Google DeepMind's AI Revolutionizes Gravitational Wave Detection at LIGO

We could spot a new type of black hole thanks to a mirror-wobbling AI

The Laser Interferometer Gravitational-Wave Observatory (LIGO) uses lasers and mirrors to look for black holes across the universe, and it turns out a Google DeepMind AI could make it even more sensitive Efforts to understand the universe could get a boost from an AI developed by Google DeepMind. The algorithm, which can reduce unwanted noise by up to 100 times, could allow the Laser Interferometer Gravitational-Wave Observatory (LIGO) to spot a particular type of black hole that has so far eluded us. LIGO is designed to detect the gravitational waves produced when objects such as black holes spiral into each other and collide. These waves cross the universe at the speed of light, but the fluctuations they cause in space-time are extremely small - 10,000 times smaller than the nucleus of an atom. Since its first observations 10 years ago, LIGO has recorded such signals produced by nearly 100 black hole collisions. To do so, the experiment consists of two observatories in the US, each with two arms 4 kilometres long that are set perpendicular to each other. Lasers are beamed down each arm, reflected by precise mirrors at the end and then compared using an interferometer. The length of the arms is changed by a tiny amount as gravitational waves wash over them, and this is carefully recorded to build a picture of the origin of these signals. The problem is that such demanding accuracy is required that even distant ocean waves or clouds passing overhead can affect measurements. This noise can easily drown out signals, making some observations impossible. Dozens of major adjustments need to be made to filter out the worst of this noise, tweaking the orientation of mirrors and other equipment. Rana Adhikari at the California Institute of Technology in Pasadena, who worked with DeepMind to develop the new AI technology, says that attempting to automate these adjustments can ironically create more noise. "That controls noise has been bedevilling us for decades and decades - everything in this field has been blocked," says Adhikari. "How do you hold the mirrors so still without inducing noise? If you don't control them, the mirrors swing all over the place, and if you control it too much, then it sort of buzzes around." Laura Nuttall at the University of Portsmouth in the UK was one of the scientists who used to manually make these tweaks at LIGO. "As you move one thing, something else goes, and something else goes and something else goes," she says. "You'd spend forever tweaking." DeepMind's new Deep Loop Shaping AI aims to reduce the level of noise from adjusting the mirrors at LIGO by up to 100 times. The AI was trained in a simulation before testing in the real world, and is effectively tasked with achieving two goals: reducing noise and minimising the number of adjustments it makes. "Over time, by repeatedly doing it - it's like hundreds and thousands of trials that are running in simulation - the controller will sort of find what works and what doesn't work and find a really, really good policy," says Jonas Buchli at DeepMind. Alberto Vecchio at the University of Birmingham, UK, who wasn't involved in the research but works on LIGO, says the AI is exciting, although there are many hurdles yet to overcome. Firstly, the technology has only been run for an hour in the real world on LIGO, so it needs to be shown that it can operate for weeks or even months at a time. Secondly, the technology has so far only been applied to one aspect of control, helping to stabilise the mirrors, and there are hundreds if not thousands of aspects it could conceivably be applied to. "It's clearly just the first step, but I still think it's a very intriguing one. And clearly there is plenty of room for enormous progress," says Vecchio. If similar improvements could be made across the board, then he believes we could spot so-called intermediate-size black holes - for example those with masses around 1000 times that of our sun - a class of objects without any confirmed observations. The improvements would tend to occur on lower-frequency gravitational waves, where the length of wave is more susceptible to noise, and which are created by larger objects. "We know black holes up to 100 solar masses. We know the black holes in our galaxy that are a million solar masses and above. What's in between?" says Vecchio. "People think there will be black holes at all these different mass ranges, but nobody has got uncontroversial experimental observational evidence." Nuttall says that the new approach could also provide more detailed observation of the types of black hole we have already seen. "This is looking pretty damn good," she says. "I'm super excited by this."

LIGO and Google create a new AI tool to supercharge the hunt for gravitational waves

The Laser Interferometer Gravitational-Wave Observatory, or LIGO, has already won its researchers a Nobel Prize -- and now artificial intelligence is poised to take LIGO's search for cosmic collisions to the next level. Google DeepMind and the LIGO team say they've developed an AI tool called Deep Loop Shaping that has been shown to enhance the observatory's ability to track gravitational waves -- faint ripples in the fabric of spacetime that are thrown off by smash-ups involving black holes and massive neutron stars. The researchers describe the technique in a proof-of-concept study published today by the journal Science. They hope to make Deep Loop Shaping part of routine operations at LIGO's detectors in Louisiana and on the Hanford nuclear site in Washington state. "Deep Loop Shaping is revolutionary, because it is able to reduce the noise level in the most unstable and most difficult feedback loop at LIGO," lead author Jonas Buchli, a research scientist at Google DeepMind, told reporters. The existence of gravitational waves was predicted by Albert Einstein a century ago, but they were first observed directly only in 2015, using LIGO's twin 2.5-mile-long interferometers. That feat was recognized with the Nobel Prize in physics in 2017. Since then, the LIGO team has worked to increase the sensitivity of the detectors, but it's not an easy task. LIGO's system of mirrors and precision laser beams have to register spacetime warps that amount to just one-10,000th the width of a proton. At that level of sensitivity, perturbations from distant earthquakes and ocean waves can jiggle the mirrors enough to make a difference in what's registered by the detectors. Researchers developed sophisticated techniques -- including some that rely on AI -- to keep the 88-pound mirrors still and cancel out the "noise" from such perturbations. But compensating for that noise introduces a different kind of disturbance. "The hardest problem left is, how do you keep everything so still without disturbing your measurement?" said Rana Adhikari, a member of the LIGO team at Caltech. "That 'controls noise' has been bedeviling us for decades and decades." Adhikari compared the problem to trying to hold a mirror still with your bare hands. "If you try to keep it really still, your hands start to shake because you're holding it tightly," he said. "This method takes away the shaking." Google's engineers worked with LIGO's scientists to develop software that was trained on simulated gravitational-wave readings, using a process known as reinforcement learning. "Basically, they were running dozens of simulated LIGOs in parallel," Adhikari said in a news release. "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." The proof-of-concept test results, based on an hour's worth of LIGO data from the detector in Louisiana, showed that Deep Loop Shaping could quiet the motion of the mirrors 30 to 100 times better than traditional noise-reduction methods alone. Study co-author Jan Harms, a professor at Italy's Gran Sasso Science Institute, said the technique could open up a new frontier in astronomy. "We are really excited about the potential to advance gravitational-wave science with Deep Loop Shaping," he said. "More specifically, what we can now do is open a new frequency band for gravitational-wave observations, toward the low-frequency end." Harms said the opportunity was analogous to expanding the range of a telescope to take in infrared or X-rays as well as optical wavelengths. With greater sensitivity at lower frequencies, LIGO could do a better job of detecting the collisions of neutron stars or intermediate-mass black hole binaries. LIGO could also provide more advance warning of imminent cosmic collisions. "What you can do is a pre-merger alert, so you can let people know [that] a minute from now, two neutron stars will merge," Harms said. "And then, if you have just enough detectors online, you can even point to a specific patch in the sky and tell them, 'Look there and wait for it.'" With all the talk about AI hallucinations, should people be worried that Deep Loop Shaping could produce false data? "I think the question of 'Will it misbehave after it's running a year' is a legitimate question, but we also worry about that for our classical methods, and so we monitor all of these things," Adhikari said. "This is a new area for us, so I think we'll be learning as we go along, and we'll be developing methods to veto any sort of misbehavior, not just from this system, but even our classical systems that do misbehave sometimes," he said. In the next phase of the rollout, the LIGO team plans to put Deep Loop Shaping through longer test runs that could go on for days or weeks at a time -- in Louisiana, at Hanford and eventually at LIGO-India. "This week is the time when we're going to start to have those discussions," Adhikari said. Deep Loop Shaping, and programs like it, could well become part of the standard engineering toolkit -- not just for gravitational-wave detectors, but also for other applications where components have to be controlled with high precision. "This is about applications in aerospace, for example," Harms said. "Navigation or manufacturing, or generally noise reduction in systems, or also civil engineering." The technology might even find its way into noise-canceling headphones. And Buchli said there may be still other applications that engineers haven't yet thought of. "I think once we send this out, hopefully some more people come to think, 'Oh, actually, gee, I have this really hard control problem. I think I will try that,'" he said. Buchli, Adhikari and Harms are among 30 authors of the Science study, "Improving Cosmological Reach of a Gravitational Wave Observatory Using Deep Loop Shaping."

AI helps astronomers better explore the universe

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. LIGO uses the interference of laser light to measure the properties of gravitational waves. By studying these properties, scientists can figure out what caused them and where they came from. The observatory's lasers reflect off mirrors positioned 4 kilometers apart, housed in the world's largest vacuum chambers.