According to quantum field theory (QFT), the framework that describes elementary particles and their interactions, empty space should be filled with quantum fluctuations that contribute an enormous amount of energy. In fact, calculations suggest the cosmological constant should be extraordinarily large, effectively approaching infinity.

Yet observations show something very different. The actual value of the cosmological constant is incredibly small compared with what theory predicts.

Now, researchers at Brown University have proposed a possible explanation.

Their work suggests that a mathematical feature of space-time itself may prevent the cosmological constant from ballooning to the huge values expected from quantum physics. The idea draws on an unexpected connection between quantum gravity and the quantum Hall effect, a remarkable phenomenon in condensed matter physics.

A Surprising Link Between Quantum Gravity and the Quantum Hall Effect

The team found that the mathematics behind a simple approach to quantum gravity closely resembles the mathematics that describes the quantum Hall effect, an unusual state of matter in which electrical conductance takes on highly precise values.

In the quantum Hall effect, those values remain fixed even when the conducting material contains imperfections. The stability comes from topology, a branch of mathematics concerned with the underlying “shape” or structure of a system.

The researchers argue that a similar type of topology appears in the Chern-Simons-Kodama state, a proposed ground state of quantum gravity.

“What we’ve shown is that if space-time has this non-trivial topology, then it resolves one of the deadliest problems of the cosmological constant,” said study co-author Stephon Alexander, a professor of physics at Brown. “All the quantum perturbations that should blow up the value of the cosmological constant are rendered inert by this topology, which keeps the constant’s value stable.”

The study, co-authored by Alexander and Brown Theoretical Physics Center colleagues Aaron Hui and Heliudson Bernardo, was published in Physical Review Letters.

Einstein’s “Ugly” Cosmological Constant

The cosmological constant first appeared in Albert Einstein’s equations of general relativity, his theory of space, time, and gravity.

At the time, Einstein believed the universe was static. To keep his equations from predicting a collapsing universe, he introduced the cosmological constant as a kind of repulsive effect in empty space that counterbalanced gravity.

That idea seemed unnecessary after Edwin Hubble discovered in 1929 that the universe was expanding. Since the cosmos was not static after all, Einstein removed the term from his equations. He reportedly disliked the constant and later referred to it as his “biggest blunder.”

For decades, the cosmological constant largely faded from prominence.

Then, in 1998, astronomers discovered something surprising: the expansion of the universe is speeding up. Rather than disappearing from the story, the cosmological constant suddenly became essential again because it could account for this accelerating expansion.

The Cosmological Constant Problem

The revival of the cosmological constant created a serious problem.

During the years when the constant had fallen out of favor, quantum field theory had become one of the most successful theories in science and a cornerstone of the Standard Model of particle physics.

QFT describes empty space as anything but empty. Instead, it is filled with particles constantly appearing and disappearing through quantum fluctuations.

All of this activity should contribute a vast amount of vacuum energy. That vacuum energy is associated with the cosmological constant, which means the constant should be extraordinarily large.

But observations show that it is not.

If the cosmological constant were as large as QFT predicts, the universe would have expanded so rapidly that galaxies, stars, planets, and ultimately life could never have formed.

The mismatch between theory and observation remains one of the most perplexing problems in modern physics. The puzzle is made even more striking because experiments have repeatedly confirmed the extraordinary accuracy of quantum field theory in other contexts.

A Topological Solution

Alexander has spent years studying Chern-Simons-Kodama (CSK) theory, a proposed quantum gravity state that emerges from quantum field theory.

Physicists still lack a complete quantum theory of gravity that describes gravity at the smallest scales. According to Alexander, the CSK approach is among the more straightforward possibilities.

“It’s a really conservative approach to quantizing gravity,” he said. “This is the approach used by people like Dirac, Schrödinger and Wheeler. It’s just good, old-fashioned quantization.”

Alexander had long noticed similarities between CSK theory and the mathematics of the quantum Hall effect. To better understand those connections, he collaborated with Hui, an assistant professor at Brown who studies topological systems.

“This is the beauty of the Brown Theoretical Physics Center,” Alexander said. “We want to be a place where there’s a mixing of lots of perspectives, and this is us practicing what we preach — a cosmologist working closely with a condensed matter theorist.”

How Topology Creates Stability

The researchers found that the cosmological constant in the CSK framework appears to benefit from the same kind of topological protection seen in the quantum Hall effect.

The quantum Hall effect occurs when electricity flows through extremely thin materials exposed to a magnetic field.

Imagine a thin rectangular strip of metal carrying an electric current. When a magnetic field is applied, a second voltage develops at right angles to the current. This effect produces what is known as a Hall voltage (named after Edwin Hall, who discovered it).

Under ordinary conditions, the Hall voltage changes smoothly as the magnetic field increases.

Under extremely cold temperatures and very strong magnetic fields, however, the behavior changes dramatically. Instead of varying smoothly, the Hall voltage increases in distinct steps and plateaus. Remarkably, those values remain identical regardless of the material being used or any imperfections it contains.

That reliability comes from topology.

In these extreme conditions, electrons behave collectively and enter a highly correlated quantum state. The topology of that state fixes the values of the steps and plateaus, making them resistant to disturbances and defects.

The Brown researchers argue that an analogous process occurs in the CSK description of quantum gravity.

Just as topology locks the Hall voltage into specific values, the topology of space-time could lock the cosmological constant into stable values, protecting it from the quantum fluctuations that would otherwise drive it much higher.

“What we find is that this quantization of the electrical conductance in quantum Hall has an analog with the cosmological constant,” Hui said. “It also ends up becoming quantized for topological reasons. There turn out to be constraints in the theory that force the cosmological constant to take certain allowed quantized values.”

A New Direction for Quantum Gravity

Alexander emphasizes that much more work is needed before a topological explanation of the cosmological constant can be fully established.

Still, he believes the findings represent an important step toward solving the gravitational side of the problem. The work also strengthens the case for the CSK state as a serious candidate for a future theory of quantum gravity.

“We took something old, which is this conservative, canonical approach to quantum gravity, and discovered something new that had been there all along,” Alexander said. “Now we’re working on a bigger picture of how this phenomenon works.”

Among the most ambitious and challenging ideas riding this wave of enthusiasm is something that sounds almost like science fiction: orbital data centers. SpaceX may be one of the most well-known companies seeking to build them, but it is not the only one.

The logic is seductive: Launch the data centers into orbit, where solar energy is abundant and land, water and local power grids are no longer constraints. As artificial intelligence drives an explosion in computing demand, companies are pitching orbital data centers as a way to escape the growing environmental and infrastructure pressures of Earth-based computing. Data centers often also face backlash from the public at having these centers located in their communities.

But there is a vast difference between launching satellites and operating an industrial-scale computing infrastructure in orbit. Space is unforgiving. Radiation damages electronics. The electronics generate enormous amounts of heat, and getting rid of that heat is surprisingly difficult in space. Repairs are extraordinarily expensive, and every pound launched into orbit still carries a significant cost.

We are engineering professors who study data center design and space systems engineering. Building a space-based data center will involve considerations from both sides.

What goes into a data center on Earth

First off, consider what goes into an Earth-based data center, like those that you’ve probably begun to see pop up everywhere. These facilities power cloud computing, video streaming, online banking, scientific computing and, increasingly, artificial intelligence. But a data center is much more than a room full of servers.

A data center needs several things to operate reliably. The first is electric power. Servers, networking equipment and storage devices consume large amounts of electricity, and that power demand is growing rapidly with AI.

The second is cooling. Almost all the electricity consumed by servers eventually becomes heat. If that heat is not removed quickly and reliably, equipment performance drops, failures increase and the data center can shut down. Cooling systems often include air handling units, chillers, cooling towers, pumps and, increasingly, liquid-cooling equipment. In many facilities, cooling is the largest energy consumer after the computing equipment itself.

The third is physical infrastructure, including the necessary land, buildings, structural support, backup power, water systems, communication networks and maintenance access. Data centers also need to be close enough to users and network backbones to provide fast digital services.

In short, Earth-based data centers are large electrical and thermal infrastructure systems built around computing hardware.

Placing them in space

So what would it take to build these data centers in space, and why are companies finding this possibility such an interesting business proposition?

As on Earth, these data centers would require massive amounts of power. In space, this power would come from solar panels. The Sun always shines in space and can’t be blocked by clouds. However, depending on the orbit the solar panels are put in, the Earth may shadow them for some portion of the orbit.

And even the best solar cells available today can convert only about half the sunlight that hits them to electricity.

Another potential advantage found in space is cooling. The cold background of space (near minus 455 degrees Fahrenheit, or minus 270 degrees Celsius) creates an opportunity: waste heat from the data center could escape into space through radiators, keeping the electronics cool.

In principle, that design could eliminate some of the bulky and water-intensive cooling infrastructure used on Earth. However, those thermal radiators would require a large amount of surface area, and that would be in addition to the area required by the solar panels.

In space, there is no air to blow across hot equipment and help heat escape. The heat has to leave as infrared radiation, which is a relatively slow process. As a result, removing 10 megawatts of waste heat can require radiator surfaces comparable to the size of two football fields.

Space-based data centers could also avoid some of the local conflicts that come with building large data centers on the ground. Many communities resist new data center developments because of their land use, energy and water demand, and noise and environmental impact.

A space-based system would avoid competing for local land and water resources, and it would not generate neighborhood noise or require local zoning approval in the same way.

However, space is already getting crowded, and launching thousands of large orbital data centers would accelerate this issue. Orbital debris and micrometeorites are hazards because they can puncture the space data center, and a worst-case collision could destroy it and create even more space debris.

The frequency of space launches necessary to send all the equipment to orbit may also become a concern for some communities. SpaceX has had protests at its launch complex in Boca Chica, Texas, from local activists who argue that its rocket testing and launches damage the surrounding environment.

All that data would need to be sent between Earth and these data centers – and between the data centers themselves – using radio waves or laser communications systems. Although satellite constellations such as Starlink and Amazon Leo have demonstrated that doing this is possible, the amount of data sent to and from space would balloon.

Additional challenges

These data centers, along with their solar panels and radiators, cannot be launched in one piece and would need to be assembled in space. This process would require new equipment for in-space servicing, assembly and manufacturing.

Another key challenge is the refresh cycle of computing hardware. Data center servers are not built to last forever. Operators on Earth usually replace or upgrade hardware every three to five years as chips improve, workloads change and equipment ages.

And equipment failures can require replacing components. The refresh and repair processes are relatively straightforward on Earth, where workers can physically remove and replace servers.

In space, refresh and repair becomes much harder. Hardware sent to orbit may be difficult or too expensive to upgrade. If the computing platform cannot be updated, or too many components fail, it may become obsolete long before the surrounding infrastructure reaches the end of its useful life.

In a field where performance improves so rapidly and demand from computing continues to increase, this hurdle could prove a major economic and operational challenge.

Then there is the harshness of space. These data centers would be in a near vacuum, with constant radiation hitting them. And depending on their orbit, they would go from hot when in the sunlight to cold in Earth’s shadow many times a day. All of these challenges, and more, are issues that will need to be addressed.

So, do they still make sense?

Despite these challenges, companies are moving forward with designing space-based data centers. SpaceX just announced the design for its AI1 Compute Satellite, which it hopes to use as an orbital data center spacecraft. However, this satellite is 100 to 1,000 times less capable than current Earth-based data centers.

Not every computing task makes sense to do in space. Many data center applications depend on fast response times and close connections to users on Earth. Financial transactions, interactive AI services and most cloud applications are extremely sensitive to delay.

More feasible early applications may be those that are less latency-sensitive and more tightly connected to space operations. Examples could include processing Earth observation data from satellites, military or intelligence data processing, scientific computing related to space missions, or specialized computing for satellites and other space assets.

In other words, the first viable space data centers may serve space-based customers before they compete with mainstream cloud data centers on Earth.

In years with ample precipitation, winter snow that accumulates in the Mogollon Mountains and Black Range provides much of the river’s spring runoff. That water helps replenish San Carlos Reservoir, which was created by the Coolidge Dam. When full, the reservoir ranks among Arizona’s largest lakes.

Snowpack Collapse Drains the Reservoir

Conditions were dramatically different in 2026. Snowfall in the Gila River watershed was exceptionally scarce, leaving mountain snowpack at just 2 percent of the 1991-2020 March median. As a result, streamflow during April reached only 39 percent of normal levels.

By June, required releases of water for downstream agricultural use had further reduced supplies. The reservoir contained less than 400 acre-feet of water.

A comparison of Landsat satellite images highlights the dramatic change. The image above (right), captured on May 22, 2026, shows San Carlos Reservoir holding only 389 acre-feet of water, making it less than 1 percent full. By contrast, the image on the left, taken in June 2023, shows the reservoir at roughly 60 percent capacity. Vegetation visible along the reservoir shoreline and river channel includes tamarisk, willow, cottonwood, sedges, and various grasses.

Massive Fish Kill Forces Closure

As water levels continued to fall, oxygen levels in the reservoir dropped sharply. The resulting hypoxia killed virtually all of the fish living there.

Officials responded by closing the reservoir indefinitely on June 5, 2026. Fish species affected included largemouth bass, black crappie, bluegill, channel catfish, flathead catfish, and stocked species such as brown trout and rainbow trout.

The San Carlos Recreation and Wildlife Department also warned that decomposing fish could create health risks for people attempting to fish or boat in the area.

A Long History of Extreme Low Water

Although the current situation is severe, it is not without precedent. News reports indicate that San Carlos Reservoir has completely run dry at least 20 times since it first filled in 1930.

Even during the original dedication of the dam and reservoir, conditions were dry enough for grass to grow across exposed lakebed. Humorist Will Rogers famously joked to President Calvin Coolidge: “If that was my lake, I’d mow it.”

Major fish kills have occurred before as well, including in 1976 and 2018. According to the Gila Herald, a fish kill in 1976 claimed more than 5 million fish, and the reservoir’s ecosystem required five years to recover.

Drought Continues but Rain Could Bring Relief

The region remains locked in a multi-year dry spell. Data from the U.S. Drought Monitor show that much of the Gila River’s headwaters in New Mexico is currently experiencing severe drought.

Still, the river’s flow can vary dramatically from year to year. Significant rainfall during the upcoming wet season could help replenish the reservoir.

A NOAA seasonal monsoon outlook issued in May 2026 estimated a 33 to 50 percent chance of above-average rainfall across the region during the summer. At the same time, El Niño conditions in the central and eastern equatorial Pacific were strengthening during late spring 2026, a pattern that can increase the likelihood of heavy rainfall across the U.S. Southwest.

The findings were published today in Environmental Research Letters by Kevin Gurney, a professor in NAU’s School of Informatics, Computing, and Cyber Systems (SICCS). The study focused on carbon dioxide (CO₂) emissions from cars and trucks reported in the recently released Climate TRACE database.

Gurney said the results, together with an earlier study that identified similar issues in Climate TRACE estimates for power plants, raise concerns about the reliability of emissions data used to guide climate policy and decision-making.

“Given the importance of vehicle CO₂ emissions in cities, we carefully examined the Climate TRACE data which relied on promising new artificial intelligence-based approaches,” Gurney said. “When combined with our previous study on Climate TRACE power plant CO₂ emissions, our results suggest that the Climate TRACE data significantly underestimate over half of U.S. fossil fuel-based CO₂ emissions in cities.”

Comparing Climate TRACE to the Vulcan Emissions Database

To evaluate the Climate TRACE estimates, Gurney and his colleagues compared them with data from Vulcan, an “onroad” emissions database developed by his laboratory. The Vulcan system is calibrated using official traffic records and energy consumption data, providing an independent benchmark for measuring vehicle emissions.

The researchers compared vehicle CO₂ emissions data from 260 U.S. cities across the two databases.

“While the Vulcan onroad data is not perfect, with uncertainty of about 14%, this is far lower than the differences found when we compared 260 city vehicle CO₂ emissions in the U.S. to the Climate TRACE database,” said Bilal Aslam, a SICCS postdoc and co-investigator on the study. “The Climate TRACE CO₂ emissions were, on average, 70% lower than those same emissions in the Vulcan onroad CO₂ emissions database.”

According to the researchers, the discrepancies were even larger in some locations.

“Individual cities such as Indianapolis and Nashville were lower by more than 90%,” added Pawlok Dass, a research associate in SICCS and contributor to the study.

The authors believe the underestimation may extend beyond the United States and could affect Climate TRACE data globally. They also expressed concerns about other aspects of the database that may warrant additional scrutiny.

Concerns About AI-Based Emissions Tracking

The researchers emphasized that artificial intelligence has enormous potential for monitoring environmental conditions and generating emissions estimates. However, they argue that strong scientific standards remain essential.

In their view, transparency, expert review, and rigorous scientific methods are necessary to ensure that emissions data are accurate and trustworthy. Reliable greenhouse gas measurements are a fundamental part of creating effective climate policies and evaluating progress toward emissions reductions.

The paper also outlines several recommendations intended to strengthen and improve Climate TRACE’s work, helping policymakers and budget planners make better-informed decisions about reducing greenhouse gas emissions.

“We will never estimate emissions with perfect accuracy, but we must ensure that the data shared with policymakers and the public is unbiased and meets best practices and the most rigorous scientific standards available,” Gurney said. “Without this, we mislead decision makers and potentially lose public trust in our ability to tackle climate change.”

Kevin Gurney’s Work on Greenhouse Gas Emissions

Gurney, whose expertise spans atmospheric science, ecology and public policy, has spent more than 20 years developing standardized approaches for measuring greenhouse gas emissions across the United States.

His Vulcan and Hestia projects, supported by multiple federal agencies, quantify and visualize greenhouse gas emissions nationwide, from individual power plants and roadways to neighborhoods. These systems help identify emissions “hotspots” and support more targeted strategies for reducing pollution. Gurney’s emissions estimates have also demonstrated strong agreement with direct atmospheric monitoring measurements.

Over the course of his career, Gurney has authored more than 180 scientific papers that have received more than 20,000 citations. His work includes contributions to a recent U.S. National Academy Report, “Greenhouse Gas Emissions for Decisionmaking.”

He has also participated in the United Nations Climate Change Framework Convention and the Kyoto Protocol process for more than 25 years and serves as a lead author for the Intergovernmental Panel on Climate Change (IPCC).