Ghostly particles called neutrinos pose some of the biggest mysteries of modern physics, but researchers thought they could get a handle on them by corralling them into a laser beam. Two new analyses show that neutrinos are more slippery than that, making the proposed neutrino laser design impossible.

Researchers have been studying neutrinos since the 1940s. They are among the most abundant particles in the universe, but much about them remains unknown. Notably, they are extremely light, mere ghosts compared with more massive particles like neutrons, yet their exact mass is unclear.Ěý

In 2025,  at the University of Manchester in the UK and  at the Massachusetts Institute of Technology (MIT) suggested a novel and surprising way to gain clarity –  using thousands of extremely cold radioactive atoms to create a laser beam of neutrinos.

, also at MIT, heard a lecture about the idea and immediately worried about it being too good to be true. He and his colleagues have now confirmed that hunch with two rigorous mathematical investigations.

Neutrinos are produced when radioactive atoms undergo nuclear decay. Jones and Formaggio theorised that if many such decaying atoms were pushed into a quantum phase of matter called a “Bose-Einstein condensate” (BEC), where they all share a quantum state, then their respective neutrino emissions would be amplified, forming a laser-like beam. Creating such a BEC would require making thousands of radioactive atoms extremely cold so they could behave quantumly, which is a big technical challenge.Ěý

But Ketterle, who received the Nobel prize for creating some of the first ever BECs in the 1990s, and his team uncovered a more fundamental obstacle.

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The key to the neutrino laser proposal was a memory effect: when an atom in the BEC emitted a neutrino, it would be more likely to continue emitting more neutrinos in the same direction, thus pushing them into a beam, because the quantum state that all the ultracold atoms share would retain a trace of that first emission. Ketterle and his colleagues showed that this memory, although present, would be about 10,000 billion times too brief to affect the neutrinos as intended. Even more troublingly, the team uncovered that the memory would actually have the opposite effect from that intended, which Ketterle calls an anti-memory.Ěý

“If I am an atom and I have emitted a neutrino, I am not allowed to [immediately] emit a neutrino again,” he says. This effect’s origin is subtle, stemming from neutrinos being a type of particle called fermions, which fundamentally behave differently than particles of light that our ideas about lasers tend to be built upon.Ěý

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“I think these papers sharpen where the real difficulty lies,” says  at Queen’s University in Canada. “For nuclear-scale energies [pertaining to atoms’ decay], these requirements become extraordinarily demanding.” 

He says the new analysis does not categorically rule out every possible way to build a neutrino laser, but shows that the most conventional scenario where each atom emits one neutrino cannot work. If each atom emitted two neutrinos at a time, the analysis may be different, he says. “I think the interesting scientific question now becomes more precise rather than disappearing: what kinds of nuclear or neutrino processes, if any, avoid the limitations they have identified?” says Leach. In his view, this question is most likely to be definitively answered through experiments.Ěý

Formaggio and Jones didn’t respond Â鶹´«Ă˝â€™s request for comment.

For Ketterle, the lesson of the neutrino laser that couldn’t be is that science can correct itself even when doing so means abandoning inspiring and creative ideas. Getting clarity required hours of discussion, including with Jones and Formaggio. But that is ultimately how physics ought to work, he says.

AI agents devised a way to reduce friction of an airplane wing model after being trained using relatively simple computer simulations. The work demonstrates how AI could help speed up the development of more efficient and sustainable vehicles.Ěý

How we and our machines move is affected by fluids, from air dragging on wind turbine blades to blood flowing through our veins. But calculating what a fluid will do under specific circumstances is very difficult, even with supercomputers.Ěý

“Simulating fluids usually involves millions or billions of coupled differential equations, and even with Moore’s law, with the fastest computers in the world, we’re maybe 100 years away from simulating the flows we actually care about at engineering scales,” says  at the University of Washington.Ěý

He and his colleagues have discovered that AI might offer a shortcut, because it can devise ways to control fluid flow in complex situations based on relatively simple computer simulations, substituting an AI training period for difficult-to-run computations.

They created a platform, HydroGym, in which many AI agents could tweak how a fluid flowed over virtual objects – for instance, by adding actuators that inject fluid or changing the object’s motion – to decrease the drag they experienced against virtual fluids. The virtual objects, and the behaviour of the fluids, could be simulated with today’s computers but varied in levels of complexity.

The AI agents tackled the fluid control task by using a trial-and-error approach known as reinforcement learning. They could also coordinate with each other to achieve the best overall performance, a strategy which researchers had not tried for fluids problems on this scale before, says team member  at RWTH Aachen University in Germany.

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The team discovered that the AI agents could apply lessons learned from experimenting on more simple, textbook examples in a computer simulation to work out how virtual objects would behave in more complex scenarios – even without access to a computer simulation of those complex scenarios.Ěý

For instance, after working out how to control flow of a turbulent fluid in a flat channel, the agents successfully took on the task of controlling fluid surrounding a curved, three-dimensional airplane wing model, ultimately managing to decrease the energetically wasteful friction between the wing and the fluid by 38 per cent.Ěý

“The AI wasn’t just memorising one flow configuration. It is picking up something genuinely general about how fluids behave, not just fitting to the one setup it was trained on,” says at the University of Michigan, who was part of the team.

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This transfer of principles from a simple to a more complex case suggests that the AI agents could help us tackle ever-bigger and more intricate fluid flow scenarios, without requiring those scenarios to be fully simulated on a computer first. It may eventually be possible to explore fluid flow scenarios that are currently too challenging to simulate.Ěý

The researchers also hope HydroGym will provide computational infrastructure for AI to become a well-tested tool across all areas of science and engineering that deal with fluids, similar to how AlphaFold is used across studies of proteins, says Vinuesa.Ěý

“If we took something like global shipping, if you could reduce the drag by one percentage point, that would result in probably billions of dollars of fuel saving and an enormous amount of reduction in greenhouse gas emissions,” says Brunton. “The financial and the ecological impact is profound for the tiniest improvements.”