Our novel Deep Loop Shaping method improves control of gravitational wave observatories, helping astronomers better understand the dynamics and formation of the universe.
To help astronomers study the universe’s most powerful processes, our teams have been using AI to stabilize one of the most sensitive observation instruments ever built.
In a paper published today in Science, we introduce Deep Loop Shaping, a novel AI method that will 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 fabric 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 better 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 at the observatory in Livingston, Louisiana.
LIGO measures the properties and origins of gravitational waves with incredible accuracy. But the slightest vibration can disrupt its measurements, even from waves crashing 100 miles away on the Gulf coast. To function, LIGO relies on thousands 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 the most unstable and difficult feedback loop at LIGO by 30 to 100 times, improving the stability 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 hundreds of more events per year, in far greater detail.
In the future, Deep Loop Shaping could also be applied to many other engineering problems involving vibration suppression, noise cancellation and highly dynamic or unstable systems important in aerospace, robotics, and structural engineering.
Source link
#perceive #universe #greater #depth
