'RocketGPT' among first deployments of government-compliant ChatGPT designed for sensitive aerospace data
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China launched the ChinaSat-9C communications satellite Friday to replace an aging, foreign-built predecessor and boost domestic broadcasting capabilities.
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Airbus executives say they are making good progress to turn around the company’s space business unit even as they consider combining it with those at two other European companies.
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The goal is creating what DoD calls "enterprise satcom" — a virtualized, software-defined network that could automatically reroute communications.
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Independent measurements of the charge radius of the helium-3 nucleus using two different methods have yielded significantly different results – prompting a re-evaluation of underlying theory to reconcile them. The international CREMA Collaboration used muonic helium-3 ions to determine the radius, whereas a team in the Netherlands used a quantum-degenerate gas of helium-3 atoms.
The charge radius is a statistical measure of how far the electric charge of a particle extends into space. Both groups were mystified by the discrepancy in the values – which hints at physics beyond the Standard Model of particle physics. However, new theoretical calculations inspired by the results may have already resolved the discrepancy.
Both groups studied the difference between the charge radii of the helium-3 and helium-4 nuclei. CREMA used muonic helium ions, in which the remaining electrons replaced by muons. Muons are much more massive than electrons, so they spend more time near the nucleus – and are therefore more sensitive to the charge radius.
Muonic atoms have spectra at much shorter wavelengths than normal atoms. This affects values such as the Lamb shift. This is the energy difference in the 2S1/2 and 2P1/2 atomic states, which are split by interactions with virtual photons and vacuum polarization. This is most intense near the nucleus. More importantly, a muon in an S orbital becomes more sensitive to the finite size of the nucleus.
In 2010, CREMA used the charge radius of muonic hydrogen to conclude that the charge radius of the proton is significantly smaller than the current accepted value. The same procedure was then used with muonic helium-4 ions. Now, CREMA has used pulsed laser spectroscopy of muonic helium-3 ions to extract several key parameters including the Lamb shift and used them to calculate the charge radius of muonic helium-3 nuclei. They then calculated the difference with the charge radius in helium-4. The value they obtained was 15 times more accurate than any previously reported.
Meanwhile, at the Free University of Amsterdam in the Netherlands, researchers were taking a different approach, using conventional helium-3 atoms. This has significant challenges, because the effect of the nucleus on electrons is much smaller. However, it also means that an electron affects the nucleus it measures less than does a muon, which mitigates a source of theoretical uncertainty.
The Amsterdam team utilized the fact that the 2S triplet state in helium is extremely long-lived. ”If you manage to get the atom up there, it’s like a new ground state, and that means you can do laser cooling on it and it allows very efficient detection of the atoms,” explains Kjeld Eikema, one of the team’s leaders after its initial leader Wim Vassen died in 2019. In 2018, the Amsterdam group created an ultracold Bose–Einstein condensate (BEC) of helium-4 atoms in the 2S triplet state in an optical dipole trap before using laser spectroscopy to measure the ultra-narrow transition between the 2S triplet state and the higher 2S singlet state.
In the new work, the researchers turned to helium-3, which does not form a BEC but instead forms a degenerate Fermi gas. Interpreting the spectra of this required new discoveries itself. “Current theoretical models are insufficiently accurate to determine the charge radii from measurements on two-electron atoms,” Eikema explains. However, “the nice thing is that if you measure the transition directly in one isotope and then look at the difference with the other isotope, then most complications from the two electrons are common mode and drop out,” he says. This can be used to the determine the difference in the charge radii.
The researchers obtained a value that was even more precise than CREMA’s and larger by 3.6σ. The groups could find no obvious explanation for the discrepancy. “The scope of the physics involved in doing and interpreting these experiments is quite massive,” says Eikema; “a comparison is so interesting, because you can say ‘Well, is all this physics correct then? Are electrons and muons the same aside from their mass? Did we do the quantum electrodynamics correct for both normal atoms and muonic atoms? Did we do the nuclear polarization correctly?’” The results of both teams are described in Science (CREMA, Amsterdam).
While these papers were undergoing peer review, the work attracted the attention of two groups of theoretical physicists – one led by Xiao-Qiu Qi f the Wuhan Institute of Physics and Mathematics in China, and the other by Krzysztof Pachucki of the University of Warsaw in Poland. Both revised the calculation of the hyperfine structure of helium-3, finding that incorporating previously neglected higher orders into the calculation produced an unexpectedly large shift.
“Suddenly, by plugging this new value into our experiment – ping! – our determination comes within 1.2σ of theirs,” says Eikema; “which is a triumph for all the physics involved, and it shows how, by showing there’s a difference, other people think, ‘Maybe we should go and check our calculations,’ and it has improved the calculation of the hyperfine effect.” In this manner the ever improving experiments and theory calculations continue to seek the limits of the Standard Model.
Xiao-Qiu Qi and colleagues describe their calculations in Physical Review Research, while Pachucki’s team have published in Physical Review A.
Eikema adds “Personally I would have adjusted the value in our paper according to these new calculations, but Science preferred to keep the paper as it was at the time of submission and peer review, with an added final paragraph to explain the latest developments.”
Theoretical physicist Marko Horbatsch at Canada’s York University is impressed by the experimental results and bemused by the presentation. “I would say that their final answer is a great success,” he concludes. “There is validity in having the CREMA and Eikema work published side-by-side in a high-impact journal. It’s just that the fact that they agree should not be confined to a final sentence at the end of the paper.”
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Computational physicist Jose Milovich of the Lawrence Livermore National Laboratory (LLNL) and colleagues have been awarded the 2025 Plasma Physics and Controlled Fusion (PPCF) Outstanding Paper Prize for their computational research on capsule implosions during laser fusion.
The work – Understanding asymmetries using integrated simulations of capsule implosions in low gas-fill hohlraums at the National Ignition Facility – is an important part of understanding the physics at the heart of inertial confinement fusion (ICF).
Fusion is usually performed via two types of plasma confinement. Magnetic involves using magnetic fields to hold stable a plasma of deuterium-tritium (D-T), while inertial confinement uses rapid compression, usually by lasers, to create a confined plasma for a short period of time.
The award-winning work was based on experiments carried out at the National Ignition Facility (NIF) based in California, which is one of the leading fusion centres in the world.
During NIF’s ICF experiments, a slight imbalance of the laser can induce motion of the hot central core of an ignition capsule, which contains the D-T fuel. This effect results in a reduced performance.
Experiments at NIF in 2018 found that laser imbalances alone, however, could not account for the motion of the capsule. The simulations carried out by Milovich and colleagues demonstrated that other factors were at play such as non-concentricity of the layers of the material surrounding the D-T fuel as well as “drive perturbations” induced by diagnostic windows on the implosion.
Changes made following the team’s findings then helped towards the recent demonstration of “energy breakeven” at NIF in December 2022.
Awarded each year, the PPCF prize aims to highlight work of the highest quality and impact published in the journal. The award was judged on originality, scientific quality and impact as well as being based on community nominations and publication metrics. The prize will be presented at the 51st European Physical Society Conference on Plasma Physics in Vilnius, Lithuania, on 7–11 July.
The journal is now seeking nominations for next year’s prize, which will focus on papers in magnetic confinement fusion.
Below, Milovich talks to Physics World about prize, the future of fusion and what advice he has for early-career researchers.
What does winning the 2025 PPCF Outstanding Paper Prize mean to you and for your work?
The award is an incredible honour to me and my collaborators as a recognition of the detailed work required to make inertial fusion in the laboratory a reality and the dream of commercial fusion energy a possibility. The paper presented numerical confirmation of how seemingly small effects can significantly impact the performance of fusion targets. This study led to target modifications and revised manufacturing specifications for improved performance. My collaborators and I would like to deeply thank PPCF for granting us this award.
What excites you about fusion?
Nuclear fusion is the process that powers the stars, and achieving those conditions in the laboratory is exciting in many ways. It is an interesting scientific problem in its own right and it is an incredibly challenging engineering problem to handle the extreme conditions required for successful energy production. This is an exciting time since the possibility of realizing this energy source became tangibly closer two years ago when NIF successfully demonstrated that more energy can be released from D-T fusion than the laser energy delivered to the target.
What are your thoughts on the future direction of ICF and NIF?
While the challenges ahead to make ICF commercially feasible are daunting, we are well positioned to address them by developing new technologies and innovative target configurations. Applications of artificial intelligence to reactor plant designs, optimized operations, and improvements on plasma confinement could potentially lead to improved designs at a fraction of the cost. The challenges are many but the potential for providing a clean and inexhaustible source of energy for the benefit of mankind is invigorating.
What advice would you give to people thinking about embarking on a career in fusion?
This is an exciting time to get involved in fusion. The latest achievements at NIF have shown that fusion is possible. There are countless difficulties to overcome, making it an ideal time to devote one’s career in this area. My advice is to get involved now since, at this early stage, any contribution will have a major and lasting impact on mankind’s future energy needs.
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The world recently watched an argument unfold on X between Elon Musk and Donald Trump. It was a surreal exchange, featuring one of the richest men ever to have lived […]
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The European Space Agency has signed an agreement regarding potential use of Orbital Reef as it refines its strategy for use of commercial space stations.
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Chinese launch startup Landspace carried out a breakthrough static fire test Friday as it builds towards an orbital launch attempt with its Zhuque-3 rocket.
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The European Space Agency and European Commission say their relationship is closer than ever as they embark on a new satellite program.
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Artificial intelligence (AI) has the potential to generate a sea change in the practice of radiology, much like the introduction of radiology information system (RIS) and picture archiving and communication system (PACS) technology did in the late 1990s and 2000s. However, AI-driven software must be accurate, safe and trustworthy, factors that may not be easy to assess.
Machine learning software is trained on databases of radiology images. But these images might lack the data or procedures needed to prevent algorithmic bias. Such algorithmic bias can cause clinical errors and performance disparities that affect a subset of the analyses that the AI performs, unintentionally disadvantaging certain groups of patients.
A multinational team of radiology informaticists, biomedical engineers and computer scientists has identified potential pitfalls in the evaluation and measurement of algorithmic bias in AI radiology models. Describing their findings in Radiology, the researchers also suggest best practices and future directions to mitigate bias in three key areas: medical image datasets; demographic definitions; and statistical evaluations of bias.
The medical image datasets used for training and evaluation of AI algorithms are reflective of the population from which they are acquired. It is natural that a dataset acquired in a country in Asia will not be representative of the population in a Nordic country, for example. But if there’s no information available about the image acquisition location, how might this potential source of bias be determined?
Lead author Paul Yi, of St. Jude Children’s Research Hospital in Memphis, TN, and coauthors advise that many existing medical imaging databases lack a comprehensive set of demographic characteristics, such as age, sex, gender, race and ethnicity. Additional potential confounding factors include the scanner brand and model, the radiology protocols used for image acquisition, radiographic views acquired, the hospital location and disease prevalence. In addition to incorporating these data, the authors recommend that raw image data are collected and shared without institution-specific post-processing.
The team advise that generative AI, a set of machine learning techniques that generate new data, provides the potential to create synthetic imaging datasets with more balanced representation of both demographic and confounding variables. This technology is still in development, but might provide a solution to overcome pitfalls related to measurement of AI biases in imperfect datasets.
Radiology researchers lack consensus with respect to how demographic variables should be defined. Observing that demographic categories such as gender and race are self-identified characteristics informed by many factors, including society and lived experiences, the authors advise that concepts of race and ethnicity do not necessarily translate outside of a specific society and that biracial individuals reflect additional complexity and ambiguity.
They emphasize that ensuring accurate measurements of race- and/or ethnicity-based biases in AI models is important to enable accurate comparison of bias evaluations. This not only has clinical implications, but is also essential to prevent health policies being established in error from erroneous AI-derived findings, which could potentially perpetuate pre-existing inequities.
The researchers define bias in the context of demographic fairness and how it reflects differences in metrics between demographic groups. However, establishing consensus on the definition of bias is complex, because bias can have different clinical and technical meanings. They point out that in statistics, bias refers to a discrepancy between the expected value of an estimated parameter and its true value.
As such, the radiology speciality needs to establish a standard notion of bias, as well as tackle the incompatibility of fairness metrics, the tools that measure whether a machine learning model treats certain demographic groups differently. Currently there is no universal fairness metric that can be applied to all cases and problems, and the authors do not think there ever will be one.
The different operating points of predictive AI models may result in different performance that could lead to potentially different demographic biases. These need to be documented, and thresholds should be included in research and by commercial AI software vendors.
The authors suggest some key courses of action to mitigate demographic biases in AI in radiology:
Yi and co-lead collaborator Jeremias Sulam, of Hopkins BME, Whiting School of Engineering, tell Physics World that their assessment of pitfalls and recommendations to mitigate demographic biases reflect years of multidisciplinary discussion. “While both the clinical and computer science literature had been discussing algorithmic bias with great enthusiasm, we learned quickly that the statistical notions of algorithmic bias and fairness were often quite different between the two fields,” says Yi.
“We noticed that progress to minimize demographic biases in AI models is often hindered by a lack of effective communication between the computer science and statistics communities and the clinical world, radiology in particular,” adds Sulam.
A collective effort to address the challenges posed by bias and fairness is important, notes Melissa Davis of Yale School of Medicine, in an accompanying editorial in Radiology. “By fostering collaboration between clinicians, researchers, regulators and industry stakeholders, the healthcare community can develop robust frameworks that prioritize patient safety and equitable outcomes,” she writes.
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France would more than double its stake in Eutelsat to nearly 30% as part of a $1.56 billion capital raise backed by multiple shareholders, bolstering the French operator’s plans to refresh its OneWeb constellation amid Starlink’s growing dominance.
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Gilbert, AZ – Moog Inc. (NYSE: MOG.A and MOG.B), a worldwide designer, manufacturer and systems integrator of high-performance precision motion and fluid controls and control systems, is now accepting orders […]
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Portal Space Systems will create a second factory to scale up production of high-performance in-space vehicles as it gears up for initial test flights in 2026.
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In this week's episode of Space Minds, we explore how microgravity accelerates aging—and guest Dr. Nadia Maroouf shares her insights on the phenomenon and what she’s doing to help protect astronauts.
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Last week, Physics World’s Matin Durrani boarded a ferry in Hamburg that was bound for Helgoland – an archipelago in the North Sea about 70 km off the north-west coast of Germany.
It was a century ago in Helgoland that the physicist Werner Heisenberg devised the mathematical framework that underpins our understanding of quantum physics.
Matin was there with some of the world’s leading quantum physicists for the conference Helgoland 2025: 100 Years of Quantum Mechanics – which celebrated Heisenberg’s brief stay in Helgoland.
He caught up with three eminent physicists and asked them to reflect on Heisenberg’s contributions to quantum mechanics and look forward to the next 100 years of quantum science and technology. They are Tracy Northup at the University of Vienna; Michelle Simmons of the University of New South Wales, Sydney; and Peter Zoller of the University of Innsbruck.
• Don’t miss the 2025 Physics World Quantum Briefing, which is free to read via this link.
This article forms part of Physics World‘s contribution to the 2025 International Year of Quantum Science and Technology (IYQ), which aims to raise global awareness of quantum physics and its applications.
Stayed tuned to Physics World and our international partners throughout the next 12 months for more coverage of the IYQ.
Find out more on our quantum channel.
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Last week, I looked at the faces of our guests at our moon landing event and watched as awe, wonder and hopefulness transformed into disappointment. Moments before the expected touch […]
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