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Astronomers using the European Space Agency’s Cheops mission mission have caught an exoplanet that seems to be triggering flares of radiation from the star it orbits. These tremendous explosions are blasting away the planet’s wispy atmosphere, causing it to shrink every year.

This is the first-ever evidence for a ‘planet with a death wish’. Though it was theorised to be possible since the nineties, the flares seen in this research are around 100 times more energetic than expected.

This planet’s star makes our Sun look sleepy

Thanks to telescopes like the NASA/ESA/CSA James Webb Space Telescope and NASA’s Transiting Exoplanet Survey Satellite ( TESS ), we already had some clues about this planet and the star it orbits.

The star, named HIP 67522, was known to be just slightly larger and cooler than our own host star, the Sun. But whilst the Sun is a middle-aged 4.5-billion-year-old, HIP 67522 is a fresh-faced 17-million-year-old. It bears two planets. The closer of the two – given the catchy name HIP 67522 b – takes just seven days to whip around its host star.

Because of its youth and size, scientists suspected that star HIP 67522 would churn and spin with lots of energy. This churning and spinning would turn the star into a powerful magnet.

Our much-older Sun has its own smaller and more peaceful magnetic field. From studying the Sun, we already knew that flares of energy can burst from magnetic stars when ‘twisted’ magnetic field lines are suddenly released. This energy can take the form of anything from gentle radio waves to visible light to aggressive gamma rays.

A la carte research with Cheops

Ever since the first exoplanet was discovered in the 1990s, astronomers have pondered whether some of them might be orbiting close enough to disturb their host stars’ magnetic fields. If so, they could be triggering flares.

A team led by Ekaterina Ilin at the Netherlands Institute for Radio Astronomy ( ASTRON ) figured that with our current space telescopes, it was time to investigate this question further.

“We hadn’t seen any systems like HIP 67522 before; when the planet was found it was the youngest planet known to be orbiting its host star in less than 10 days,” says Ekaterina.

The team was using TESS to do a broad sweep of stars that might be flaring because of an interaction with their planets. When TESS turned its eyes to HIP 67522, the team thought they could be on to something. To be sure, they called upon ESA’s sensitive CHaracterising ExOPlanet Satellite, Cheops .

“We quickly requested observing time with Cheops, which can target individual stars on demand, ultra precisely,” says Ekaterina. “With Cheops we saw more flares, taking the total count to 15, almost all coming in our direction as the planet transited in front of the star as seen from Earth.”

Because we are seeing the flares as the planet passes in front of the star, it is very likely that they are being triggered by the planet.

A flaring star is nothing new. Our own Sun regularly releases bursts of energy, which we experience on Earth as ‘ space weather ‘ that causes the auroras and can damage technology. But we’ve only ever seen this energy exchange as a one-way street from star to planet.

Knowing that HIP 67522 b orbits extremely close to its host star, and assuming that the star’s magnetic field is strong, Ekaterina’s team deduced that the clingy HIP 67522 b sits close enough to exert its own magnetic influence on its host star.

They think that the planet gathers energy as it orbits, then redirects that energy as waves along the star’s magnetic field lines, as if whipping a rope. When the wave meets the end of the magnetic field line at the star’s surface, it triggers a massive flare.

It’s the first time we see a planet influencing its host star, overturning our previous assumption that stars behave independently.

And not only is HIP 67522 b triggering flares, but it is also triggering them in its own direction. As a result, the planet experiences six times more radiation than it otherwise would.

A self-imposed downfall

Unsurprisingly, being bombarded with so much high-energy radiation does not bode well for HIP 67522 b. The planet is similar in size to Jupiter but has the density of candy floss, making it one of the wispiest exoplanets ever found.

Over time, the radiation is eroding away the planet’s feathery atmosphere, meaning it is losing mass much faster than expected. In the next 100 million years, it could go from an almost Jupiter-sized planet to a much smaller Neptune-sized planet.

“The planet seems to be triggering particularly energetic flares,” points out Ekaterina. “The waves it sends along the star’s magnetic field lines kick off flares at specific moments. But the energy of the flares is much higher than the energy of the waves. We think that the waves are setting off explosions that are waiting to happen.”

More questions than answers

When HIP 67522 was found, it was the youngest known planet orbiting so close to its host star. Since then, astronomers have spotted a couple of similar systems and there are probably dozens more in the nearby Universe. Ekaterina and her team are keen to take a closer look at these unique systems with TESS, Cheops and other exoplanet missions.

“I have a million questions because this is a completely new phenomenon, so the details are still not clear,” she says.

“There are two things that I think are most important to do now. The first is to follow up in different wavelengths (Cheops covers visible to near-infrared wavelengths) to find out what kind of energy is being released in these flares – for example ultraviolet and X-rays are especially bad news for the exoplanet.

“The second is to find and study other similar star-planet systems; by moving from a single case to a group of 10–100 systems, theoretical astronomers will have something to work with.”

Maximillian Günther, Cheops project scientist at ESA, is excited to see the mission contributing to research in a way that he never thought possible: “Cheops was designed to characterise the sizes and atmospheres of exoplanets, not to look for flares. It’s really beautiful to see the mission contributing to this and other results that go so far beyond what it was envisioned to do.”

Looking further ahead, ESA’s future exoplanet hunter Plato will also study Sun-like stars like HIP 67522. Plato will be able to capture much smaller flares to really give us the detail that we need to better understand what is going on.

UCLA researchers have made a significant discovery showing that biological brains and artificial intelligence systems develop remarkably similar neural patterns during social interaction. This first-of-its-kind study reveals that when mice interact socially, specific brain cell types synchronize in “shared neural spaces,” and AI agents develop analogous patterns when engaging in social behaviors. The study appears in the journal Nature.

Why it matters

This new research represents a striking convergence of neuroscience and artificial intelligence, two of today’s most rapidly advancing fields. By directly comparing how biological brains and AI systems process social information, scientists reveal fundamental principles that govern social cognition across different types of intelligent systems. The findings could advance understanding of social disorders like autism, while simultaneously informing the development of socially-aware AI systems. This comes at a critical time when AI systems are increasingly integrated into social contexts, making understanding of social neural dynamics essential for both scientific and technological progress.

What the study did

A multidisciplinary team from UCLA’s departments of Neurobiology, Biological Chemistry, Bioengineering, Electrical and Computer Engineering, and Computer Science across the David Geffen School of Medicine and the Henry Samueli School of Engineering used advanced brain imaging techniques to record activity from molecularly defined neurons in the dorsomedial prefrontal cortex of mice during social interactions. Mice serve as an important model for understanding mammalian brain function because they share fundamental neural mechanisms with humans, particularly in brain regions involved in social behavior. The researchers developed a novel computational framework to identify high-dimensional “shared” and “unique” neural subspaces across interacting individuals. The team then trained artificial intelligence agents to interact socially and applied the same analytical framework to examine neural network patterns in AI systems that emerged during social versus non-social tasks.

What they found

The research revealed striking parallels between biological and artificial systems during social interaction. In both mice and AI systems, neural activity could be partitioned into two distinct components: a “shared neural subspace” containing synchronized patterns between interacting entities, and a “unique neural subspace” containing activity specific to each individual.

Remarkably, GABAergic neurons—inhibitory brain cells that regulate neural activity—showed significantly larger shared neural spaces compared to glutamatergic neurons, the brain’s primary excitatory cells. This represents the first investigation of inter-brain neural dynamics in molecularly defined cell types, revealing previously unknown differences in how specific neuron types contribute to social synchronization.

When the same framework was applied to AI agents, shared neural dynamics also emerged as artificial systems developed social interaction capabilities. Most importantly, when researchers selectively disrupted these shared neural components in artificial systems, social behaviors were substantially reduced, providing the direct evidence that synchronized neural patterns causally drive social interactions.

The study also revealed that shared neural dynamics don’t simply reflect coordinated behaviors between individuals, but emerge from representations of each other’s unique behavioral actions during social interaction.

What’s next

The research team plans to further investigate shared neural dynamics in different and potentially more complex social interactions. They also aim to explore how disruptions in shared neural space might contribute to social disorders and whether therapeutic interventions could restore healthy patterns of inter-brain synchronization. The artificial intelligence framework may serve as a platform for testing hypotheses about social neural mechanisms that are difficult to examine directly in biological systems. They also aim to develop methods to train socially intelligent AI.

From the experts

“This discovery fundamentally changes how we think about social behavior across all intelligent systems,” said Weizhe Hong, Ph.D., professor of Neurobiology, Biological Chemistry, and Bioengineering at UCLA and lead author of the new work. “We’ve shown for the first time that the neural mechanisms driving social interaction are remarkably similar between biological brains and artificial intelligence systems. This suggests we’ve identified a fundamental principle of how any intelligent system—whether biological or artificial—processes social information. The implications are significant for both understanding human social disorders and developing AI that can truly understand and engage in social interactions.”

About the study

Inter-brain neural dynamics in biological and artificial intelligence systems, Nature 2025; DOI: 10.1038/s41586-025-09196-4.

About the Research Team

The study was led by Weizhe Hong and Jonathan C. Kao at UCLA. Co-first authors Xingjian Zhang and Nguyen Phi, along with collaborators Qin Li, Ryan Gorzek, Niklas Zwingenberger, Shan Huang, John L. Zhou, Lyle Kingsbury, Tara Raam, Ye Emily Wu, and Don Wei contributed to the research. The interdisciplinary team includes researchers from UCLA’s Department of Neurobiology, Department of Biological Chemistry, Department of Bioengineering, Department of Electrical and Computer Engineering, and Department of Computer Science. This work was supported in part by the NIH, NSF, Packard Foundation, Vallee Foundation, Mallinckrodt Foundation, and Brain and Behavior Research Foundation.

What is the significance of being able to identify and catalog a gravitational wave network? How would it benefit society?

Chiara Mingarelli: Right now, we are combining traditional astronomy — which looks at the universe with radio waves, x-rays, optical waves, and more — with gravitational wave astronomy. It’s like we’ve discovered the fact that we have ears and can now hear the universe instead of just looking at it. Gravitational waves come straight from the source — merging supermassive black hole binaries — and aren’t affected by gas and dust on their way to the Earth. This makes them exceptionally clean probes of extreme physics, not accessible by any other means.

It’s exciting to think about how this work will eventually benefit society. Einstein’s General Relativity gave us GPS [global positioning satellites] about 100 years later, and lasers, MRI, and Wi-Fi all came from scientists asking “why” before anyone knew “what for.” Plus, imagine 100 years ago telling people about GPS — could they even imagine what we mean? I can’t wait to see what the eventual applications of this work will be.

How does your new study fit into NANOGrav’s overall work?

Mingarelli: We believe there is a gravitational wave background that is composed of millions of slowly merging pairs of supermassive black holes. In 2023 we found evidence of this, and the next big thing is the detection of individual black hole pairs.

In this paper, we predict that quasars are up to five times more likely than any other type of galaxy to host these pairs of black holes. We conclude that quasars should be the number one targets to search for pairs of merging supermassive black holes.

Furthermore, my team of Yale graduate and undergraduate students and I are currently using the results of this paper to identify target galaxies that host supermassive black hole binaries. A paper with those results will likely come out by the end of the summer.

What other aspects of the current study stand out?

Mingarelli: This is the first study to statistically constrain the supermassive black hole binary population by combining the gravitational wave background measurement with quasar variability. It represents a novel approach to characterizing the binary population.

If confirmed, even a small population of binary quasars could anchor our model of gravitational wave sources at low frequencies and pave the way for direct detections.

Your approach hinges on the use of pulsar timing arrays. What makes pulsars an advantageous tool for locating black hole mergers?

Mingarelli: Pulsar timing arrays monitor ultra-stable stars called pulsars, which emit signals that are, in effect, excellent clocks.

Gravitational waves stretch and squeeze the fabric of space and time itself. When space/time is squeezed, pulsar pulses arrive early. When space/time is stretched, the pulses arrive late. The overall stretch and squash is about 1 part in a million billion — or the size of a virus divided by the diameter of the Earth. Very small!

The fact that we can monitor these pulsars for years to decades, with amazing accuracy — within 100 nanoseconds — means that we can detect gravitational waves with intervals of years-to-decades. It makes pulsar arrays the perfect instruments to detect gravitational waves from supermassive black holes, since they create gravitational waves with such long periods.

But without a list of targets, we can’t localize a pair of merging supermassive black holes to anything smaller than an error box big enough to contain thousands of galaxies.

Beyond establishing the gravitational wave background network, what can we learn from the new study?

Mingarelli: Constraining the demographics of supermassive black hole binaries is central to our understanding of galaxy evolution, black hole growth, as well as the gravitational wave background. This work provides a data-driven framework for identifying the host galaxies of supermassive black hole pairs and lays the foundations for future searches.