater detail.
In the longer term, Deep Loop Shaping may be applied to many other engineering problems involving vibration suppression, noise cancellation and highly dynamic or unstable systems vital in aerospace, robotics, and structural engineering.
