An unconventional approach to solving the dark energy problem called the cosmologically coupled black hole (CCBH) hypothesis appears to be compatible with the observed masses of neutrinos. This new finding from researchers working at the DESI collaboration suggests that black holes may represent little Big Bangs played in reverse and could be used as a laboratory to study the birth and infancy of our universe. The study also confirms that the strength of dark energy has increased along with the formation rate of stars.

The Dark Energy Spectroscopic Instrument (DESI) is located on the Nicholas U Mayall four-metre Telescope at Kitt Peak National Observatory in Arizona. Its raison d’être is to shed more light on the “dark universe” – the 95% of the mass and energy in the universe that we know very little about. Dark energy is a hypothetical entity invoked to explain why the rate of expansion of the universe is (mysteriously) increasing – something that was discovered at the end of the last century.

According to standard theories of cosmology, matter is thought to comprise cold dark matter (CDM) and normal matter (mostly baryons and neutrinos). DESI can observe fluctuations in the matter density of the universe known as baryonic acoustic oscillations (BAOs), which are density fluctuations that were created after the Big Bang in the hot plasma of baryons and electrons that prevailed then. BAOs expand with the growth of the universe and represent a sort of “standard ruler” that allows cosmologists to map the universe’s expansion by statistically analysing the distance that separates pairs of galaxies and quasars.

Largest 3D map

DESI has produced the largest such 3D map of the universe ever and it recently published the first set of BAO measurements determined from observations of over 14 million extragalactic targets going back 11 billion years in time.

In the new study, the DESI researchers combined measurements from these new data with cosmic microwave background (CMB) datasets (which measure the density of dark matter and baryons from a time when the universe was less than 400,000 years old) to search for evidence of matter converting into dark energy. They did this by focusing on a new hypothesis known as the cosmologically coupled black hole (CCBH), which was put forward five years ago by DESI team member Kevin Croker, who works at Arizona State University (ASU), and his colleague Duncan Farrah at the University of Hawaii. This physical model builds on a mathematical description of black holes as bubbles of dark energy in space that was introduced over 50 years ago. CCBH describes a scenario in which massive stars exhaust their nuclear fuel and collapse to produce black holes filled with dark energy that then grows as the universe expands. The rate of dark energy production is therefore determined by the rate at which stars form.

Neutrino contribution

Previous analyses by DESI scientists suggested that there is less matter in the universe today compared to when it was much younger. When they then added the additional, known, matter source from neutrinos, there appeared to be no “room” and the masses of these particles therefore appeared negative in their calculations. Not only is this unphysical, explains team member Rogier Windhorst of the ASU’s School of Earth and Space Exploration, it also goes against experimental measurements made so far on neutrinos that give them a greater-than-zero mass.

When the researchers re-interpreted the new set of data with the CCBH model, they were able to resolve this issue. Since stars are made of baryons and black holes convert exhausted matter from stars into dark energy, the number of baryons today has decreased in comparison to the CMB measurements. This means that neutrinos can indeed contribute to the universe’s mass, slowing down the expansion of the universe as the dark energy produced sped it up.

“The new data are the most precise measurements of the rate of expansion of the universe going back more than 10 billion years,” says team member Gregory Tarlé at the University of Michigan, “and it results from the hard work of the entire DESI collaboration over more than a decade. We undertook this new study to confront the CCBH hypothesis with these data.”

Black holes as a laboratory

“We found that the standard assumptions currently employed for cosmological analyses simply did not work and we had to carefully revisit and rewrite massive amounts of a lot of cosmological computer code,” adds Croker.

“If dark energy is being sourced by black holes, these structures may be used as a laboratory to study the birth and infancy of our own universe,” he tells Physics World. “The formation of black holes may represent little Big Bangs played in reverse, and to make a biological analogy, they may be the ‘offspring’ of our universe.”

The researchers say they studied the CCBH scenario in its simplest form in this work, and found that it performs very well. “The next big observational test will involve a new layer of complexity, where consistency with the large-scale features of the Big Bang relic radiation, or CMB, and the statistical properties of the distribution of galaxies in space will make or break the model,” says Tarlé.

The research is described in Physical Review Letters.

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Can quantum mechanics fully describe macroscopic reality? Everyday objects are typically well-described by classical mechanics, whereas atomic-scale objects are governed by quantum mechanics. Exploring the boundary between the two domains could enable fundamental tests of quantum mechanics and the development of new sensing technologies for gravitational measurements.

Now, a team of researchers at Switzerland’s ETH Zürich and Spain’s Institute of Photonic Sciences in Barcelona has taken an important step towards bridging the two regimes by extending the quantum wave nature of nanoparticles — objects a thousand times larger than atoms.

Quantum mechanics posits that even large objects behave as waves. However, the spatial extent of this wave-like behaviour, known as the “coherence length”, is far smaller than the size of large objects. This renders quantum phenomena effectively unobservable for such systems. “To push quantum physics into the macroscopic domain, we need to increase both [mass and coherence length] simultaneously”, explains lead researcher Massimiliano Rossi. This pursuit motivated the team’s recent study, which is described in Physical Review Letters.

Playing with light

The researchers studied large objects called silica nanoparticles, which are 100 nm in diameter. The nanoparticles were held and levitated in vacuum using a tightly-focused laser beam.

Nanoparticles naturally scatter the laser light, and the phase of the scattered photons encodes information about the nanoparticle’s centre-of-mass position. The researchers used this information in a feedback loop, applying electric fields to cool the nanoparticles close to their quantum ground state. The colder sample is in a more “pure” quantum state, such that the quantum wave-like behaviour extends farther in space and the coherence length is longer than in a hot sample. The team measured an initial coherence length of 21 pm (21 × 10^-12  m).

Further extending the coherence length required careful manipulation of the laser light. The researchers started with high-power light, which provided a tight harmonic potential for the nanoparticles – like a marble trapped at the bottom of a steep bowl. An advantage of using a light-induced potential is that the curvature of the bowl is easily tuned over a large range by adjusting the laser power.

The researchers lowered the laser power in two pulses, each of which caused the bowl to become shallower, therefore allowing the marble to roll around and explore more of the bowl. In the experiment, this translated to an expansion of the nanoparticle’s coherence length to 73 pm, more than three-fold that of the initial value.

Preserving quantum information

Rossi notes that the main experimental challenge was limiting decoherence, a process that destroys quantum information. He explains that when a nanoparticle interacts with its surroundings, it becomes correlated with a noisy and unmeasurably complex environment. This interaction causes the nanoparticle’s motion to become increasingly random when measured. As a result, the nanoparticle’s quantum mechanical behaviour is washed out and the particle is well described as a classical ball.

It was therefore critical that the researchers expand the coherence length faster than the rate of any decoherence. To achieve this, they meticulously measured, identified, and suppressed all sources of decoherence, with the dominant source being laser light scattering. Scattering was reduced during the expansion pulses because of the lower laser power.

The achieved 73 pm remains orders of magnitude smaller than the size of the nanoparticle, which was 100 nm in diameter. However, Rossi remarks that “we do not know of any fundamental reason why achieving nanometre coherence lengths should be impossible.” One next step could be to use more expansion pulses to increase the coherence length further.

With a longer expansion time, the main challenge would be to outpace decoherence. Researchers propose using hybrid traps that employ both light and electric fields to confine the nanoparticles, since an electric trap would reduce the decoherence from light scattering. Rossi is now pursuing this direction in his new research group at the Delft University of Technology in the Netherlands.

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Adding energy to a system usually heats it up, but physicists at the University of Innsbruck in Austria have now discovered a scenario in which this is not the case. Their new platform – a one-dimensional fluid of strongly interacting atoms cooled to just a few nanokelvin above absolute zero and periodically “kicked” using an external force – could be used to study how objects transition from being quantum and ordered to classical and chaotic.

Our everyday world is chaotic and chaos plays a crucial and often useful role in many areas of science – from nonlinear complex systems in mathematics, physics and biology to ecology, meteorology and economics. How a system evolves depends on its initial conditions, but this evolution is, by nature, inherently unpredictable.

While we know how chaos emerges in classical systems, how it does so in quantum materials is still little understood. When this happens, the quantum system reverts to being a classical one.

The quantum kicked rotor

Researchers have traditionally studied chaotic behaviour in driven systems – that is, rotating objects periodically kicked by an external force. The quantum version of these is the quantum kicked rotor (QKR). Here, quantum coherence effects can prevent the system from absorbing external energy, meaning that, in contrast to its classical counterpart, it doesn’t heat up – even if a lot of energy is applied. This “dynamical localization” effect has already been seen in dilute ultracold atomic gases.

The QKR is a highly idealized single-particle model system, explains study lead Hanns-Christoph Nägerl. However, real-world systems contain many particles that interact with each other – something that can destroy dynamical localization. Recent theoretical work has suggested that this localization may persist in some types of interacting, even strongly interacting, many-body quantum systems – for example, in 1D bosonic gases.

In the new work, Nägerl and colleagues made a QKR by subjecting samples of ultracold caesium (Cs) atoms to periodic kicks by means of a “flashed-on lattice potential”. They did this by loading a Bose-Einstein condensate of these atoms into an array of narrow 1D tubes created by a 2D optical lattice formed by laser beams propagating in the x–y plane at right angles to each other. They then increased the power of the beams to heat up the Cs atoms.

Many-body dynamical localization

The researchers expected the atoms to collectively absorb energy over the course of the experiment. Instead, when they recorded how their momentum distribution evolved, they found that it actually stopped spreading and that the system’s energy reached a plateau. “Despite being continually kicked and strongly interacting, it no longer absorbed energy,” says Nägerl. “We say that it had localized in momentum space – a phenomenon known as many-body dynamical localization (MBDL).”

In this state, quantum coherence and many-body interactions prevent the system from heating up, he adds. “The momentum distribution essentially freezes and retains whatever structure it has.”

Nägerl and colleagues repeated the experiment by varying the interaction between the atoms – from zero (non-interacting) to strongly interacting. They found that the system always localizes.

Quantum coherence is crucial for preventing thermalization

“We had already found localization for our interacting QKR in earlier work and set out to reproduce these results in this new study,” Nägerl tells Physics World. “We had not previously realised the significance of our findings and thought that perhaps we were doing something wrong, which turned out not to be the case.”

The MBDL is fragile, however – something the researchers proved by introducing randomness into the laser pulses. A small amount of disorder is enough the destroy the localization effect and restore diffusion, explains Nägerl: the momentum distribution smears out and the kinetic energy of the system rises sharply, meaning that it is absorbing energy.

“This test highlights that quantum coherence is crucial for preventing thermalization in such driven many-body systems,” he says.

Simulating such a system on classical computers is only possible for two or three particles, but the one studied in this work, reported in Science, contains 20 or more. “Our new experiments now provide precious data to which we can compare the QKR model system, which is a paradigmatic one in quantum physics,” adds Nägerl.

Looking ahead, the researchers say they would now like to find out how stable MBDL is to various external perturbations. “In our present work, we report on MBDL in 1D, but would it happen in a 2D or a 3D system?” asks Nägerl. “I would like to do an experiment in which we have a 1D + 1D situation, that is, where the 1D is allowed to communicate with just one neighbouring 1D system (via tunnelling; by lowering the barrier to this system in a controlled way).”

Another way of perturbing the system would be to add a local defect – for example a bump in the potential of a different atom, he says. “Generally speaking, we would like to measure the ‘phase diagram’ for MBDL, where the axes of the graph would quantify the strength of the various perturbations we apply.”

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Photographers Weitang Liang, Qi Yang and Chuhong Yu have beaten thousands of amateur and professional photographers from around the world to bag the 2025 Royal Observatory Greenwich’s ZWO Astronomy Photographer of the Year 17.

The image – The Andromeda Core – showcases the core of the Andromeda Galaxy (M31) in exceptional detail, revealing the intricate structure of the galaxy’s central region and its surrounding stellar population.

The image was taken with a long focal-length telescope from the AstroCamp Observatory, Nerpio, Spain.

“Not to show it all − this is one of the greatest virtues of this photo. The Andromeda Galaxy has been photographed in so many different ways and so many times with telescopes that it is hard to imagine a new photo would ever add to what we’ve already seen,” notes astrophotographer László Francsics who was a judge for this year’s competition. “But this does just that, an unusual dynamic composition with unprecedented detail that doesn’t obscure the overall scene.”

As well as winning the £10,000 top prize, the image has gone on display along with other selected pictures from the competition at an exhibition at the National Maritime Museum observatory that opened on 12 September.

The award – now in its 17th year – is run by the Royal Observatory Greenwich in association with the astrophotography firm ZWO and BBC Sky at Night Magazine.

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A new optically addressable quantum bit (qubit) encoded in a fluorescent protein could be used as a sensor that can be directly produced inside living cells. The device opens up a new era for fluorescence microscopy to monitor biological processes, say the researchers at the University of Chicago Pritzker School of Molecular Engineering who designed the novel qubit.

Quantum technologies use qubits to store and process information. Unlike classical bits, which can exist in only two states, qubits can exist in a superposition of both these states. This means that computers employing these qubits can simultaneously process multiple streams of information, allowing them to solve problems that would take classical computers years to process.

Qubits can be manipulated and measured with high precision, and in quantum sensing applications they act as nanoscale probes whose quantum state can be initialized, coherently controlled and read out. This allows them to detect minute changes in their environment with exquisite sensitivity.

Optically addressable qubit sensors – that is, those that are read out using light pulses from a laser or other light source – are able to measure nanoscale magnetic fields, electric fields and temperature. Such devices are now routinely employed by researchers working in the physical sciences. However, their use in the life sciences is lagging behind, with most applications still at the proof-of-concept stage.

Difficult to position inside living cells

Many of today’s quantum sensors are based on nitrogen-vacancy (NV) centres, which are crystallographic defects in diamond. These centres occur when two neighbouring carbon atoms in diamond are replaced by a nitrogen atom and an empty lattice site and they act like tiny quantum magnets with different spins. When excited with laser pulses, the fluorescent signal that they emit can be used to monitor slight changes in the magnetic properties of a nearby sample of material. This is because the intensity of the emitted NV centre signal changes with the local magnetic field.

“The problem is that such sensors are difficult to position at well-defined sites inside living cells,” explains Peter Maurer, who co-led this new study together with David Awschalom. “And the fact that they are typically ten times larger than most proteins further restricts their applicability,” he adds.

“So, rather than taking a conventional quantum sensor and trying to camouflage it to enter a biological system, we therefore wanted to explore the idea of using a biological system itself and developing it into a qubit,” says Awschalom.

Fluorescent proteins, which are just 3 nm in diameter, could come into their own here as they can be genetically encoded, allowing cells to produce these sensors directly at the desired location with atomic precision. Indeed, fluorescent proteins have become the “gold standard” in cell biology thanks to this unique ability, says Maurer. And decades of biochemistry research has allowed researchers to generate a vast library of such fluorescent proteins that can be tagged to thousands of different types of biological targets.

“We recognized that these proteins possess optical and spin properties that are strikingly similar to those of qubits formed by crystallographic defects in diamond – namely that they have a metastable triplet state,” explain Awschalom and Maurer. “Building on this insight, we combined techniques from fluorescence microscopy with methods of quantum control to encode and manipulate protein-based qubits.”

In their work, which is detailed in Nature, the researchers used a near-infrared laser pulse to optically address a yellow fluorescent protein known as EYFP and read out its triplet spin state with up to 20% “spin contrast” – measured using optically detected magnetic resonance (ODMR) spectroscopy.

To test the technique, the team genetically modified the protein so that it was expressed in human embryonic kidney cells and Escherichia coli (E. coli) cells. The measured OMDR signals exhibited a contrast of up to 8%. While this performance is not as good as that of NV quantum sensors, the fluorescent proteins open the door to magnetic resonance measurements directly inside living cells – something that NV centres cannot do, says Maurer. “They could thus transform medical and biochemical studies by probing protein folding, monitoring redox states or detecting drug binding at the molecular scale,” he tells Physics World.

“A new dimension for fluorescence microscopy”

Beyond sensing, the unique quantum resonance “signatures” offer a new dimension for fluorescence microscopy, paving the way for highly multiplexed imaging far beyond today’s colour palette, Awschalom adds. Looking further ahead, using arrays of such protein qubits could even allow researchers to explore many-body quantum effects within biologically assembled structures.

Maurer, Awschalom and colleagues say they are now busy trying to improve the stability and sensitivity of their protein-based qubits through protein engineering via “directed evolution” – similar to the way that fluorescent proteins were optimized for microscopy.

“Another goal is to achieve single-molecule detection, enabling readout of the quantum state of individual protein qubits inside cells,” they reveal. “We also aim to expand the palette of available qubits by exploring new fluorescent proteins with improved spin properties and to develop sensing protocols capable of detecting nuclear magnetic resonance signals from nearby biomolecules, potentially revealing structural changes and biochemical modifications at the nanoscale.”

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It is Peer Review Week and the theme for 2025 is “Rethinking Peer Review in the AI Era”. This is not surprising given the rapid rise in the use and capabilities of artificial intelligence. However, views on AI are deeply polarized for reasons that span its legality, efficacy and even its morality.

A recent survey done by IOP Publishing – the scientific publisher that brings you Physics World – reveals that physicists who do peer review are polarized regarding whether AI should be used in the process.

IOPP’s Laura Feetham-Walker is lead author of AI and Peer Review 2025, which describes the survey and analyses its results. She joins me in this episode of the Physics World Weekly podcast in a conversation that explores reviewers’ perceptions of AI and their views of how it should, or shouldn’t, be used in peer review.

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“Imagine the day the aliens arrive.” So begins Do Aliens Speak Physics? by the US particle physicist Daniel Whiteson and the cartoonist and author Andy Warner. From that starting point, if you believe the plots of many works of science fiction, it wouldn’t be long before we’re communicating with emissaries of an extraterrestrial civilization. Quickly, we’d be marvelling at their advanced science and technology.

But is this a reasonable assumption? Would we really be able to communicate with aliens? Even if we could, would their way of doing science have any meaning to us? What if an advanced alien civilization had no science at all? These are some of the questions tackled by Whiteson and Warner in their entertaining and thought-provoking book.

While Do Aliens Speak Physics? focuses on the possible differences between human and alien science, it made me think about what science means to humans – and the role of science in our civilization. Indeed, when I spoke to Whiteson for a future episode of the Physics World Weekly podcast, he told me that his original plan for the book was to examine if physics is universal or shaped by human perspective.

But when he pitched the idea to his teenage son, Whiteson realized that approach was a bit boring and decided to spice things up using an alien landing. At the heart of the book is a new equation for estimating the number of alien civilizations that scientists could potentially communicate with – ideally, when the aliens arrive on Earth.

The authors aren’t the first people to do such a calculation. In 1961 the US astrophysicist Frank Drake famously did so by estimating how many habitable planets might exist and whether they could harbour life that’s evolved so far that it could communicate with us. Whiteson and Warner’s “extended Drake equation” adds four extra terms related to alien science.

The first is the probability that a civilization has developed science. The second is the likelihood that we would be able to communicate with the civilization, with the third being the probability that an alien civilization would ask scientific questions that are meaningful to us. The final term is whether human science would benefit from the answers to those questions.

One of Whiteson and Warner’s more interesting ideas is that aliens could perceive science and technology in very different ways to us. After all, an alien civilization could be completely focused on developing technology and not be at all interested in the underlying science. Technology without science might seem deeply foreign to us today, but for most of history humans have focused on how things work – not why.

Blacksmiths of the past, for example, developed impressive swords and other metal implements without any understanding of how the materials they worked with behaved at a microscopic level. So perhaps our alien visitors will come from a planet of blacksmiths rather than materials scientists.

Mind you, communicating with alien scientists could be a massive challenge given that we do so mainly using sound and visual symbols, whereas an alien might use smells or subatomic particles to get their point across. As the authors point out, it’s difficult even translating the Danish/Norwegian word hygge into English, despite the concept’s apparent popularity in the English-speaking world. Imagine how much harder things would be if we used a different form of communication altogether.

But could physics function as a kind of Rosetta Stone, offering a universal way of translating one language into another? We could then get the aliens to explain various physical processes – such as how a mass falls under the influence of gravity – and compare their reasoning to our understanding of the same phenomena.

Of course, an alien scientist’s questions might depend on how they perceive the universe. In a chapter titled “Can aliens taste electrons?”, the authors explore what might happen if aliens were so small that they experience quantum effects such as entanglement in their daily lives. What if an organism were so big that it feels the gravitational tug of dark matter? Or what if an intelligent alien could exist in an ultracold environment where everything moves so slowly that their perception of physics is completely different to ours?

The final term in the authors’ extended Drake equation looks at whether the answers to the questions of alien physics would be meaningful to humans. We naturally assume there are deep truths about nature that can be explored using experimental and mathematical tools. But what if there are no deep truths out there – and what if our alien friends are already aware of that fact?

When Drake proposed his equation, humans did not know of any planets beyond the solar system. Today, however, we have discovered nearly 6000 such exoplanets, and it is possible that there are billions of habitable, Earth-like exoplanets in the Milky Way. So it does not seem at all fanciful that we could soon be communicating with an alien civilization.

But when I asked Whiteson if he’s worried that visiting aliens could be hostile towards humans, he said he hoped for a “peaceful” visit. In fact, Whiteson is unable to think of a good reason why an advanced civilization would be hostile to Earth – pointing out that there is probably nothing of material value here for them. Fingers crossed, any visit will be driven by curiosity, peace and goodwill.

• 4 November 2025 WW Norton & Company 272pp £23.00 hb; £21.84 ebook

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What role does the Hartree Centre play in quantum computing?

The Hartree Centre gives industry fast-track access to next-generation supercomputing, AI and digital capabilities. We are a “connector” when it comes to quantum computing, helping UK businesses and public-sector organizations to de-risk the early-stage adoption of a technology that is not yet ready to buy off-the-shelf. Our remit spans quantum software, theoretical studies and, ultimately, the integration of quantum computing into existing high-performance computing (HPC) infrastructure and workflows.

What does industry need when it comes to quantum computing?

It’s evident that industry wants to understand the commercial upsides of quantum computing, but doesn’t yet have the necessary domain knowledge and skill sets to take full advantage of the opportunities. By working with the STFC Hartree Centre, businesses can help their computing and R&D teams to bridge that quantum knowledge gap.   

How does the interaction with industry partners work?

The Hartree Centre’s quantum computing effort is built around a cross-disciplinary team of scientists and a mix of expertise spanning physics, chemistry, mathematics, computer science and quantum information science. We offer specialist quantum consultancy to clients across industries as diverse as energy, pharmaceuticals and food manufacturing.

How does that work in practice?

We begin by doing the due diligence on the client’s computing challenge, understanding the computational bottlenecks and, where appropriate, translating the research problem so that it can be executed, in whole or in part, on a quantum computer or a mixture of hybrid and quantum computing resources.

What are the operational priorities for the Hartree Centre in quantum computing?

Integrating classical HPC and quantum computing is a complex challenge along three main pathways: infrastructure – bridging fundamentally different hardware architectures; software – workflow management, resource scheduling and organization; and finally applications – adapting and optimizing computing workflows across quantum and classical domains. All of these competencies are mandatory for successful exploitation of quantum computing systems.

So it’s likely these pathways will converge?

Correct. Ultimately, the task is how do we distribute a workload to run on an HPC platform, also on a quantum computer, when many of the algorithms and data streams must loop back and forth between the two systems.

How do you link up classical computing and quantum resources?

We have been addressing this problem with our quantum technology partners – IBM and Pasqal – and a team at Rensselaer Polytechnic in New York. Together, we have introduced a Quantum Resource Management Interface – an open-source tool that supports unified job submission for quantum and classical computing tasks and that’s scalable to cloud computing environments. It’s the “black-box” solution industry has been looking for to bridge the established HPC and emerging quantum domains.

The Hartree Centre has a flagship collaboration with IBM in quantum computing. Can you tell us more?

The Hartree National Centre for Digital Innovation (HNCDI) is a £210m public–private partnership with IBM to create innovative digital technologies spanning HPC, AI, data analytics and quantum computing. HNCDI is the cornerstone of IBM’s quantum technology strategy in the UK and, over the past four years, the collaboration has clocked up more than 30 joint projects with industry. In each of these projects, HNCDI is using quantum computers to tackle problems that are out of reach for classical computers.

Do you have any examples of early wins for HNCDI in quantum?

One is streamlining drug discovery and development. As part of a joint effort with the pharmaceutical firm AstraZeneca and quantum-software developer Algorithmiq, we have improved the accuracy of molecular modelling with the help of quantum computing and, by extension, developed a better understanding of the molecular interactions and processes involved in drug synthesis. Another eye-catching development is Qiskit Machine Learning (ML), an open-source library for quantum machine-learning tasks on quantum hardware and classical simulators. While Qiskit ML started as a proof-of-concept library from IBM, our team at the Hartree Centre has, over the past couple of years, developed it into a modular tool for non-specialist users as well as quantum computational scientists and developers.

So quantum computing could play a big role in healthcare?

Healthcare has yielded productive lines of enquiry, including a proof-of-concept study to demonstrate the potential of quantum machine-learning in cancer diagnostics. Working with Royal Brompton and Harefield Hospitals and Imperial College London, we have evaluated histopathology datasets to categorize different types of breast-cancer cells through AI workflows. It’s research that could eventually lead to better predictions regarding the onset and progression of disease.

And what about other sectors?

We have been collaborating with the German power utility E.ON to study the complex challenges that quantum computing may be able to address in the energy sector – such as strategic infrastructure development, effective energy demand management and streamlined integration of renewable energy sources.

What does the next decade look like for the Hartree Centre’s quantum computing programme?

Longer term, the goal is to enable our industry partners to become at-scale end-users of quantum computing, delivering economic and societal impact along the way. As for our own development roadmap at the Hartree Centre, we are evaluating options for the implementation of a large-scale quantum computing platform to further diversify our existing portfolio of HPC, AI, data science and visual computing technologies.

STFC Hartree Centre: helping UK industry deliver societal impact

The Hartree Centre is part of the Science and Technology Facilities Council (STFC), one of the main UK research councils supporting fundamental and applied initiatives in astronomy, physics, computational science and space science.

Based at the Daresbury Laboratory, part of the Sci-Tech Daresbury research and innovation campus in north-west England, the Hartree Centre has more than 160 scientists and technologists specializing in supercomputing, applied scientific computing, data science, AI, cloud and quantum computing.

“Our goal is to help UK industry generate economic growth and societal impact by exploiting advanced HPC capabilities and digital technologies,” explains Vassil Alexandrov, chief science officer at STFC Hartree Centre.

One of the core priorities for Alexandrov and his team is the interface between “exascale” computing and scalable AI. It’s a combination of technologies that’s being lined up to tackle “grand challenges” like the climate crisis and the transition from fossil fuels to clean energy.

A case in point is the Climate Resilience Demonstrator, which uses “digital twins” to simulate how essential infrastructure like electricity grids and telecoms networks might respond to extreme weather events. “These kinds of insights are critical to protect communities, maintain service delivery and build more resilient public infrastructure,” says Alexandrov.

Elsewhere, as part of the Fusion Computing Lab, the Hartree Centre is collaborating with the UK Atomic Energy Authority on sustainable energy generation from nuclear fusion. “We have a joint team of around 60 scientists and engineers working on this initiative to iterate and optimize the building blocks for a fusion power plant,” notes Alexandrov. “The end-game is to deliver net power safely and affordably to the grid from magnetically confined fusion.”

Exascale computing and AI also underpin the Research Computing and Innovation Centre, a collaboration with AWE, the organization that runs research, development and support for the UK’s nuclear-weapons stockpile.

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Today’s artificial intelligence (AI) systems are built on data generated by humans. They’re trained on huge repositories of writing, images and videos, most of which have been scraped from the Internet without the knowledge or consent of their creators. It’s a vast and sometimes ill-gotten treasure trove of information – but for machine-learning pioneer David Silver, it’s nowhere near enough.

“I think if you provide the knowledge that humans already have, it doesn’t really answer the deepest question for AI, which is how it can learn for itself to solve problems,” Silver told an audience at the 12th Heidelberg Laureate Forum (HLF) in Heidelberg, Germany, on Monday.

Silver’s proposed solution is to move from the “era of human data”, in which AI passively ingests information like a student cramming for an exam, into what he calls the “era of experience” in which it learns like a baby exploring its world. In his HLF talk on Monday, Silver played a sped-up video of a baby repeatedly picking up toys, manipulating them and putting them down while crawling and rolling around a room. To murmurs of appreciation from the audience, he declared, “I think that provides a different perspective of how a system might learn.”

Silver, a computer scientist at University College London, UK, has been instrumental in making this experiential learning happen in the virtual worlds of computer science and mathematics. As head of reinforcement learning at Google DeepMind, he was instrumental in developing AlphaZero, an AI system that taught itself to play the ancient stones-and-grid game of Go. It did this via a so-called “reward function” that pushed it to improve over many iterations, without ever being taught the game’s rules or strategy.

More recently, Silver coordinated a follow-up project called AlphaProof that treats formal mathematics as a game. In this case, AlphaZero’s reward is based on getting correct proofs. While it isn’t yet outperforming the best human mathematicians, in 2024 it achieved silver-medal standard on problems at the International Mathematical Olympiad.

Learning in the physics playroom

Could a similar experiential learning approach work in the physical sciences? At an HLF panel discussion on Tuesday afternoon, particle physicist Thea Klaeboe Åarrestad began by outlining one possible application. Whenever CERN’s Large Hadron Collider (LHC) is running, Åarrestad explained, she and her colleagues in the CMS experiment must control the magnets that keep protons on the right path as they zoom around the collider. Currently, this task is performed by a person, working in real time.

In principle, Åarrestad continued, a reinforcement-learning AI could take over that job after learning by experience what works and what doesn’t. There’s just one problem: if it got anything wrong, the protons would smash into a wall and melt the beam pipe. “You don’t really want to do that mistake twice,” Åarrestad deadpanned.

For Åarrestad’s fellow panellist Kyle Cranmer, a particle physicist who works on data science and machine learning at the University of Wisconsin-Madison, US, this nightmare scenario symbolizes the challenge with using reinforcement learning in physical sciences. In situations where you’re able to do many experiments very quickly and essentially for free – as is the case with AlphaGo and its descendants – you can expect reinforcement learning to work well, Cranmer explained. But once you’re interacting with a real, physical system, even non-destructive experiments require finite amounts of time and money.

Another challenge, Cranmer continued, is that particle physics already has good theories that predict some quantities to multiple decimal places. “It’s not low-hanging fruit for getting an AI to come up with a replacement framework de novo,” Cranmer said. A better option, he suggested, might be to put AI to work on modelling atmospheric fluid dynamics, which are emergent phenomena without first-principles descriptions. “Those are super-exciting places to use ideas from machine learning,” he said.

Not for nuclear arsenals

Silver, who was also on Tuesday’s panel, agreed that reinforcement learning isn’t always the right solution. “We should do this in areas where mistakes are small and it can learn from those small mistakes to avoid making big mistakes,” he said. To general laughter, he added that he would not recommend “letting an AI loose on nuclear arsenals”, either.

Reinforcement learning aside, both Åarrestad and Cranmer are highly enthusiastic about AI. For Cranmer, one of the most exciting aspects of the technology is the way it gets scientists from different disciplines talking to each other. The HLF, which aims to connect early-career researchers with senior figures in mathematics and computer science, is itself a good example, with many talks in the weeklong schedule devoted to AI in one form or another.

For Åarrestad, though, AI’s most exciting possibility relates to physics itself. Because the LHC produces far more data than humans and present-day algorithms can handle, Åarrestad explained, much of it is currently discarded. The idea that, as a result, she and her colleagues could be throwing away major discoveries sometimes keeps her up at night. “Is there new physics below 1 TeV?” Åarrestad wondered.

Someday, maybe, an AI might be able to tell us.

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Radiotherapy is a precision cancer therapy that employs personalized treatment plans to target radiation to tumours with high accuracy. Such plans are usually created from high-resolution CT scans of the patient. But interest is growing in an alternative approach: MR simulation, in which MR images are used to generate the treatment plans – for delivery on conventional linac systems as well as the increasingly prevalent MR-guided radiotherapy systems.

One site that has transitioned to this approach is the Institut Jules Bordet in Belgium, which in 2021 acquired both an Elekta Unity MR-Linac and a Siemens MAGNETOM Aera MR-Simulator. “It was a long-term objective for our clinic to have an MR-only workflow,” says Akos Gulyban, a medical physicist at Institut Jules Bordet. “When we moved to a new campus, we decided to purchase the MR-Linac. Then we thought that if we are getting into the MR world for treatment adaptation, we also need to step up in terms of simulation.”

The move to MR simulation delivers many clinical benefits, with MR images providing the detailed anatomical information required to delineate targets and organs-at-risk with the highest precision. But it also creates new challenges for the physicists, particularly when it comes to quality assurance (QA) of MR-based systems. “The biggest concern is geometric distortion,” Gulyban explains. “If there is no distortion correction, then the usability of the machine or the sequence is very limited.”

Addressing distortion

While the magnetic field gradient is theoretically linear, and MRI is indeed extremely accurate at the imaging isocentre, moving away from the isocentre increases distortion. Images of regions 30 or 40 cm away from the isocentre – a reasonable distance for a classical linac – can differ from reality by 15 to 20 mm, says Gulyban. Thankfully, 3D correction algorithms can reduce this discrepancy down to just a couple of millimetres. But such corrections first require an accurate way to measure the distortion.

To address this task, the team at Institut Jules Bordet employ a geometric distortion phantom –the QUASAR MRID^3D Geometric Distortion Analysis System from IBA Dosimetry. Gulyban explains that the MRID^3D was chosen following discussions with experienced users, and that key selling points included the phantom’s automated software and its ability to efficiently store results for long-term traceability.

“My concern was how much time we spend cross-processing, generating reports or evaluating results,” he says. “This software is fully automated, making it much easier to perform the evaluation and less dependent on the operator.”

Gulyban adds that the team was looking for a vendor-independent solution. “I think it is a good approach to use the tools provided [by the vendor] but now we have a way to measure the same thing using a different approach. Since our new campus has a mixture of Siemens MRs and the MR-Linac, this phantom provides a vendor-independent bridge between the two worlds.”

For quality control of the MR-Simulator, the team perform distortion measurements every three months, as well as after system interventions such as shimming and following any problems arising during other routine QA procedures. “We should not consider tests as individual islands in the QA process,” says Gulyban. “For instance, the ACR image quality phantom, which is used for more frequent evaluation, also partly assesses distortion. If we see that failing, I would directly trigger measurements with the more appropriate geometric distortion phantom.”

A lightweight option

To perform MR simulation, the images used for treatment planning must encompass both the target volume and the surrounding region, to ensure accurate delineation of the tumour and nearby organs-at-risk. This requires a large field-of-view (FOV) scan – plus geometric distortion QA that covers the same large FOV.

“You’re using this image to delineate the target and also to spare the organs-at-risk, so the image must reflect reality,” explains Kawtar Lakrad, medical physicist and clinical application specialist at IBA Dosimetry. “You don’t want that image to be twisted or the target volume to appear smaller or bigger than it actually is. You want to make sure that all geometric qualities of the image align with what’s real.”

Typically, geometric distortion phantoms are grid-like, with control points spaced every 0.5 or 1 cm. The entire volume is imaged in the MR scanner and the locations of control points seen in the image compared with their actual positions. “If we apply this to a large FOV phantom, which for MRI will be filled with either water or oil, it’s going to be a very large grid and it’s going to be heavy, 40 or 50 kg,” says Lakrad.

To overcome this obstacle, IBA researchers used innovative harmonic analysis algorithms to design a lightweight geometric distortion phantom with submillimetre accuracy and a large (35 x 30 cm) FOV: the MRID^3D. The phantom comprises two concentric hollow acrylic cylinders, the only liquid being a prefilled mineral oil layer between the two shells, reducing its weight to just 21 kg.

“The idea behind the phantom was very smart because it relies on a mathematical tool,” explains Lakrad. “There is a Fourier transform for the linear signal, which is used for standard grids. But there are also spherical harmonics – and this is what’s used in the MRID^3D. The control points are all on the cylinder surface, plus one in the isocentre, creating a virtual grid that measures 3D geometric distortion.” She adds that the MRID^3D can also differentiate distortion due to the main magnetic field from gradient non-linearity distortion.

Moving into the MR world

Gulyban and his team at Institut Jules Bordet first used MR simulation for pelvic treatments, particularly prostate cancer, he tells Physics World. This was followed by abdominal tumours, such as pancreatic and liver cancers (where many patients were being treated on the MR-Linac) and more recently, cranial and head-and-neck irradiations.

Gulyban points out that the introduction of the MR-Simulator was eased by the team’s experience with the MR-Linac, which helped them “step into the MR world”. Here also, the MRID^3D phantom is used to quantify geometric distortion, both for initial commissioning and continuous QA of the MR-Linac.

“It’s like a consistency check,” he explains. “We have certain manufacturer-defined conditions that we need to meet for the MR-Linac – for instance, that distortion within a 40 mm diameter should be less than 1 mm. To ensure that these are met in a consistent fashion, we repeat the measurements with the manufacturer’s phantom and with the MRID^3D. This gives us extra peace of mind that the machine is performing under the correct conditions.”

For other cancer centres looking to integrate MR into their radiotherapy clinics, Gulyban has some key points of advice. These include starting with MR-guided radiotherapy and then adding MR simulation, identifying a suitable pathology to treat first and gain familiarity, and attending relevant courses or congresses for inspiration.

“The biggest change is actually a change in culture because you have an active MRI in the radiotherapy department,” he notes. “We are used to the radioprotection aspects of radiotherapy, wearing a dosimeter and observing radiation protection principles. MRI is even less forgiving – every possible thing that could go wrong you have to eliminate. Closing all the doors and emptying your pockets must become a reflex habit. You have to prepare mentally for that.”

“When you’re used to CT-based machines, moving to an MR workflow can be a little bit new,” adds Lakrad. “Most physicists are already familiar with the MR concept, but when it comes to the QA process, that’s the most challenging part. Some people would just repeat what’s done in radiology – but the use case is different. In radiotherapy, you have to delineate the target and surrounding volumes exactly. You’re going to be delivering dose, which means the tolerance between diagnostic and radiation therapy is different. That’s the biggest challenge.”

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Artificial intelligence (AI) could help sniff out questionable open-access publications that are more interested in profit than scientific integrity. That is according to an analysis of 15,000 scientific journals by an international team of computer scientists. They find that dubious journals tend to publish an unusually high number of articles and feature authors who have many affiliations and frequently self-cite (Sci. Adv. 11 eadt2792).

Open access removes the requirement for traditional subscriptions. Articles are instead made immediately and freely available for anyone to read, with publication costs covered by authors by paying an article-processing charge.

But as the popularity of open-access journals has risen, there has been a growth in “predatory” journals that exploit the open-access model by making scientists pay publication fees without a proper peer-review process in place.

To build an AI-based method for distinguishing legitimate from questionable journals, Daniel Acuña, a computer scientist at the University of Colorado Boulder, and colleagues used the Directory of Open Access Journals (DOAJ) – an online, community-curated index of open-access journals.

The researchers trained their machine-learning model on 12,869 journals indexed on the DOAJ and 2536 journals that have been removed from the DOAJ due to questionable practices that violate the community’s listing criteria. The team then tested the tool on 15,191 journals listed by Unpaywall, an online directory of free research articles.

To identify questionable journals, the AI-system analyses journals’ bibliometric information and the content and design of their websites, scrutinising details such as the affiliations of editorial board members and the average author h-index – a metric that quantifies a researcher’s productivity and impact.

The AI-model flagged 1437 journals as questionable, with the researchers concluding that 1092 were genuinely questionable while 345 were false positives.

They also identified around 1780 problematic journals that the AI screening failed to flag. According to the study authors, their analysis shows that problematic publishing practices leave detectable patterns in citation behaviour such as the last authors having a low h-index together with a high rate of self-citation.

Acuña adds that the tool could help to pre-screen large numbers of journals, adding, however, that “human professionals should do the final analysis”. The researcher’s novel AI screening system isn’t publicly accessible but they hope to make it available to universities and publishing companies soon.

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It’s almost impossible to avoid reading about advances in quantum computing these days. Despite this, we’re still some way off having fully fault-tolerant, large-scale quantum computers as of right now. One practical difficulty is that even the best present-day quantum computers suffer from noise that can often cause them to return erroneous results.

Research in this field can be broadly divided into two areas: a) designing quantum algorithms with potential practical advantages over classical algorithms (the software) and b) physically building a quantum computer (the hardware).

One of the main approaches to algorithm design is to minimise the number of operations or runtime in an algorithm. One intuitively expects that reducing the number of operations would decrease the chance of errors – the key to constructing a reliable quantum computer.

However, this is not always the case. In a recent paper, the research team found that minimising the number of operations in a quantum algorithm can sometimes be counterproductive, leading to an increased sensitivity to noise. Essentially, running a faster algorithm in non-ideal conditions can result in more errors than if a slower algorithm had been used.

The authors proved that there’s a trade-off between an algorithm’s number of operations and its resilience to noise. This means that, for certain types of errors, slower algorithms might actually be better in some real-world conditions.

These results bring together research on quantum hardware and software. The mathematical framework developed will enable quantum algorithms to be designed with the limitations of current real quantum computers in mind.

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Despite the huge success of the Standard Model of particle physics, we know it’s not complete. Dark matter and neutrino masses are just two of the things that are conspicuously missing in our current theory.

However, it’s been notoriously difficult to perform an experiment that actually disagrees with the model’s predictions.

Many proposed extensions of the Standard Model, such as the fraternal twin Higgs or folded supersymmetry models, include so-called long-lived particles (LLPs).

Unlike most particles produced in high-energy collisions, which decay almost instantaneously, LLPs have relatively long lifetimes, meaning they travel a measurable distance before decaying.

A new paper from the CMS collaboration at CERN searched for evidence of these particles by re-examining previous data from proton-proton collision events.

The new analysis used new techniques such as machine-learning methods to enhance the sensitivity to LLPs.

So, did they find any new particles? The short answer, unfortunately, is no.

However, the new study achieves up to a tenfold improvement over previous limits for LLP masses. It also places the first constraints on many proposed models that predict these particles.

Although this study found no new physics, we’re still confident that something must be out there. And by narrowing down the possible spaces where we might find new particles, we’re one step closer to finding them.

The search continues.

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Call it millennial, generation Y or fin de siècle, high-energy physics during the last two decades of the 20th century had a special flavour. The principal pieces of the Standard Model of particle physics had come together remarkably tightly – so tightly, in fact, that physicists had to rethink what instruments to build, what experiments to plan, and what theories to develop to move forward. But it was also an era when the hub of particle physics moved from the US to Europe.

The momentous events of the 1980s and 1990s will be the focus of the 4th International Symposium on the History of Particle Physics, which is being held on 10–13 November at CERN. The meeting will take place more than four decades after the first symposium in the series was held at Fermilab near Chicago in 1980. Entitled The Birth of Particle Physics, that initial meeting covered the years 1930 to 1950.

Speakers back then included trailblazers such as Paul Dirac, Julian Schwinger and Victor Weisskopf. They reviewed discoveries such as the neutron and the positron and the development of relativistic quantum field theory. Those two decades before 1950 were a time when particle physicists “constructed the room”, so to speak, in which the discipline would be based.

The second symposium – Pions to Quarks – was also held at Fermilab and covered the 1950s. Accelerators could now create particles seen in cosmic-ray collisions, populating what Robert Oppenheimer called the “particle zoo”. Certain discoveries of this era, such as parity violation in the weak interaction, were so shocking that C N Yang likened it to having a blackout and not knowing if the room would look the same when the lights came back on. Speakers at that 1985 event included Luis Alvarez, Val Fitch, Abdus Salam, Robert Wilson and Yang himself.

The third symposium, The Rise of the Standard Model, was held in Stanford, California, in 1992 and covered the 1960s and 1970s. It was a time not of blackouts but of disruptions that dimmed the lights. Charge-parity violation and the existence of two types of neutrino were found in the 1960s, followed in the 1970s by deep inelastic electron scattering and quarks, neutral currents, a fourth quark and gluon jets.

These discoveries decimated alternative approaches to quantum field theory, which was duly established for good as the skeleton of high-energy physics. The era culminated with Sheldon Glashow, Abdus Salam and Steven Weinberg winning the 1979 Nobel Prize for Physics for their part in establishing the Standard Model. Speakers at that third symposium included Murray Gell-Mann, Leon Lederman and Weinberg himself.

Changing times

The upcoming CERN event, on whose programme committee I serve, will start exactly where the previous symposium ended. “1980 is a natural historical break,” says conference co-organizer Michael Riordan, who won the 2025 Abraham Pais Prize for History of Physics. “It begins a period of the consolidation of the Standard Model. Colliders became the main instruments, and were built with specific standard-model targets in mind. And the centre of gravity of the discipline moved across the Atlantic to Europe.”

The conference will address physics that took place at CERN’s Super Proton Synchrotron (SPS), where the W and Z particles were discovered in 1983. It will also examines the SPS’s successor – the Large Electron-Positron (LEP) collider. Opened in 1989, it was used to make precise measurements of these and other implications of the Standard Model until being controversially shut down in 2000 to make way for the Large Hadron Collider (LHC).

There will be coverage as well of failed accelerator projects, which – perhaps perversely – can be equally interesting and revealing as successful facilities

Speakers at the meeting will also discuss Fermilab’s Tevatron, where the top quark – another Standard Model component – was found in 1995. Work at the Stanford Linear Accelerator Center, DESY in Germany, and Tsukuba, Japan, will be tackled too. There will be coverage as well of failed accelerator projects, which – perhaps perversely – can be equally interesting and revealing as successful facilities.

In particular, I will speak about ISABELLE, a planned and partially built proton–proton collider at Brookhaven National Laboratory, which was terminated in 1983 to make way for the far more ambitious Superconducting Super Collider (SSC). ISABELLE was then transformed into the Relativistic Heavy Ion Collider (RHIC), which was completed in 1999 and took nuclear physics into the high-energy regime.

Riordan will talk about the fate of the SSC, which was supposed to discover the Higgs boson or whatever else plays its mass-generating role. But in 1993 the US Congress terminated that project, a traumatic episode for US physics, about which Riordan co-authored the book Tunnel Visions. Its cancellation signalled the end of the glory years for US particle physics and the realization of the need for international collaborations in ever-costlier accelerator projects.

The CERN meeting will also explore more positive developments such as the growing convergence of particle physics and cosmology during the 1980s and 1990s. During that time, researchers stepped up their studies of dark matter, neutrino oscillations and supernovas. It was a period that saw the construction of underground detectors at Gran Sasso in Italy and Kamiokande in Japan.

Other themes to be explored include the development of the Web – which transformed the world – and the impact of globalization, the end of the Cold War, and the rise of high-energy physics in China, and physics in Russia, former Soviet Union republics, and former Eastern Bloc countries. While particle physics became more global, it also grew more dependent on, and vulnerable to, changing political ambitions, economic realities and international collaborations. The growing importance of diversity, communication and knowledge transfer will be looked at too.

The critical point

The years between 1980 and 2000 were a distinct period in the history of particle physics. It took place in the afterglow of the triumph of the Standard Model. The lights in high energy physics did not go out or even dim, to use Yang’s metaphor. Instead, the Standard Model shed so much light on high-energy physics that the effort and excitement focused around consolidating the model.

Particle physics, during those years, was all about finding the deeply hidden outstanding pieces, developing the theory, and connecting with other areas of physics. The triumph was so complete that physicists began to wonder what bigger and more comprehensive structure the Standard Model’s “room” might be embedded in – what was “beyond the Standard Model”. A quarter of a century on, our attempt to make out that structure is still an ongoing task.

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When researchers at Microsoft released a list of the 40 jobs most likely to be affected by generative artificial intelligence (gen AI), few outsiders would have expected to see “mathematician” among them. Yet according to speakers at this year’s Heidelberg Laureate Forum (HLF), which connects early-career researchers with distinguished figures in mathematics and computer science, computers are already taking over many tasks formerly performed by human mathematicians – and the humans have mixed feelings about it.

One of those expressing disquiet is Yang-Hui He, a mathematical physicist at the London Institute for Mathematical Sciences. In general, He is extremely keen on AI. He’s written a textbook about the use of AI in mathematics, and he told the audience at an HLF panel discussion that he’s been peddling machine-learning techniques to his mathematical physics colleagues since 2017.

More recently, though, He has developed concerns about gen AI specifically. “It is doing mathematics so well without any understanding of mathematics,” he said, a note of wonder creeping into his voice. Then, more plaintively, he added, “Where is our place?”

AI advantages

Some of the things that make today’s gen AI so good at mathematics are the same as the ones that made Google’s DeepMind so good at the game of Go. As the theoretical computer scientist Sanjeev Arora pointed out in his HLF talk, “The reason it’s better than humans is that it’s basically tireless.” Put another way, if the 20th-century mathematician Alfréd Rényi once described his colleagues as “machines for turning coffee into theorems”, one advantage of 21st-century AI is that it does away with the coffee.

Arora, however, sees even greater benefits. In his view, AI’s ability to use feedback to improve its own performance – a technique known as reinforcement learning – is particularly well-suited to mathematics.

In the standard version of reinforcement learning, Arora explains, the AI model is given a large bank of questions, asked to generate many solutions and told to use the most correct ones (as labelled by humans) to refine its model. But because mathematics is so formalized, with answers that are so verifiably true or false, Arora thinks it will soon be possible to replace human correctness checkers with AI “proof assistants”. Indeed, he’s developing one such assistant himself, called Lean, with his colleagues at Princeton University in the US.

Humans in the loop?

But why stop there? Why not use AI to generate mathematical questions as well as producing and checking their solutions? Indeed, why not get it to write a paper, peer review it and publish it for its fellow AI mathematicians – which are, presumably, busy combing the literature for information to help them define new questions?

Arora clearly thinks that’s where things are heading, and many of his colleagues seem to agree, at least in part. His fellow HLF panellist Javier Gómez-Serrano, a mathematician at Brown University in the US, noted that AI is already generating results in a day or two that would previously have taken a human mathematician months. “Progress has been quite quick,” he said.

The panel’s final member, Maia Fraser of the University of Ottawa, Canada, likewise paid tribute to the “incredible things that are possible with AI now”.  But Fraser, who works on mathematical problems related to neuroscience, also sounded a note of caution. “My concern is the speed of the changes,” she told the HLF audience.

The risk, Fraser continued, is that some of these changes may end up happening by default, without first considering whether humans want or need them. While we can’t un-invent AI, “we do have agency” over what we want, she said.

So, do we want a world in which AI mathematicians take humans “out of the loop” entirely? For He, the benefits may outweigh the disadvantages. “I really want to see a proof of the Riemann hypothesis,” he said,  to ripples of laughter. If that means that human mathematicians “become priests to oracles”, He added, so be it.

The post Are we heading for a future of superintelligent AI mathematicians? appeared first on Physics World.

The first-ever “space–time crystal” has been created in the US by Hanqing Zhao and Ivan Smalyukh at the University of Colorado Boulder. The system is patterned in both space and time and comprises a rigid lattice of topological solitons that are sustained by steady oscillations in the orientations of liquid crystal molecules.

In an ordinary crystal atomic or molecular structures repeat at periodic intervals in space. In 2012, however, Frank Wilczek suggested that systems might also exist with quantum states that repeat at perfectly periodic intervals in time – even as they remain in their lowest-energy state.

First observed experimentally in 2017, these time crystals are puzzling to physicists because they spontaneously break time–translation symmetry, which states that the laws of physics are the same no matter when you observe them. In contrast, a time crystal continuously oscillates over time, without consuming energy.

A space–time crystal is even more bizarre. In addition to breaking time–translation symmetry, such a system would also break spatial symmetry, just like the repeating molecular patterns of an ordinary crystal. Until now, however, a space–time crystal had not been observed directly.

Rod-like molecules

In their study, Zhao and Smalyukh created a space–time crystal in the nematic phase of a liquid crystal. In this phase the crystal’s rod-like molecules align parallel to each other and also flow like a liquid. Building on computer simulations, they confined the liquid crystal between two glass plates coated with a light-sensitive dye.

“We exploited strong light–matter interactions between dye-coated, light-reconfigurable surfaces, and the optical properties of the liquid crystal,” Smalyukh explains.

When the researchers illuminated the top plate with linearly polarized light at constant intensity, the dye molecules rotate to align perpendicular to the direction of polarization. This reorients nearby liquid crystal molecules, and the effect propagates deeper into the bulk. However, the influence weakens with depth, so that molecules farther from the top plate are progressively less aligned.

As light travels through this gradually twisting structure, its linear polarization is transformed, becoming elliptically polarized by the time it reaches the bottom plate. The dye molecules there become aligned with this new polarization, altering the liquid crystal alignment near the bottom plate. These changes propagate back upward, influencing molecules near the top plate again.

Feedback loop

This is a feedback loop, with the top and bottom plates continuously influencing each other via the polarized light passing through the liquid crystal.

“These light-powered dynamics in confined liquid crystals leads to the emergence of particle-like topological solitons and the space–time crystallinity,” Smalyukh says.

In this environment, particle-like topological solitons emerge as stable, localized twists in the liquid crystal’s orientation that do not decay over time. Like particles, the solitons move and interact with each other while remaining intact.

Once the feedback loop is established, these solitons emerge in a repeating lattice-like pattern. This arrangement not only persisted as the feedback loop continued, but is sustained by it. This is a clear sign that the system exhibits crystalline order in time and space simultaneously.

Accessible system

Having confirmed their conclusions with simulations, Zhao and Smalyukh are confident this is the first experimental demonstration of a space–time crystal. The discovery that such an exotic state can exist in a classical, room-temperature system may have important implications.

“This is the first time that such a phenomenon is observed emerging in a liquid crystalline soft matter system,” says Smalyukh. “Our study calls for a re-examining of various time-periodic phenomena to check if they meet the criteria of time-crystalline behaviour.”

Building on these results, the duo hope to broaden the scope of time crystal research beyond a purely theoretical and experimental curiosity. “This may help expand technological utility of liquid crystals, as well as expand the currently mostly fundamental focus of studies of time crystals to more applied aspects,” Smalyukh adds.

The research is described in Nature Materials.

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Physicists at Osaka Metropolitan University in Japan and the Korea Advanced Institute of Science and Technology (KAIST) claim to have observed the quantum counterpart of the classic Kelvin-Helmholtz instability (KHI), which is the most basic instability in fluids. The effect, seen in a quantum gas of ⁷Li atoms, produces a new type of exotic vortex pattern called an eccentric fractional skyrmion. The finding not only advances our understanding of complex topological quantum systems, it could also help in the development of next-generation memory and storage devices.

Topological defects occur when a system rapidly transitions from a disordered to an ordered phase. These defects, which can occur in a wide range of condensed matter systems, from liquid crystals and atomic gases to the rapidly cooling early universe, can produce excitations such as solitons, vortices and skyrmions.

Skyrmions, first discovered in magnetic materials, are swirling vortex-like spin structures that extend across a few nanometres in a material. They can be likened to 2D knots in which the magnetic moments rotate about 360° within a plane.

Eccentric fractional skyrmions contain singularities

Skyrmions are topologically stable, which makes them robust to external perturbations, and are much smaller than the magnetic domains used to encode data in today’s disk drives. That makes them ideal building blocks for future data storage technologies such as “racetrack” memories. Eccentric fractional skyrmions (EFSs), which had only been predicted in theory until now, have a crescent-like shape and contain singularities – points in which the usual spin structure breaks down, creating sharp distortions as it becomes unsymmetrical.

“To me, the large crescent moon in the upper right corner of Van Gogh’s ‘The Starry Night’ also looks exactly like an EFS,” says Hiromitsu Takeuchi at Osaka, who co-led this new study with Jae-Yoon Choi of KAIST. “EFSs carry half the elementary charge, which means they do not fit into traditional classifications of topological defects.”

The KHI is a classic phenomenon in fluids in which waves and vortices form at the interface between two fluids moving at different speeds. “To observe the KHI in quantum systems, we need a structure containing a thin superfluid interface (a magnetic domain wall), such as in a quantum gas of ⁷Li atoms,” says Takeuchi. “We also need experimental techniques that can skilfully control the behaviour of this interface. Both of these criteria have recently been met by Choi’s group.”

The researchers began by cooling a gas of ⁷Li atoms to near absolute zero temperatures to create a multi-component Bose-Einstein condensate – a quantum superfluid containing two streams flowing at different speeds. At the interface of these streams, they observed vortices, which corresponded to the predicted EFSs.

The behaviour of the KHI is universal

“We have shown that the behaviour of the KHI is universal and exists in both the classical and quantum regimes,” says Takeuchi. This finding could not only lead to a better understanding of quantum turbulence and the unification of quantum and classic hydrodynamics, it could also help in the development of technologies such as next-generation storage and memory devices and spintronics, an emerging technology in which magnetic spin is used to store and transfer information using much less energy than existing electronic devices.

“By further refining the experiment, we might be able to verify certain predictions (some of which were made as long ago as the 19th century) about the wavelength and frequency of KHI-driven interface waves in non-viscous quantum fluids, like the one studied in this work,” he adds.

“In addition to the universal finger pattern we observed, we expect structures like zipper and sealskin patterns, which are unique to such multi-component quantum fluids,” Takeuchi tells Physics World. “As well as experiments, it is necessary to develop a theory that more precisely describes the motion of EFSs, the interaction between these skyrmions and their internal structure in the context of quantum hydrodynamics and spontaneous symmetry breaking.”

The study is detailed in Nature Physics.

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For decades, physicists believed that the top quark, the heaviest known subatomic particle, was too short-lived to form a temporary pair with its antimatter partner. Unlike lighter quarks, which can combine to form protons, neutrons, or longer-lived quark–antiquark pairs, the top quark decays almost instantly. This made the idea of a top–antitop bound state – a fleeting association held together by the strong force – seem impossible. But now, the CMS collaboration at the Large Hadron Collider (LHC) has found the first evidence of such a state, which is dubbed toponium.

Gautier Hamel de Monchenault, spokesperson for CMS, explains, “Many physicists long believed this was impossible. That’s why this result is so significant — it challenges assumptions that have been around for decades, and particle physics textbooks will likely need to be updated because of it.”

Protons and neutrons are formed from quarks, which are fundamental particles that cannot be broken down into smaller constituents.

“There are six types of quark,” explains the German physicist Christian Schwanenberger, who is at DESY and the University of Hamburg and was not involved in the study. “Five of them form bound states thanks to the strong force, one of the four fundamental forces of nature. The top quark, however, is somehow different. It is the heaviest fundamental particle we know, but so far we have not observed it forming bound states in the same way the others do.”

Quasi-bound state

The top quark’s extreme mass makes it decay almost immediately after it is produced. “The top and antitop quarks just have time to exchange a few gluons, the carriers of the strong force, before one of them decays, hence the appellation ‘quasi-bound state’,” Hamel de Monchenault explains.

By detecting these ephemeral interactions, physicists can observe the strong force in a new regime – and the CMS team developed a clever new method to do so. The breakthrough came when the team examined how the spins of the top quark and antitop quark influence each other to create a subtle signature in the particles produced when the quarks decay.

Top quarks are produced in proton–proton collisions at the LHC, where they quickly decay into other particles. These include bottom quarks that then decay to form jets of particles, which can be detected. Top quarks can also decay to form W bosons, which themselves decay into lighter particles (leptons) such as electrons or muons, accompanied by neutrinos.

“We can detect the charged leptons directly and measure their energy very precisely, but we have to infer the presence of the neutrinos indirectly, through an imbalance of the total energy measured,” says Hamel de Monchenault. By studying the pattern and energy of the leptons and jets, the CMS team deduced the existence of top–antitop pairs and spotted the subtle signature of the fleeting quasi-bound state.

Statistical significance

The CMS researchers observed an excess of events in which the top and antitop quarks were produced almost at rest relative to each other – the precise condition needed for a quasi-bound state to form. “The signal has a statistical significance above 5σ, which means the chance it’s just a statistical fluctuation is less than one in a few million,” Hamel de Monchenault says.

While this excess accounts for only about 1% of top quark pair production, it aligns with predictions for toponium formation and offers insights into the strong force.

“Within the achieved precision, the result matches the predictions of advanced calculations involving the strong force,” explains Hamel de Monchenault. “An effect once thought too subtle to detect with current technology has now been observed. It’s comforting in a way: even the heaviest known quarks are not always alone – they can briefly embrace their opposites.”

Future directions

The discovery has energized the particle physics community. “Scientists are excited to explore the strong force in a completely new regime,” says Schwanenberger. Researchers will refine theoretical models, simulate toponium more precisely, and study its decay patterns and excited states. Much of this work will rely on the High-Luminosity LHC, expected to start operations around 2030, and potentially on future electron–positron colliders capable of studying top quarks with unprecedented precision.

“The present results are based on LHC data recorded between 2015 and 2018 [Run 2]. Since 2022, ATLAS and CMS are recording data at a slightly higher energy, which is favourable for top quark production. The amount of data already surpasses that of Run 2, and we expect that with such huge amounts of data, the properties of this new signal can be studied in detail,” Hamel de Monchenault says.

This research could ultimately answer a fundamental question: is the top quark simply another quark like its lighter siblings, or could it hold the key to physics beyond the Standard Model? “Investigating different toponium states will be a key part of the top quark research programme,” Schwanenberger says. “It could reshape our understanding of matter itself and reveal whatever holds the world together in its inmost folds.”

The results are published in Reports on Progress in Physics.

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It was early in the morning of Monday 14 September 2015, exactly 10 years ago, when gravitational waves created from the collision of two black holes 1.3 billion light-years away hit the LIGO detectors in the US. The detections took place just as the two giant interferometers – one in Washington and the other in Louisiana – were being calibrated before the first observational run was due to begin four days later.

In one of those curious accidents of history, staff on duty at the Louisiana detector had gone to bed a few hours before the waves rolled in. If they hadn’t packed in their calibrations for the night, it would have prevented LIGO from making its historic measurement, dubbed GW150914. Of course, it would surely only have been a matter of time until LIGO had spotted its first signal, with more than 200 gravitational-wave events so far detected.

Observing these “ripples in space–time”, which had long been on many physicists’ bucket lists, has over the last decade become almost routine. Most gravitational-wave detections have been binary black-hole mergers, though there have also been a few neutron-star/black-hole collisions and some binary neutron-star mergers too. Gravitational-wave astronomy is now a well-established field not just thanks to LIGO but also Virgo in Italy and KAGRA in Japan.

In fact, physicists are already planning what would be a third-generation gravitational-wave detector. The Einstein Telescope, which could do in a day what took LIGO a decade, could be open by 2035, with three locations vying to host the facility. The Italian island of Sardinia is one option. Saxony in Germany is another, with the third being a site near where Germany, Belgium and the Netherlands meet.

A decision is expected to be made in two years’ time, but whichever site is picked – and assuming the €2bn construction costs can be found – Europe would be installed firmly at the forefront of gravitational-wave research. That’s because the European Space Agency is also planning a space-based gravitational-wave detector called LISA. It is set to start in 2035 – the same year as the Einstein Telescope.

The US has its own third-generation design, dubbed the Cosmic Explorer. But given the turmoil in US science under Donald Trump, it’s far from certain if it’ll ever be built. However, if other nations step in and build a network of such facilities around the world, as researchers hope, we could well be in for a new golden age for gravitational-wave astronomy. That bucket list just got longer.

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The isle of Vulcano is a part of the central volcanic ridge of the Aeolian archipelago on the Tyrrhenian Sea in southern Italy. Over the course of its history, Vulcano has undergone multiple explosive eruptions, with the last one thought to have occurred around 1888–1890. However, there is an active hydrothermal system under Vulcano that has shown evidence of intermittent magma and gas flows since 2021 – a sign that the volcano has been in a state of unrest.

During unrest, the volcanic risk increases significantly – and the summer months on the island currently attract a lot of tourists that might be at risk, even from minor eruptive events or episodes of increased degassing. To examine why this unrest has occurred, researchers from the University of Geneva have collaborated with the National Institute of Geophysics and Volcanology (INGV) in Italy to recreate a 3D model of the interior of the volcano on Vulcano, using a combination of nodal seismic networks and artificial intelligence (AI).

Until now, few studies have examined the deep underground details of volcanoes, instead relying on looking at the outline of their internal structure. This is because the geological domains where eruptions nucleate are often inaccessible using airborne geophysical techniques, and onshore studies don’t penetrate far enough into the volcanic plumbing system to look at how the magma and hydrothermal fluids mix. Recent studies have shown the outline of the plumbing systems, but they’ve not had sufficient resolution to distinguish the magma from the hydrothermal system.

3D modelling of the volcano

To better understand what could have caused the 2021 Vulcano unrest, the researchers deployed a nodal network of 196 seismic sensors across Vulcano and Lipari (another island in the archipelago) to measure secondary seismic waves (S-waves) using a technique called seismic ambient noise tomography. S-waves propagate slowly as they pass through fluid-rich zones, which allows magma to be identified.

The researchers captured the S-wave data using the nodal sensor network and processed it with AI – using a deep neural network. This allowed the extensive seismic dispersion data to be quickly and automatically recovered, enabling generation of a 3D S-wave velocity model. The data were captured during the volcano’s early unrest’s phase, and the sensors recorded the natural ground vibrations over a period of one month. The model revealed the high-resolution tomography of the shallow part of a volcanic system in unrest, with the approach compared to taking an “X-ray” of the volcano.

“Our study shows that our end-to-end ambient noise tomography method works with an unprecedented resolution due to using dense nodal seismic networks,” says lead author Douglas Stumpp from the University of Geneva. “The use of deep neural networks allowed us to quickly and accurately measure enormous seismic dispersion data to provide near-real time monitoring.”

The model showed that there was no new magma body between Lipari and Vulcano within the first 2 km of the Earth’s crust, but it did reveal regions that could host cooling melts at the base of the hydrothermal system. These melts were proposed to be degassing melts that could easily release gas and brines if disturbed by an Earthquake – suggesting that tectonic fault dynamics may trigger volcanic unrest. It’s thought that the volcano might have released trapped fluids at depth after being perturbed by fault activity during the 2021 unrest.

Improving risk management

While this method doesn’t enable the researchers to predict when the eruption will happen, it provides a significant understanding into how the internal dynamics of volcanoes work during periods of unrest. The use of AI enables rapid processing of large amounts of data, so in the future, the approach could be used as an early warning system by analysing the behaviour of the volcano as it unfolds.

In theory, this could help to design dynamic evacuation plans based on the direct real-time behaviour of the volcano, which would potentially save lives. The researchers state that this could take some time to develop due to the technical challenge of processing such massive volumes of data in real time – but they note that this is now more feasible thanks to machine learning and deep learning.

When asked about how the researchers plan to further develop the research, Stumpp concludes that “our study paves the ground for 4D ambient noise tomography monitoring – three dimensions of space and one dimension of time. However, I believe permanent and maintained seismic nodal networks with telemetric access to the data need to be implemented to achieve this goal”.

The research is published in Nature Communications.

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A new high-speed multifocus microscope could facilitate discoveries in developmental biology and neuroscience thanks to its ability to image rapid biological processes over the entire volume of tiny living organisms in real time.

The pictures from many 3D microscopes are obtained sequentially by scanning through different depths, making them too slow for accurate live imaging of fast-moving natural functions in individual cells and microscopic animals. Even current multifocus microscopes that capture 3D images simultaneously have either relatively poor image resolution or can only image to shallow depths.

In contrast, the new 25-camera “M25” microscope – developed during his doctorate by Eduardo Hirata-Miyasaki and his supervisor Sara Abrahamsson, both then at the University of California Santa Cruz, together with collaborators at the Marine Biological Laboratory in Massachusetts and the New Jersey Institute of Technology – enables high-resolution 3D imaging over a large field-of-view, with each camera capturing 180 × 180 × 50 µm volumes at a rate of 100 per second.

“Because the M25 microscope is geared towards advancing biomedical imaging we wanted to push the boundaries for speed, high resolution and looking at large volumes with a high signal-to-noise ratio,” says Hirata-Miyasaki, who is now based in the Chan Zuckerberg Biohub in San Francisco.

The M25, detailed in Optica, builds on previous diffractive-based multifocus microscopy work by Abrahamsson, explains Hirata-Miyasaki. In order to capture multiple focal planes simultaneously, the researchers devised a multifocus grating (MFG) for the M25. This diffraction grating splits the image beam coming from the microscope into a 5 × 5 grid of evenly illuminated 2D focal planes, each of which is recorded on one of the 25 synchronized machine vision cameras, such that every camera in the array captures a 3D volume focused on a different depth. To avoid blurred images, a custom-designed blazed grating in front of each camera lens corrects for the chromatic dispersion (which spreads out light of different wavelengths) introduced by the MFG.

The team used computer simulations to reveal the optimal designs for the diffractive optics, before creating them at the University of California Santa Barbara nanofabrication facility by etching nanometre-scale patterns into glass. To encourage widespread use of the M25, the researchers have published the fabrication recipes for their diffraction gratings and made the bespoke software for acquiring the microscope images open source. In addition, the M25 mounts to the side port of a standard microscope, and uses off-the-shelf cameras and camera lenses.

The M25 can image a range of biological systems, since it can be used for fluorescence microscopy – in which fluorescent dyes or proteins are used to tag structures or processes within cells – and can also work in transmission mode, in which light is shone through transparent samples. The latter allows small organisms like C. elegans larvae, which are commonly used for biological research, to be studied without disrupting them.

The researchers performed various imaging tests using the prototype M25, including observations of the natural swimming motion of entire C. elegans larvae. This ability to study cellular-level behaviour in microscopic organisms over their whole volume may pave the way for more detailed investigations into how the nervous system of C. elegans controls its movement, and how genetic mutations, diseases or medicinal drugs affect that behaviour, Hirata-Miyasaki tells Physics World. He adds that such studies could further our understanding of human neurodegenerative and neuromuscular diseases.

“We live in a 3D world that is also very dynamic. So with this microscope I really hope that we can keep pushing the boundaries of acquiring live volumetric information from small biological organisms, so that we can capture interactions between them and also [see] what is happening inside cells to help us understand the biology,” he continues.

As part of his work at the Chan Zuckerberg Biohub, Hirata-Miyasaki is now developing deep-learning models for analysing dynamic cell and organism multichannel dynamic live datasets, like those acquired by the M25, “so that we can extract as much information as possible and learn from their dynamics”.

Meanwhile Abrahamsson, who is currently working in industry, hopes that other microscopy development labs will make their own M25 systems.  She is also considering commercializing the instrument to help ensure its widespread use.

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This episode of the Physics World Weekly podcast features Scott Bolton, who is principal investigator on NASA’s Juno mission to Jupiter. Launched in 2011, the mission has delivered important insights into the nature of the gas-giant planet. In this conversation with Physics World’s Margaret Harris, Bolton explains how Juno continues to change our understanding of Jupiter and other gas giants.

Bolton and Harris chat about the mission’s JunoCam, which has produced some gorgeous images of Jupiter and it moons.

Although the Juno mission was expected to last only a few years, the spacecraft is still going strong despite operating in Jupiter’s intense radiation belts. Bolton explains how the Juno team has rejuvenated radiation-damaged components, which has provided important insights for those designing future missions to space.

However Juno’s future is uncertain. Despite its great success, the mission is currently scheduled to end at the end of September, which is something that Bolton also addresses in the conversation.

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A combination of static proton arcs and shoot-through proton beams could increase plan conformity and homogeneity and reduce delivery times in upright proton therapy, according to new research from RaySearch Laboratories in Sweden.

Proton arc therapy (PAT) is an emerging rotational delivery technique with potential to improve plan quality – reducing dose to organs-at-risk while maintaining target dose. The first clinical PAT treatments employed static arcs, in which multiple energy layers are delivered from many (typically 10 to 30) discrete angles. Importantly, static arc PAT can be delivered on conventional proton therapy machines. It also offers simpler beam arrangements than intensity-modulated proton therapy (IMPT).

“In IMPT of head-and-neck cancers, the beam directions are normally set up in a complicated pattern in different planes, with range shifters needed to treat the shallow part of the tumour,” explains Erik Engwall, chief physicist at RaySearch Laboratories. “In PAT, the many beam directions are arranged in the same plane and no range shifters are typically needed. With all beams in the same plane, it is easier to move to upright treatments.”

Upright proton therapy involves rotating the patient (in an upright position) in front of a static horizontal treatment beam. The approach could reduce costs by using compact proton delivery systems. This compactness, however, places energy selection close to the patient, increasing scattering in the proton beam. To combat this, the team propose adding a layer of shoot-through protons to each direction of the proton arc.

The idea is that while most protons are delivered with Bragg peaks placed in the target, the sharp penumbra of the high-energy protons shooting through the target will combat beam broadening. The rotational delivery in the proton arc spreads the exit dose from these shoot-through beams over many angles, minimizing dose to surrounding tissues. And as the beamline is fixed, shoot-through protons exit in the same direction (behind the patient) for all angles, simplifying shielding to a single beam dump opposite the fixed beam.

Simulation studies

To test this approach, Engwall and colleagues simulated treatment plans for a virtual phantom containing three targets and an organ-at-risk, reporting their findings in Medical Physics. They used a development version of RayStation v2025 with a beam model of the Mevion s250-FIT system (which combines a compact cyclotron, an upright positioner and an in-room CT scanner).

For each target, the team created static arc plans with (Arc+ST) and without shoot-through beams and with/without collimation, as well as 3-beam IMPT plans with and without shoot-through beams (all with collimation). Arc plans used 20 uniformly spaced beam directions, and the shoot-through plans included an additional layer of the highest system energy (230 MeV) for each direction.

For all targets, Arc+ST plans showed superior conformity, homogeneity and target robustness to arc plans without shoot-through protons. Adding collimation slightly improved the arc plans without shoot-through protons but had little impact on Arc+ST plans.

The IMPT plans achieved similar homogeneity and robustness to the best arc plans, but with far lower conformity due to the shoot-through protons delivering a concentrated exit dose behind the target (while static arcs distribute this dose over many directions). Adding shoot-through protons improved IMPT plan quality, but to a lesser degree than for PAT plans.

Clinical case

The researchers repeated their analysis for a clinical head-and-neck cancer case, comparing static arcs with 5-beam IMPT. Again, Arc+ST plans performed better than any others for almost all metrics. “The Arc+ST plans have the best quality due to the sharpening of the penumbra of the shoot-through part, even better than when using a collimator,” says Engwall.

Notably, the findings suggest that collimation is not needed when combining arcs with shoot-through beams, enabling rapid treatments. With fast energy switching and the patient rotation at 1 rpm, Arc+ST achieved an estimated delivery time of less than 5.4 min – faster than all other plans for this case, including 5-beam IMPT.

“Treatment time is reduced when the leaves of the dynamic collimator do not need to move,” Engwall explains. “There is also no risk of mechanical failures of the collimator and the secondary neutron production will be lower when there are fewer objects in the beamline.”

Another benefit of upright delivery is that the shoot-through protons can be used for range verification during treatments, using a detector integrated into the beam dump behind the patient. The team investigated this concept with three simulated error scenarios: 5% systematic shift in stopping power ratio; 5 mm setup shift; and 2 cm shoulder movement. The technique successfully detected all errors.

As the range detector is permanently installed in the treatment room and the shoot-through protons are part of the treatment plan, this method does not add time to the patient setup and can be used in every treatment fraction to detect both intra- and inter-fraction uncertainties.

Although this is a proof-of-concept study, the researchers conclude that it highlights the combined advantages of the new treatment technique, which could “leverage the potential of compact upright proton treatments and make proton treatments more affordable and accessible to a larger patient group”.

Engwall tells Physics World that the team is now collaborating with several clinical research partners to investigate the technique’s potential across larger patient data sets, for other treatment sites and multiple treatment machines.

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A machine learning-based approach that could help astronomers detect lower-frequency gravitational waves has been unveiled by researchers in the UK, US, and Italy. Dubbed deep loop shaping, the system would apply real-time corrections to the mirrors used in gravitational wave interferometers. This would dramatically reduce noise in the system, and could lead to a new wave of discoveries of black hole and neutron star mergers – according to the team.

In 2015, the two LIGO interferometers made the very first observation of a gravitational wave: attributing its origin to a merger of two black holes that were roughly 1.3 billion light–years from Earth.

Since then numerous gravitational waves have been observed with frequencies ranging from 30–2000 Hz. These are believed to be from the mergers of small black holes and neutron stars.

So far, however, the lower reaches of the gravitational wave frequency spectrum (corresponding to much larger black holes) have gone largely unexplored. Being able to detect gravitational waves at 10–30 Hz would allow us to observe the mergers of intermediate-mass black holes at 100–100,000 solar masses. We could also measure the eccentricities of binary black hole orbits. However, these detections are not currently possible because of vibrational noise in the mirrors at the end of each interferometer arm.

Subatomic precision

“As gravitational waves pass through LIGO’s two 4-km arms, they warp the space between them, changing the distance between the mirrors at either end,” explains Rana Adhikari at Caltech, who is part of the team that has developed the machine-learning technique. “These tiny differences in length need to be measured to an accuracy of 10^-19 m, which is 1/10,000^th the size of a proton. [Vibrational] noise has limited LIGO for decades.”

To minimize noise today, these mirrors are suspended by a multi-stage pendulum system to suppress seismic disturbances. The mirrors are also polished and coated to eliminate surface imperfections almost entirely. On top of this, a feedback control system corrects for many of the remaining vibrations and imperfections in the mirrors.

Yet for lower-frequency gravitational waves, even this subatomic level of precision and correction is not enough. As a laser beam impacts a mirror, the mirror can absorb minute amounts of energy – creating tiny thermal distortions that complicate mirror alignment. In addition, radiation pressure from the laser, combined with seismic motions that are not fully eliminated by the pendulum system, can introduce unwanted vibrations in the mirror.

The team proposed that this problem could finally be addressed with the help of artificial intelligence (AI). “Deep loop shaping is a new AI method that helps us to design and improve control systems, with less need for deep expertise in control engineering,” describes Jonas Buchli at Google DeepMind, who led the research. “While this is helping us to improve control over high precision devices, it can also be applied to many different control problems.”

Deep reinforcement learning

The team’s approach is based on deep reinforcement learning, whereby a system tests small adjustments to its controls and adapts its strategy over time through a feedback system of rewards and penalties.

With deep loop shaping, the team introduced smarter feedback controls for the pendulum system suspending the interferometer’s mirrors. This system can adapt in real time to keep the mirrors aligned with minimal control noise – counteracting thermal distortions, seismic vibrations, and forces induced by radiation pressure.

“We tested our controllers repeatedly on the LIGO system in Livingston, Louisiana,” Buchli continues. “We found that they worked as well on hardware as in simulation, confirming that our controller keeps the observatory’s system stable over prolonged periods.”

Based on these promising results, the team is now hopeful that deep loop shaping could help to boost the cosmological reach of LIGO and other existing detectors, along with future generations of gravitational-wave interferometers.

“We are opening a new frequency band, and we might see a different universe much like the different electromagnetic bands like radio, light, and X-rays tell complementary stories about the universe,” says team member Jan Harms at the Gran Sasso Science Institute in Italy. “We would gain the ability to observe larger black holes, and to provide early warnings for neutron star mergers. This would allow us to tell other astronomers where to point their telescopes before the explosion occurs.”

The research is described in Science.

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