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LIGO, the Laser Interferometer Gravitational-wave Observatory, has been called the most precise ruler in the world for its ability to measure motions more than 10,000 times smaller than the width of a proton. By making these extremely precise measurements, LIGO, which consists of two facilities—one in Washington and one in Louisiana—can detect undulations in space-time called gravitational waves that roll outward from colliding cosmic bodies such as black holes.

LIGO ushered in the field of gravitational-wave astronomy beginning in 2015 when it made the of these ripples, a discovery that subsequently earned three of its founders the Nobel Prize in Â鶹ÒùÔºics in 2017.

Improvements to LIGO's interferometers mean that it now detects an average of about one black hole merger every three days during its current science run. Together with its partners, the Virgo gravitational-wave detector in Italy and KAGRA in Japan, the observatory has in total detected hundreds of black hole merger candidates, in addition to a handful involving at least one neutron star.

Researchers want to further enhance LIGO's abilities, so that they can detect a larger variety of black-hole mergers, including more massive mergers that might belong to a hypothesized intermediate-mass class bridging the gap between stellar-mass black holes and much larger supermassive black holes residing at the centers of galaxies.

The advances would also make it easier for LIGO to find black holes with eccentric, or oblong, orbits, as well as catch mergers earlier in the coalescing process, when the dense bodies spiral in toward one another.

To do this, researchers at Caltech and Gran Sasso Science Institute in Italy teamed up with Google DeepMind to develop a new AI method–called Deep Loop Shaping–that can better hush unwanted noise in LIGO's detectors. To scientists, the term "noise" can refer to any number of pesky background disturbances that interfere with data collection.

The noise can be literal noise, as in sound waves, but in the case of LIGO, the term often refers to a very tiny amount of jiggling in the giant mirrors at the heart of LIGO. Too much jiggling can mask gravitational-wave signals.

Now, reporting in Science, the that their new AI algorithm, though still a proof-of-concept, quieted motions of the LIGO mirrors by 30 to 100 times more than what is possible using traditional noise-reduction methods alone. The paper is titled "Improving cosmological reach of LIGO using Deep Loop Shaping."

"We were already at the forefront of innovation, making the most precise measurements in the world, but with AI, we can boost LIGO's performance to detect bigger black holes," says co-author Rana Adhikari, professor of physics at Caltech. "This technology will help us not only improve LIGO but also to build next-generation, even bigger gravitational-wave detectors."

The approach could also improve technologies that use control systems. "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," write study co-authors Brendan Tracey and Jonas Buchli, an engineer and scientist, respectively, at Google DeepMind, in a blog post about the study.

The stillest mirrors

Both the Louisiana and Washington LIGO facilities are shaped like enormous "L's," in which each arm of the L contains a vacuum tube that houses advanced laser technology. Within the 4-kilometer-long tubes, lasers bounce back and forth with the aid of giant 40-kilogram suspended mirrors at each end.

As gravitational waves reach Earth from space, they distort space-time in such a way that the length of one arm changes relative to the other by infinitesimally small amounts. LIGO's laser system detects these minute, subatomic-length changes to the arms, registering gravitational waves.

But to achieve this level of precision, engineers at LIGO must ensure that background noises are kept at bay. This study looked specifically at unwanted noises, or motions, in LIGO's mirrors that occur when the mirrors shift in orientation from the desired position by very tiny amounts. Although both of the LIGO facilities are relatively far from the coast, one of the strongest sources of these mirror vibrations is ocean waves.

"It's as if the LIGO detectors are sitting at the beach," explains co-author Christopher Wipf, a gravitational-wave interferometer research scientist at Caltech.

"Water is sloshing around on Earth, and the ocean waves create these very low-frequency, slow vibrations that both LIGO facilities are severely disturbed by."

The solution to the problem works much like noise-canceling headphones, Wipf explains. "Imagine you are sitting on the beach with noise-canceling headphones. A microphone picks up the ocean sounds, and then a controller sends a signal to your speaker to counteract the wave noise," he says. "This is similar to how we control ocean and other seismic ground-shaking noise at LIGO."

However, as is the case with noise-canceling headphones, there is a price. "If you have ever listened to these headphones in a quiet area, you might hear a faint hiss. The microphone has its own intrinsic noise. This self-inflicted noise is what we want to get rid of in LIGO," Wipf says.

LIGO already handles the problem extremely well using a traditional feedback control system. The controller senses the rumble in the mirrors caused by seismic noise and then counteracts these vibrations, but in a way that introduces a new higher-frequency quiver in the mirrors—like the hiss in the headphones.

The controller senses the hiss too and constantly reacts to both types of disturbances to keep the mirrors as still as possible. This type of system is sometimes compared to a waterbed: Trying to quiet waves at one frequency leads to extra jiggling at another frequency. Controllers can automatically sense the disturbances and stabilize a system.

Adhikari wanted to further improve the LIGO control system, in particular to reduce the controller-induced hiss, which interferes with gravitational-wave signals in the lower-frequency portion of the observatory's range.

LIGO detects gravitational waves with a frequency between 10 and 5,000 Hertz (humans hear sound waves with a frequency between 20 and 20,000 Hertz).

The unwanted hiss lies in the range between 10 and 30 Hertz—and this is where more massive black hole mergers would be picked up, as well as where black holes would be caught near the beginning of their final death spirals (for instance, the famous "chirps" heard by LIGO start in lower frequencies then rise to a higher pitch.)

About four years ago, Jan Harms, a former Caltech research assistant professor who is now a professor at Gran Sasso Science Institute, reached out to experts at Google DeepMind to see if they could help develop an AI method to better control vibrations in LIGO's mirrors.

At that point, Adhikari got involved, and the researchers began working with Google DeepMind to try different AI methods. In the end, they used a technique called reinforcement learning, which essentially taught the AI algorithm how to better control the noise.

"This method requires a lot of training," Adhikari says. "We supplied the training data, and Google DeepMind ran the simulations. Basically, they were running dozens of simulated LIGOs in parallel. You can think of the training as playing a game. You get points for reducing the noise and dinged for increasing it. The successful 'players' keep going to try to win the game of LIGO. The result is beautiful—the algorithm works to suppress mirror noise."

Richard Murray (BS), the Thomas E. and Doris Everhart Professor of Control and Dynamical Systems and Bioengineering at Caltech, explains that without AI, scientists and engineers mathematically model a system they want to control in explicit detail.

"But with AI, if you train it on a model of sufficient detail, it can exploit features in the system that you wouldn't have considered using classical methods," he says.

An expert in control theory for complex systems, Murray (who is not an author on the current study) develops AI tools for certain control systems, such as those used in self-driving vehicles.

"We think this research will inspire more students to want to work at LIGO and be part of this remarkable innovation," Adhikari says. "We are at the bleeding edge of what's possible in measuring tiny, quantum distances."

So far, the new AI method was tested on LIGO for only an hour to demonstrate that it works. The team is looking forward to conducting longer duration tests and ultimately implementing the method on several LIGO systems. "This is a tool that changes how we think about what ground-based detectors are capable of," Wipf says. "It makes an incredibly challenging problem less daunting."