Using AI to perceive the universe in greater depth

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Using AI to perceive the universe in greater depth

Science Published 4 September 2025 Authors Brendan Tracey, Jonas Buchli 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.Measuring across the universeLIGO 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. Aerial view of LIGO (Laser Interferometer Gravitational-Wave Observatory) in Livingston, Louisiana, USA. The observatory’s lasers reflect off mirrors positioned 4 kilometers apart. Photo credit of Caltech/MIT/LIGO Lab. Since first detecting gravitational waves produced by a pair of colliding black holes, in 2015, verifying the predictions of Albert Einstein’s general theory of relativity, LIGO’s measurements have deeply changed our understanding of the universe.With this observatory, astronomers have detected hundreds of black hole and neutron star collisions, proven the existence of binary black hole systems, seen new black holes formed in neutron star collisions, studied the creation of heavy elements like gold and more.Astronomers already know a lot about the largest and smallest black holes, but we only have limited data on intermediate-mass black holes — considered the “missing link” to understanding galaxy evolution.Until now, LIGO has only been capable of observing very few of these systems. To help astronomers capture more detail and data of this phenomena, we worked to improve the most difficult part of the control system and expand how far away we can see these events. “ Studying the universe using gravity instead of light, is like listening instead of looking. This work allows us to tune in to the bass. Rana Adhikari, Professor of Physics at the Caltech, 2025 Reducing noise and stabilizing the systemAs gravitational waves pass through LIGO’s two 4 kilometer arms, they warp the space between them, changing the distance between the mirrors at either end. These tiny differences in length are measured using light interference to an accuracy of 10^-19 meters, which is 1/10’000 the size of a proton. With measurements this small, LIGO’s detector mirrors must be kept extremely still, isolated from environmental disturbance. Closeup photograph of LIGO, which uses strong lasers and mirrors to detect gravitational waves in the universe, generated by events like collisions and mergers of black holes. Photo credit of Caltech/MIT/LIGO Lab. This requires one system for passive mechanical isolation and another control system for actively suppressing vibrations. Too little control causes the mirrors to swing, making it impossible to measure anything. But too much control actually amplifies vibrations in the system, instead of suppressing them, drowning out the signal in certain frequency ranges.These vibrations, known as “control noise”, are a critical blocker to improving LIGO’s ability to peer into the universe. Our team designed Deep Loop Shaping to move beyond traditional methods, such as the linear control design methods currently in operation, to remove the controller as a meaningful cause of noise.A more effective control systemDeep Loop Shaping leverages a reinforcement learning method using frequency domain rewards and surpasses state-of-the-art feedback control performance.In a simulated LIGO environment, we trained a controller that tries to avoid amplifying noise in the observation band used for measuring gravitational waves — the band where we need the mirror to be still to see events like black hole mergers of up to a few hundred solar masses. Diagram showing LIGO’s intricate systems of lasers and mirrors. A distributed control system actively adjusts the mirrors, counteracting the laser radiation pressure and vibrations from external sources. Through repeated interaction, guided by frequency domain rewards, the controller learns to suppress the control noise in the observation band. In other words, our controllers learn to stabilize the mirrors without adding harmful control noise, bringing noise levels down by a factor of ten or more, below the amount of vibrations caused by quantum fluctuations in the radiation pressure of light reflecting off the mirrors.Strong performance across simulation and hardwareWe tested our controllers on the real LIGO system in Livingston, Louisiana, USA — finding that they worked as well on hardware as in simulation.Our results show that Deep Loop Shaping controls noise up to 30-100 times better than existing controllers, and it eliminated the most unstable and difficult feedback loop as a meaningful source of noise on LIGO for the first time. Line chart showing the resulting control noise spectrum using our Deep Loop Shaping method. There is an improvement of 30-100 times in the injected control noise levels in the most unstable and difficult feedback control loop. In repeated experiments, we confirmed that our controller keeps the observatory’s system stable over prolonged periods.Better understanding the nature of the universeDeep Loop Shaping pushes the boundaries of what’s currently possible in astrophysics by solving a critical blocker to studying gravitational waves.Applying Deep Loop Shaping to LIGO’s entire mirror control system has the potential to eliminate noise from the control system itself, paving the way for expanding its cosmological reach.Beyond significantly improving how existing gravitational wave observatories measure further and dimmer sources, we expect our work to influence the design of future observatories, both on Earth and in space — and ultimately help connect missing links throughout the universe for the first time. Learn more about our work AcknowledgementsThis research was done by 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, Oliver Groth, Sandy Huang, Tamara Norman, Hannah Openshaw, Jameson Rollins, Greg Thornton, George van den Driessche, Markus Wulfmeier, Pushmeet Kohli, Martin Riedmiller and is a collaboration of LIGO, Caltech, GSSI and GDM.We’d like to thank the fantastic LIGO instrument team for their tireless work on keeping the observatories up and running and supporting our experiments.

Science

Published: 4 September 2025
Authors: Brendan Tracey, Jonas Buchli

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.

Measuring across the universe

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.

Aerial view of LIGO (Laser Interferometer Gravitational-Wave Observatory) in Livingston, Louisiana, USA. The observatory’s lasers reflect off mirrors positioned 4 kilometers apart. Photo credit of Caltech/MIT/LIGO Lab.

Since first detecting gravitational waves produced by a pair of colliding black holes, in 2015, verifying the predictions of Albert Einstein’s general theory of relativity, LIGO’s measurements have deeply changed our understanding of the universe.

With this observatory, astronomers have detected hundreds of black hole and neutron star collisions, proven the existence of binary black hole systems, seen new black holes formed in neutron star collisions, studied the creation of heavy elements like gold and more.

Astronomers already know a lot about the largest and smallest black holes, but we only have limited data on intermediate-mass black holes — considered the “missing link” to understanding galaxy evolution.

Until now, LIGO has only been capable of observing very few of these systems. To help astronomers capture more detail and data of this phenomena, we worked to improve the most difficult part of the control system and expand how far away we can see these events.

“

Studying the universe using gravity instead of light, is like listening instead of looking. This work allows us to tune in to the bass.

Rana Adhikari, Professor of Physics at the Caltech, 2025

Reducing noise and stabilizing the system

As gravitational waves pass through LIGO’s two 4 kilometer arms, they warp the space between them, changing the distance between the mirrors at either end. These tiny differences in length are measured using light interference to an accuracy of 10^-19 meters, which is 1/10’000 the size of a proton. With measurements this small, LIGO’s detector mirrors must be kept extremely still, isolated from environmental disturbance.

Closeup photograph of LIGO, which uses strong lasers and mirrors to detect gravitational waves in the universe, generated by events like collisions and mergers of black holes. Photo credit of Caltech/MIT/LIGO Lab.

This requires one system for passive mechanical isolation and another control system for actively suppressing vibrations. Too little control causes the mirrors to swing, making it impossible to measure anything. But too much control actually amplifies vibrations in the system, instead of suppressing them, drowning out the signal in certain frequency ranges.

These vibrations, known as “control noise”, are a critical blocker to improving LIGO’s ability to peer into the universe. Our team designed Deep Loop Shaping to move beyond traditional methods, such as the linear control design methods currently in operation, to remove the controller as a meaningful cause of noise.

A more effective control system

Deep Loop Shaping leverages a reinforcement learning method using frequency domain rewards and surpasses state-of-the-art feedback control performance.

In a simulated LIGO environment, we trained a controller that tries to avoid amplifying noise in the observation band used for measuring gravitational waves — the band where we need the mirror to be still to see events like black hole mergers of up to a few hundred solar masses.

Diagram showing LIGO’s intricate systems of lasers and mirrors. A distributed control system actively adjusts the mirrors, counteracting the laser radiation pressure and vibrations from external sources.

Through repeated interaction, guided by frequency domain rewards, the controller learns to suppress the control noise in the observation band. In other words, our controllers learn to stabilize the mirrors without adding harmful control noise, bringing noise levels down by a factor of ten or more, below the amount of vibrations caused by quantum fluctuations in the radiation pressure of light reflecting off the mirrors.

Strong performance across simulation and hardware

We tested our controllers on the real LIGO system in Livingston, Louisiana, USA — finding that they worked as well on hardware as in simulation.

Our results show that Deep Loop Shaping controls noise up to 30-100 times better than existing controllers, and it eliminated the most unstable and difficult feedback loop as a meaningful source of noise on LIGO for the first time.

Line chart showing the resulting control noise spectrum using our Deep Loop Shaping method. There is an improvement of 30-100 times in the injected control noise levels in the most unstable and difficult feedback control loop.

In repeated experiments, we confirmed that our controller keeps the observatory’s system stable over prolonged periods.

Better understanding the nature of the universe

Deep Loop Shaping pushes the boundaries of what’s currently possible in astrophysics by solving a critical blocker to studying gravitational waves.

Applying Deep Loop Shaping to LIGO’s entire mirror control system has the potential to eliminate noise from the control system itself, paving the way for expanding its cosmological reach.

Beyond significantly improving how existing gravitational wave observatories measure further and dimmer sources, we expect our work to influence the design of future observatories, both on Earth and in space — and ultimately help connect missing links throughout the universe for the first time.

Learn more about our work

Acknowledgements

This research was done by 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, Oliver Groth, Sandy Huang, Tamara Norman, Hannah Openshaw, Jameson Rollins, Greg Thornton, George van den Driessche, Markus Wulfmeier, Pushmeet Kohli, Martin Riedmiller and is a collaboration of LIGO, Caltech, GSSI and GDM.

We’d like to thank the fantastic LIGO instrument team for their tireless work on keeping the observatories up and running and supporting our experiments.

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To close industry watchers, it’s really no secret that the AI infrastructure race has entered another phase; one where capital formation itself may become as strategically important as GPUs, power procurement, or liquid cooling. And in launching Helix Digital Infrastructure, investment giant KKR is making a calculated wager that hyperscalers no longer simply need developers or financiers. They need a partner capable of orchestrating capital, energy, connectivity, and data center execution as a unified platform. The significance of that strategy is underscored by the executive chosen to lead it. Adam Selipsky, the former CEO of Amazon Web Services and one of the industry’s most experienced cloud operators, will serve as Co-Founder and CEO of Helix, bringing firsthand experience from the very class of customers the new venture intends to serve. A New Model for AI Infrastructure Helix launches with more than $10 billion in long-duration committed capital from founding investors including KKR, the Kuwait Investment Authority (KIA), NVIDIA, and Vistra. But the headline number tells only part of the story. The company has been structured around an increasingly important thesis: that AI infrastructure can no longer be assembled piecemeal. Rather than treating data centers, electrical supply, transmission capacity, and fiber connectivity as separate procurement exercises, Helix proposes a vertically coordinated approach in which a single organization manages and finances the entire infrastructure stack. According to KKR, the objective is to reduce execution risk and accelerate deployment for hyperscale customers facing unprecedented AI demand. As AI factories grow from hundreds of megawatts toward gigawatt-scale campuses, synchronization among land acquisition, utility planning, financing, construction, and technology deployment has emerged as one of the industry’s defining challenges. Helix is effectively positioning itself as an operating platform designed to simplify that complexity. Why Selipsky Matters The appointment of Adam Selipsky may be the announcement’s

Beyond Hyperscale: Why Enterprise Data Centers Still Matter in the AI Era

“The enterprise data centers, even the new ones, tend to be far, far smaller than new hyperscale deployments,” Killian said. “Not uncommon to see enterprises deploy a quarter meg or one meg or two, maybe up to 10 megs. Whereas the hyperscale guys are deploying 40 up to 300 meg facilities.” But scale alone does not tell the story. For every one of the roughly 20 hyperscale users that dominate headlines, Killian noted, there may be 50 to 100 times as many large and mid-sized enterprise users. Those companies run critical business systems, purchase hardware, software, telecom and services, employ large data center teams, and often operate multiple facilities across domestic, edge, EMEA and Asia-Pacific footprints. In other words, enterprise demand may be smaller in unit size, but it remains massive in aggregate. And as AI shifts from training to inference, the enterprise data center could become newly strategic. Enterprise AI Is Not Hyperscale AI Killian’s central point is that enterprise infrastructure requirements differ materially from hyperscale requirements. Hyperscalers are primarily optimizing for massive scale and speed to market. Enterprises, by contrast, tend to prioritize reliability, flexibility, integration into broader IT systems, and audit and compliance. That difference has major implications for developers and colocation providers. “The real industry opportunity is to take some of the innovation and the economies of scale that we’re seeing from the hyperscale builds to deliver smaller chunks of data center capacity,” Killian said. That might mean adapting lessons from 40 MW or 100 MW campuses into enterprise-ready deployments of 2 MW, 4 MW or 8 MW. Killian pointed to providers such as DataBank and Flexential as examples of companies working to deliver hyperscale-derived efficiencies in smaller enterprise increments. He also noted that QTS and other large campus developers may reserve portions of multi-building campuses

Revolutionizing Data Center Cooling: Innovations for AI and HPC Growth

This is a crucial point for AI infrastructure. In some markets, water can be as politically and operationally difficult as power. Evaporative cooling and cooling towers can consume large volumes of water, while discharge permits can slow projects or limit operations. Gradiant claims HyperSolved can expand access to alternative sources such as municipal reuse and impaired supplies, reduce reliance on freshwater, protect cooling performance through integrated treatment and AI-enabled operations, and minimize discharge through high-recovery concentration and reuse. The platform uses containerized systems for immediate or temporary capacity while also supporting permanent infrastructure and lifecycle operations from commissioning onward. That fits the AI data center buildout, where developers may need bridge capacity during construction, phased water infrastructure, or interim systems while permanent treatment plants are completed. This can address the speed of deployment issue that plagues many data center solutions. Water is becoming a siting and scaling variable that has to be addressed. A site may have land and power prospects, but if water sourcing, reuse, or discharge cannot be solved, the project will face higher costs, delays, and local opposition. Gradiant is positioning itself as the managed water layer for hyperscale AI, similar to how power providers, cooling vendors, and network suppliers each own critical infrastructure domains. The Pattern: Hybridization, Standardization, and Industrial Scale The announcements included here make it clear that cooling is seeing significant attention from technology vendors, and not just state-of-the-art new technologies such as direct-to-chip, but also traditional data center air cooling. T-Global and SiPearl are working on high-conductivity materials and two-phase modules for HPC chips. Castrol is providing fluids for direct-to-chip and immersion environments. These are technologies aimed at the heat source itself, where higher chip power and rack density are overwhelming conventional approaches. The reference design offerings from Johnson Controls acknowledges the importance

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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