On Nov. 2, 2000, the Soyuz TM-31 spacecraft docked with the Zvezda module of the International Space Station. On board were Roscosmos cosmonauts Yuri Gidzenko and Sergei Krikalev and NASA astronaut Bill Shepherd. They were known as Expedition 1, the first long-term crew to live on the ISS. Since their arrival, there have always been […]
Modular and scalable: the ICE-Q cryogenics platform delivers the performance and reliability needed for professional computing environments while also providing a flexible and extendable design. The standard configuration includes a cooling module, a payload with a large sample space, and a side-loading wiring module for scalable connectivity (Courtesy: ICEoxford)
At the centre of most quantum labs is a large cylindrical cryostat that keeps the delicate quantum hardware at ultralow temperatures. These cryogenic chambers have expanded to accommodate larger and more complex quantum systems, but the scientists and engineers at UK-based cryogenics specialist ICEoxford have taken a radical new approach to the challenge of scalability. They have split the traditional cryostat into a series of cube-shaped modules that slot into a standard 19-inch rack mount, creating an adaptable platform that can easily be deployed alongside conventional computing infrastructure.
“We wanted to create a robust, modular and scalable solution that enables different quantum technologies to be integrated into the cryostat,” says Greg Graf, the company’s engineering manager. “This approach offers much more flexibility, because it allows different modules to be used for different applications, while the system also delivers the efficiency and reliability that are needed for operational use.”
The standard configuration of the ICE-Q platform has three separate modules: a cryogenics unit that provides the cooling power, a large payload for housing the quantum chip or experiment, and a patent-pending wiring module that attaches to the side of the payload to provide the connections to the outside world. Up to four of these side-loading wiring modules can be bolted onto the payload at the same time, providing thousands of external connections while still fitting into a standard rack. For applications where space is not such an issue, the payload can be further extended to accommodate larger quantum assemblies and potentially tens of thousands of radio-frequency or fibre-optic connections.
The cube-shaped form factor provides much improved access to these external connections, whether for designing and configuring the system or for ongoing maintenance work. The outer shell of each module consists of panels that are easily removed, offering a simple mechanism for bolting modules together or stacking them on top of each other to provide a fully scalable solution that grows with the qubit count.
The flexible design also offers a more practical solution for servicing or upgrading an installed system, since individual modules can be simply swapped over as and when needed. “For quantum computers running in an operational environment it is really important to minimize the downtime,” says Emma Yeatman, senior design engineer at ICEoxford. “With this design we can easily remove one of the modules for servicing, and replace it with another one to keep the system running for longer. For critical infrastructure devices, it is possible to have built-in redundancy that ensures uninterrupted operation in the event of a failure.”
Other features have been integrated into the platform to make it simple to operate, including a new software system for controlling and monitoring the ultracold environment. “Most of our cryostats have been designed for researchers who really want to get involved and adapt the system to meet their needs,” adds Yeatman. “This platform offers more options for people who want an out-of-the-box solution and who don’t want to get hands on with the cryogenics.”
Such a bold design choice was enabled in part by a collaborative research project with Canadian company Photonic Inc, funded jointly by the UK and Canada, that was focused on developing an efficient and reliable cryogenics platform for practical quantum computing. That R&D funding helped to reduce the risk of developing an entirely new technology platform that addresses many of the challenges that ICEoxford and its customers had experienced with traditional cryostats. “Quantum technologies typically need a lot of wiring, and access had become a real issue,” says Yeatman. “We knew there was an opportunity to do better.”
However, converting a large cylindrical cryostat into a slimline and modular form factor demanded some clever engineering solutions. Perhaps the most obvious was creating a frame that allows the modules to be bolted together while still remaining leak tight. Traditional cryostats are welded together to ensure a leak-proof seal, but for greater flexibility the ICEoxford team developed an assembly technique based on mechanical bonding.
The side-loading wiring module also presented a design challenge. To squeeze more wires into the available space, the team developed a high-density connector for the coaxial cables to plug into. An additional cold-head was also integrated into the module to pre-cool the cables, reducing the overall heat load generated by such large numbers of connections entering the ultracold environment.
Flexible for the future: the outer shell of the modules is covered with removable panels that make it easy to extend or reconfigure the system (Courtesy: ICEoxford)
Meanwhile, the speed of the cooldown and the efficiency of operation have been optimized by designing a new type of heat exchanger that is fabricated using a 3D printing process. “When warm gas is returned into the system, a certain amount of cooling power is needed just to compress and liquefy that gas,” explains Kelly. “We designed the heat exchangers to exploit the returning cold gas much more efficiently, which enables us to pre-cool the warm gas and use less energy for the liquefaction.”
The initial prototype has been designed to operate at 1 K, which is ideal for the photonics-based quantum systems being developed by ICEoxford’s research partner. But the modular nature of the platform allows it to be adapted to diverse applications, with a second project now underway with the Rutherford Appleton Lab to develop a module that that will be used at the forefront of the global hunt for dark matter.
Already on the development roadmap are modules that can sustain temperatures as low as 10 mK – which is typically needed for superconducting quantum computing – and a 4 K option for trapped-ion systems. “We already have products for each of those applications, but our aim was to create a modular platform that can be extended and developed to address the changing needs of quantum developers,” says Kelly.
As these different options come onstream, the ICEoxford team believes that it will become easier and quicker to deliver high-performance cryogenic systems that are tailored to the needs of each customer. “It normally takes between six and twelve months to build a complex cryogenics system,” says Graf. “With this modular design we will be able to keep some of the components on the shelf, which would allow us to reduce the lead time by several months.”
More generally, the modular and scalable platform could be a game-changer for commercial organizations that want to exploit quantum computing in their day-to-day operations, as well as for researchers who are pushing the boundaries of cryogenics design with increasingly demanding specifications. “This system introduces new avenues for hardware development that were previously constrained by the existing cryogenics infrastructure,” says Kelly. “The ICE-Q platform directly addresses the need for colder base temperatures, larger sample spaces, higher cooling powers, and increased connectivity, and ensures our clients can continue their aggressive scaling efforts without being bottlenecked by their cooling environment.”
You can find out more about the ICE-Q platform by contacting the ICEoxford team at iceoxford.com, or via email at sales@iceoxford.com. They will also be presenting the platform at the UK’s National Quantum Technologies Showcase in London on 7 November, with a further launch at the American Physical Society meeting in March 2026.
Due to government shutdown restrictions currently in place in the US, the researchers who headed up this study have not been able to comment on their work
Laser plasma acceleration (LPA) may be used to generate multi-gigaelectronvolt muon beams, according to physicists at the Lawrence Berkeley National Laboratory (LBNL) in the US. Their work might help in the development of ultracompact muon sources for applications such as muon tomography – which images the interior of large objects that are inaccessible to X-ray radiography.
Muons are charged subatomic particles that are produced in large quantities when cosmic rays collide with atoms 15–20 km high up in the atmosphere. Muons have the same properties as electrons but are around 200 times heavier. This means they can travel much further through solid structures than electrons. This property is exploited in muon tomography, which analyses how muons penetrate objects and then exploits this information to produce 3D images.
The technique is similar to X-ray tomography used in medical imaging, with the cosmic-ray radiation taking the place of artificially generated X-rays and muon trackers the place of X-ray detectors. Indeed, depending on their energy, muons can traverse metres of rock or other materials, making them ideal for imaging thick and large structures. As a result, the technique has been used to peer inside nuclear reactors, pyramids and volcanoes.
As many as 10,000 muons from cosmic rays reach each square metre of the Earth’s surface every minute. These naturally produced particles have unpredictable properties, however, and they also only come from the vertical direction. This fixed directionality means that can take months to accumulate enough data for tomography.
Another option is to use the large numbers of low-energy muons that can be produced in proton accelerator facilities by smashing a proton beam onto a fixed carbon target. However, these accelerators are large and expensive facilities, limiting their use in muon tomography.
A new compact source
Physicists led by Davide Terzani have now developed a new compact muon source based on LPA-generated electron beams. Such a source, if optimized, could be deployed in the field and could even produce muon beams in specific directions.
In LPA, an ultra-intense, ultra-short, and tightly focused laser pulse propagates into an “under-dense” gas. The pulse’s extremely high electric field ionizes the gas atoms, freeing the electrons from the nuclei, so generating a plasma. The ponderomotive force, or radiation pressure, of the intense laser pulse displaces these electrons and creates an electrostatic wave that produces accelerating fields orders of magnitude higher than what is possible in the traditional radio-frequency cavities used in conventional accelerators.
LPAs have all the advantages of an ultra-compact electron accelerator that allows for muon production in a small-size facility such as BeLLA, where Terzani and his colleagues work. Indeed, in their experiment, they succeeded in generating a 10 GeV electron beam in a 30 cm gas target for the first time.
The researchers collided this beam with a dense target, such as tungsten. This slows the beam down so that it emits Bremsstrahlung, or braking radiation, which interacts with the material, producing secondary products that include lepton–antilepton pairs, such as electron–positron and muon–antimuon pairs. Behind the converter target, there is also a short-lived burst of muons that propagates roughly along the same axis as the incoming electron beam. A thick concrete shielding then filters most of the secondary products, letting the majority of muons pass through it.
Crucially, Terzani and colleagues were able to separate the muon signal from the large background radiation – something that can be difficult to do because of the inherent inefficiency of the muon production process. This allowed them to identify two different muon populations coming from the accelerator. These were a collimated, forward directed population, generated by pair production; and a low-energy, isotropic, population generated by meson decay.
Many applications
Muons can ne used in a range of fields, from imaging to fundamental particle physics. As mentioned, muons from cosmic rays are currently used to inspect large and thick objects not accessible to regular X-ray radiography – a recent example of this is the discovery of a hidden chamber in Khufu’s Pyramid. They can also be used to image the core of a burning blast furnace or nuclear waste storage facilities.
While the new LPA-based technique cannot yet produce muon fluxes suitable for particle physics experiments – to replace a muon injector, for example – it could offer the accelerator community a convenient way to test and develop essential elements towards making a future muon collider.
The experiment in this study, which is detailed in Physical Review Accelerators and Beams, focused on detecting the passage of muons, unequivocally proving their signature. The researchers conclude that they now have a much better understanding of the source of these muons.
Unfortunately, the original programme that funded this research has ended, so future studies are limited at the moment. Not to be disheartened, the researchers say they strongly believe in the potential of LPA-generated muons and are working on resuming some of their experiments. For example, they aim to measure the flux and the spectrum of the resulting muon beam using completely different detection techniques based on ultra-fast particle trackers, for example.
The LBNL team also wants to explore different applications, such as imaging deep ore deposits – something that will be quite challenging because it poses strict limitations on the minimum muon energy required to penetrate soil. Therefore, they are looking into how to increase the muon energy of their source.
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When it comes to building a fully functional “fault-tolerant” quantum computer, companies and government labs all over the world are rushing to be the first over the finish line. But a truly useful universal quantum computer capable of running complex algorithms would have to entangle millions of coherent qubits, which are extremely fragile. Because of environmental factors such as temperature, interference from other electronic systems in hardware, and even errors in measurement, today’s devices would fail under an avalanche of errors long before reaching that point.
So the problem of error correction is a key issue for the future of the market. It arises because errors in qubits can’t be corrected simply by keeping multiple copies, as they are in classical computers: quantum rules forbid the copying of qubit states while they are still entangled with others, and are thus unknown. To run quantum circuits with millions of gates, we therefore need new tricks to enable quantum error correction (QEC).
Protected states
The general principle of QEC is to spread the information over many qubits so that an error in any one of them doesn’t matter too much. “The essential idea of quantum error correction is that if we want to protect a quantum system from damage then we should encode it in a very highly entangled state,” says John Preskill, director of the Institute for Quantum Information and Matter at the California Institute of Technology in Pasadena.
There is no unique way of achieving that spreading, however. Different error-correcting codes can depend on the connectivity between qubits – whether, say, they are coupled only to their nearest neighbours or to all the others in the device – which tends to be determined by the physical platform being used. However error correction is done, it must be done fast. “The mechanisms for error correction need to be running at a speed that is commensurate with that of the gate operations,” saysMichael Cuthbert, founding director of the UK’s National Quantum Computing Centre (NQCC). “There’s no point in doing a gate operation in a nanosecond if it then takes 100 microseconds to do the error correction for the next gate operation.”
At the moment, dealing with errors is largely about compensation rather than correction: patching up the problems of errors in retrospect, for example by using algorithms that can throw out some results that are likely to be unreliable (an approach called “post-selection”). It’s also a matter of making better qubits that are less error-prone in the first place.
1 From many to few
(Courtesy: Riverlane)
To protect the information stored in qubits, a multitude of unreliable physical qubits have to be combined in such a way that if one qubit fails and causes an error, the others can help protect the system. Essentially, by combining many physical qubits (shown above on the left), one can build a few “logical” qubits that are strongly resistant to noise.
According to Maria Maragkou, commercial vice-president of quantum software company Riverlane, the goal of full QEC has ramifications for the design of the machines all the way from hardware to workflow planning. “The shift to support error correction has a profound effect on the way quantum processors themselves are built, the way we control and operate them, through a robust software stack on top of which the applications can be run,” she explains. The “stack” includes everything from programming languages to user interfaces and servers.
With genuinely fault-tolerant qubits, errors can be kept under control and prevented from proliferating during a computation. Such qubits might be made in principle by combining many physical qubits into a single “logical qubit” in which errors can be corrected (see figure 1). In practice, though, this creates a large overhead: huge numbers of physical qubits might be needed to make just a few fault-tolerant logical qubits. The question is then whether errors in all those physical qubits can be checked faster than they accumulate (see figure 2).
The illustration gives an overview of quantum error correction (QEC) in action within a quantum processing unit. UK-based company Riverlane is building its Deltaflow QEC stack that will correct millions of data errors in real time, allowing a quantum computer to go beyond the reach of any classical supercomputer.
Fault-tolerant quantum computing is the ultimate goal, says Jay Gambetta, director of IBM research at the company’s centre in Yorktown Heights, New York. He believes that to perform truly transformative quantum calculations, the system must go beyond demonstrating a few logical qubits – instead, you need arrays of at least a 100 of them, that can perform more than 100 million quantum operations (108 QuOps). “The number of operations is the most important thing,” he says.
It sounds like a tall order, but Gambetta is confident that IBM will achieve these figures by 2029. By building on what has been achieved so far with error correction and mitigation, he feels “more confident than I ever did before that we can achieve a fault-tolerant computer.” Jerry Chow, previous manager of the Experimental Quantum Computing group at IBM, shares that optimism. “We have a real blueprint for how we can build [such a machine] by 2029,” he says (see figure 3).
Others suspect the breakthrough threshold may be a little lower: Steve Brierly, chief executive of Riverlane, believes that the first error-corrected quantum computer, with around 10 000 physical qubits supporting 100 logical qubits and capable of a million QuOps (a megaQuOp), could come as soon as 2027. Following on, gigaQuOp machines (109 QuOps) should be available by 2030–32, and teraQuOps (1012 QuOp) by 2035–37.
Platform independent
Error mitigation and error correction are just two of the challenges for developers of quantum software. Fundamentally, to develop a truly quantum algorithm involves taking full advantage of the key quantum-mechanical properties such as superposition and entanglement. Often, the best way to do that depends on the hardware used to run the algorithm. But ultimately the goal will be to make software that is not platform-dependent and so doesn’t require the user to think about the physics involved.
“At the moment, a lot of the platforms require you to come right down into the quantum physics, which is a necessity to maximize performance,” says Richard Murray of photonic quantum-computing company Orca. Try to generalize an algorithm by abstracting away from the physics and you’ll usually lower the efficiency with which it runs. “But no user wants to talk about quantum physics when they’re trying to do machine learning or something,” Murray adds. He believes that ultimately it will be possible for quantum software developers to hide those details from users – but Brierly thinks this will require fault-tolerant machines.
“In due time everything below the logical circuit will be a black box to the app developers”, adds Maragkou over at Riverlane. “They will not need to know what kind of error correction is used, what type of qubits are used, and so on.” She stresses that creating truly efficient and useful machines depends on developing the requisite skills. “We need to scale up the workforce to develop better qubits, better error-correction codes and decoders, write the software that can elevate those machines and solve meaningful problems in a way that they can be adopted.” Such skills won’t come only from quantum physicists, she adds: “I would dare say it’s mostly not!”
Yet even now, working on quantum software doesn’t demand a deep expertise in quantum theory. “You can be someone working in quantum computing and solving problems without having a traditional physics training and knowing about the energy levels of the hydrogen atom and so on,” says Ashley Montanaro, who co-founded the quantum software company Phasecraft.
On the other hand, insights can flow in the other direction too: working on quantum algorithms can lead to new physics. “Quantum computing and quantum information are really pushing the boundaries of what we think of as quantum mechanics today,” says Montanaro, adding that QEC “has produced amazing physics breakthroughs.”
Early adopters?
Once we have true error correction, Cuthbert at the UK’s NQCC expects to see “a flow of high-value commercial uses” for quantum computers. What might those be?
In this arena of quantum chemistry and materials science, genuine quantum advantage – calculating something that is impossible using classical methods alone – is more or less here already, says Chow. Crucially, however, quantum methods needn’t be used for the entire simulation but can be added to classical ones to give them a boost for particular parts of the problem.
Joint effort In June 2025, IBM in the US and Japan’s national research laboratory RIKEN, unveiled the first IBM Quantum System Two that is being used outside the US. It involved IBM’s 156-qubit IBM Heron quantum computing system (left) being paired with RIKEN’s supercomputer Fugaku (right) — one of the most powerful classical systems on Earth. The computers are linked through a high-speed network at the fundamental instruction level to form a proving ground for quantum-centric supercomputing. (Courtesy: IBM and RIKEN)
For example, last year researchers at IBM teamed up with scientists at several RIKEN institutes in Japan to calculate the minimum energy state for the iron sulphide cluster (4Fe-4S) at the heart of the bacterial nitrogenase enzyme that fixes nitrogen. This cluster is too big and complex to be accurately simulated using the classical approximations of quantum chemistry. The researchers used a combination of both quantum computing (with IBM’s 72-qubit Heron chip) and RIKEN’s Fugaku high performance computing (HPC). This idea of “improving classical methods by injecting quantum as a subroutine” is likely to be a more general strategy, says Gambetta. “The future of computing is going to be heterogeneous accelerators [of discovery] that include quantum.”
Likewise, Montanaro says that Phasecraft is developing “quantum-enhanced algorithms”, where a quantum computer is used, not to solve the whole problem, but just to help a classical computer in some way. “There are only certain problems where we know quantum computing is going to be useful,” he says. “I think we are going to see quantum computers working in tandem with classical computers in a hybrid approach. I don’t think we’ll ever see workloads that are entirely run using a quantum computer.” Among the first important problems that quantum machines will solve, according to Montanaro, are the simulation of new materials – to develop, for example, clean-energy technologies (see figure 4).
“For a physicist like me,” says Preskill, “what is really exciting about quantum computing is that we have good reason to believe that a quantum computer would be able to efficiently simulate any process that occurs in nature.”
3 Structural insights
(Courtesy: Phasecraft)
A promising application of quantum computers is simulating novel materials. Researchers from the quantum algorithms firm Phasecraft, for example, have already shown how a quantum computer could help simulate complex materials such as the polycrystalline compound LK-99, which was purported by some researchers in 2024 to be a room-temperature superconductor.
Using a classical/quantum hybrid workflow, together with the firm’s proprietary material simulation approach to encode and compile materials on quantum hardware, Phasecraft researchers were able to establish a classical model of the LK99 structure that allowed them to extract an approximate representation of the electrons within the material. The illustration above shows the green and blue electronic structure around red and grey atoms in LK-99.
Montanaro believes another likely near-term goal for useful quantum computing is solving optimization problems – both here and in quantum simulation, “we think genuine value can be delivered already in this NISQ era with hundreds of qubits.” (NISQ, a term coined by Preskill, refers to noisy intermediate-scale quantum computing, with relatively small numbers of rather noisy, error-prone qubits.)
One further potential benefit of quantum computing is that it tends to require less energy than classical high-performance computing, which is notoriously high. If the energy cost could be cut by even a few percent, it would be worth using quantum resources for that reason alone. “Quantum has real potential for an energy advantage,” says Chow. One study in 2020 showed that a particular quantum-mechanical calculation carried out on a HPC used many orders of magnitude more energy than when it was simulated on a quantum circuit. Such comparisons are not easy, however, in the absence of an agreed and well-defined metric for energy consumption.
Building the market
Right now, the quantum computing market is in a curious superposition of states itself – it has ample proof of principle, but today’s devices are still some way from being able to perform a computation relevant to a practical problem that could not be done with classical computers. Yet to get to that point, the field needs plenty of investment.
The fact that quantum computers, especially if used with HPC, are already unique scientific tools should establish their value in the immediate term, says Gambetta. “I think this is going to accelerate, and will keep the funding going.” It is why IBM is focusing on utility-scale systems of around 100 qubits or so and more than a thousand gate operations, he says, rather than simply trying to build ever bigger devices.
Montanaro sees a role for governments to boost the growth of the industry “where it’s not the right fit for the private sector”. One role of government is simply as a customer. For example, Phasecraft is working with the UK national grid to develop a quantum algorithm for optimizing the energy network. “Longer-term support for academic research is absolutely critical,” Montanaro adds. “It would be a mistake to think that everything is done in terms of the underpinning science, and governments should continue to support blue-skies research.”
The road ahead IBM’s current roadmap charts how the company plans on scaling up its devices to achieve a fault-tolerant device by 2029. Alongside hardware development, the firm will also focus on developing new algorithms and software for these devices. (Courtesy: IBM)
It’s not clear, though, whether there will be a big demand for quantum machines that every user will own and run. Before 2010, “there was an expectation that banks and government departments would all want their own machine – the market would look a bit like HPC,” Cuthbert says. But that demand depends in part on what commercial machines end up being like. “If it’s going to need a premises the size of a football field, with a power station next to it, that becomes the kind of infrastructure that you only want to build nationally.” Even for smaller machines, users are likely to try them first on the cloud before committing to installing one in-house.
According to Cuthbert , the real challenge in the supply-chain development is that many of today’s technologies were developed for the science community – where, say, achieving millikelvin cooling or using high-power lasers is routine. “How do you go from a specialist scientific clientele to something that starts to look like a washing machine factory, where you can make them to a certain level of performance,” while also being much cheaper, and easier to use?
But Cuthbert is optimistic about bridging this gap to get to commercially useful machines, encouraged in part by looking back at the classical computing industry of the 1970s. “The architects of those systems could not imagine what we would use our computation resources for today. So I don’t think we should be too discouraged that you can grow an industry when we don’t know what it’ll do in five years’ time.”
Montanaro too sees analogies with those early days of classical computing. “If you think what the computer industry looked like in the 1940s, it’s very different from even 20 years later. But there are some parallels. There are companies that are filling each of the different niches we saw previously, there are some that are specializing in quantum hardware development, there are some that are just doing software.” Cuthbert thinks that the quantum industry is likely to follow a similar pathway, “but more quickly and leading to greater market consolidation more rapidly.”
However, while the classical computing industry was revolutionized by the advent of personal computing in the 1970s and 80s, it seems very unlikely that we will have any need for quantum laptops. Rather, we might increasingly see apps and services appear that use cloud-based quantum resources for particular operations, merging so seamlessly with classical computing that we don’t even notice.
That, perhaps, would be the ultimate sign of success: that quantum computing becomes invisible, no big deal but just a part of how our answers are delivered.
In the first instalment of this two-part article, Philip Ball explores the latest developments in the quantum-computing industry
When a star rapidly accumulates gas and dust during its early growth phase, it’s called an accretion burst. Now, for the first time, astronomers have observed a planet doing the same thing. The discovery, made using the European Southern Observatory’s Very Large Telescope (VLT) and the James Webb Space Telescope (JWST), shows that the infancy of certain planetary-mass objects and that of newborn stars may share similar characteristics.
Like other rogue planets, Cha1107-7626 was known to be surrounded by a disk of dust and gas. When material from this disk spirals, or accretes, onto the planet, the planet grows.
What Almendros-Abad and colleagues discovered is that this process is not uniform. Using the VLT’s XSHOOTER and the NIRSpec and MIRI instruments on JWST, they found that Cha1107-7626 experienced a burst of accretion beginning in June 2025. This is the first time anyone has seen an accretion burst in an object with such a low mass, and the peak accretion rate of six billion tonnes per second makes it the strongest accretion episode ever recorded in a planetary-mass object. It may not be over, either. At the end of August, when the observing campaign ended, the burst was still ongoing.
An infancy similar to a star’s
The team identified several parallels between Cha1107-7626’s accretion burst and those that young stars experience. Among them were clear signs that gas is being funnelled onto the planet. “This indicates that magnetic fields structure the flow of gas, which is again something well known from stars,” explains Scholz. “Overall, our discovery is establishing interesting, perhaps surprising parallels between stars and planets, which I’m not sure we fully understand yet.”
The astronomers also found that the chemistry of the disc around the planet changed during accretion, with water being present in this phase even though it hadn’t been before. This effect has previously been spotted in stars, but never in a planet until now.
“We’re struck by quite how much the infancy of free-floating planetary-mass objects resembles that of stars like the Sun,” Jayawardhana says. “Our new findings underscore that similarity and imply that some objects comparable to giant planets form the way stars do, from contracting clouds of gas and dust accompanied by disks of their own, and they go through growth episodes just like newborn stars.”
The researchers have been studying similar objects for many years and earlier this year published results based on JWST observations that featured a small sample of planetary-mass objects. “This particular study is part of that sample,” Scholz tells Physics World, “and we obtained the present results because Victor wanted to look in detail at the accretion flow onto Cha1107-7626, and in the process discovered the burst.”
The researchers say they are “keeping an eye” on Cha1107-7626 and other such objects that are still growing because their environment is dynamic and unstable. “More to the point, we really don’t understand what drives these accretion events, and we need detailed follow-up to figure out the underlying reasons for these processes,” Scholz says.
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