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Magnetic fields may significantly complicate how scientists interpret gravitational wave signals from neutron star mergers, a new study has revealed. These collisions, where two super-dense stellar remnants merge, have long offered astrophysicists a way to probe matter under extreme pressure. The results from the University of Illinois Urbana-Champaign and the University of Valencia reveal that robust magnetic fields form more complex and lengthy patterns in gravitational waves, thereby making it harder to decipher the inner workings of neutron stars. Results could doom post-merger signal interpretation strategies and the equation of states of dense matter as scientists prepare to observe the next generation of gravitational wave observatories.

Magnetic Fields Found to Distort Frequency Signals in Neutron Star Mergers

As per the study published in Physical Review Letters, the researchers simulated general relativistic magnetohydrodynamics — how the strength and arrangement of magnetic fields affect the frequency signals from the remnants left behind after a merger. They went represent real-world conditions by applying two different equations of state (EoS) for neutron stars, different magnetic field configurations, and several mass combinations.

According to lead researcher Antonios Tsokaros, the magnetic field can cause frequency shifts that can misidentify scientists into misattributing them as indications of other physical phenomena like phase transitions or quark-hadron crossover.

The discoveries also imply that scientists need to be cautious about how they interpret signals from neutron-star mergers, lest they slip into assuming how they form. They found that strong magnetic fields can change the emitted signals’ typical oscillation frequency, shifting them from what they should be and from what was predicted by one or another of the competing equations of state at play within these ferocious events.

They also discovered that in the most straightforward type of galaxy mergers they considered in their simulations, the magnetic field became overly amplified so that a greater proportion of the remnants of the merger are more likely to produce further gravitational wave emissions.

Magnetic Fields Hold Key to Unlocking Secrets of Neutron Star Mergers

Neutron stars are what remains of massive stars that have collapsed, and they contain matter so dense that a full teaspoon would weigh billions of tonnes. They have thermodynamic properties that are determined by the EoS and magnetic fields, some orders of magnitude stronger than those that one can produce in a human laboratory.

These extreme features also make neutron stars useful for probing the laws of physics under intense pressure and magnetism. Ever since it was detected in both gravitational waves and gamma rays in 2017, the scientific community has been buzzing about research on neutron star mergers, leading to ever-growing numbers of studies related to these types of mergers.

Professor Milton Ruiz also warns that it would be a mistake to misinterpret observations in the future without considering the effects of the magnetic fields. Higher-resolution simulations are needed, the researchers said, to refine our understanding of how magnetic fields shape cosmic happenings, and endeavours like the Einstein Telescope and Cosmic Explorer loom on the horizon.

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Climate Satellite MethaneSAT Fails After Just One Year in Orbit

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Climate Satellite MethaneSAT Fails After Just One Year in Orbit

One of the world’s most advanced satellites for detecting methane and other gases that contribute to the warming of the planet has gone dark and stopped communicating with ground-based controllers just over a year after being launched into orbit. Created by the nonprofit Environmental Defense Fund (EDF), the satellite — estimated to cost as much as $88 million — hitched a ride into space on a SpaceX rocket in March 2024. It was charged with monitoring methane leaks from oil and gas operations, and then making the data available to policymakers and scientists through open access. But on June 20, contact with the satellite was lost, and attempts to recover it have failed. EDF officially reported on July 1 that MethaneSAT has lost power and appears unlikely to recover.

MethaneSAT Failure Marks Setback for Climate Transparency Despite Data Gains and Global Support

As per a statement released by EDF, MethaneSAT’s failure came despite multiple recovery attempts. The satellite was constructed to lift the veil off methane’s invisible, weighty impact on global warming. It is nowhere near as common as carbon dioxide, but over a timescale of, say, a century, it is 20 to 30 times more efficient at trapping heat in the atmosphere than carbon dioxide. That makes its emissions a prime target in the effort to minimize the risks of global warming. MethaneSAT was developed to independently corroborate industrial methane reports, especially those from fossil fuel extraction. The loss of the satellite is a remarkable setback for transparency in climate science and monitoring of emissions worldwide.

Yet mission operators are hopeful that data already collected will have far-reaching effects. EDF emphasized that insights from MethaneSAT’s year in orbit will continue to be processed and made public in the coming months. The mission included backing from 10 partners such as Harvard University, the New Zealand Space Agency, BAE Systems, Google, and the Bezos Earth Fund.

Officials called MethaneSAT a bold and needed move to hold our climate accountable. Although the mission was cut short, it signaled one of the largest joint efforts between science, advocacy, and technology to battle climate change. “To succeed in meeting the climate challenge, we need bold action and fearless innovation,” EDF mentioned, describing the satellite as “at the vanguard of science.”
MethaneSAT’s brief history highlights the difficulty — and importance — of deploying space-based instruments to try and combat climate change. As other missions get ready to blaze the same trail, the data and experience this little spacecraft provided will influence the future of Earth observation.

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New Interstellar Comet 3I/ATLAS Speeds Through Solar System

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New Interstellar Comet 3I/ATLAS Speeds Through Solar System

A newly confirmed interstellar comet is making a rare passage through our solar system — and skywatchers can catch it live online tonight. The object, now called 3I/ATLAS, is just the third interstellar visitor ever detected after the well-known ‘Oumuamua (2017) and 2I/Borisov (2019). The comet was so fresh when first detected on July 1 by the ATLAS telescope in Chile that it hadn’t even been given a name yet; the Minor Planet Center has it listed as “3I,” the “I” standing for interstellar. Tonight’s webcast will kick off at 6 p.m. EDT (2200 GMT) from the Virtual Telescope Project’s virtual observing facilities in Italy.

Interstellar Comet 3I/ATLAS Speeds Toward Sun at 68 km/s, Offers Rare Study Opportunity

As per a report by Space.com, 3I/ATLAS was detected as a faint object displaying subtle cometary features, including a marginal coma and a short tail. Currently located 4.5 astronomical units (AU) from the sun — about 670 million kilometers (416 million miles) — the comet is faint at magnitude 18.8, making it invisible to amateur telescopes. The interstellar object is traveling at an astonishing pace of 68 kilometers per second (152,000 mph) relative to the sun, but NASA officials say it poses no danger to Earth.

It was imaged by the Virtual Telescope Project on July 2, showing the comet as a point of light within the trailing background stars — a sure indication that it is indeed moving through space. 3I/ATLAS should brighten a little as it approaches the sun, particularly when it gets closest, or its perihelion, on Oct. 30, when it swings within 1.4 astronomical units of the sun or Mars’ orbit.

The close pass by this interstellar visitor is a rare chance for astronomers to study the materials and dynamics outside our solar system. 3I/ATLAS, which is racing along at a frenetic pace on an elliptical orbit, may also support research into how these objects change as they sit in different stellar environments.

After disappearing behind the sun in late fall, 3I/ATLAS is projected to return to observational reach in early December. Researchers anticipate further analysis then, expanding our understanding of these rare visitors that traverse the galaxy — and occasionally, pass through our celestial neighborhood.

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Quantum Breakthrough: CSIRO Uses 5-Qubit Model to Enhance Chip Design

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Quantum Breakthrough: CSIRO Uses 5-Qubit Model to Enhance Chip Design

Researchers at Australia’s CSIRO have achieved a world-first demonstration of quantum machine learning in semiconductor fabrication. The quantum-enhanced model outperformed conventional AI methods and could reshape how microchips are designed. The team focused on modeling a crucial—but hard to predict—property called “Ohmic contact” resistance, which measures how easily current flows where metal meets a semiconductor.

They analysed 159 experimental samples from advanced gallium nitride (GaN) transistors (known for high power/high-frequency performance). By combining a quantum processing layer with a final classical regression step, the model extracted subtle patterns that traditional approaches had missed.

Tackling a difficult design problem

According to the study, the CSIRO researchers first encoded many fabrication variables (like gas mixtures and annealing times) per device and used principal component analysis (PCA) to shrink 37 parameters down to the five most important ones. Professor Muhammad Usman – who led the study – explains they did this because “the quantum computers that we currently have very limited capabilities”.

Classical machine learning, by contrast, can struggle when data are scarce or relationships are nonlinear. By focusing on these key variables, the team made the problem manageable for today’s quantum hardware.

A quantum kernel approach

To model the data, the team built a custom Quantum Kernel-Aligned Regressor (QKAR) architecture. Each sample’s five key parameters were mapped into a five-qubit quantum state (using a Pauli-Z feature map), enabling a quantum kernel layer to capture complex correlations.

The output of this quantum layer was then fed into a standard learning algorithm that identified which manufacturing parameters mattered most. As Usman says, this combined quantum–classical model pinpoints which fabrication steps to tune for optimal device performance.

In tests, the QKAR model beat seven top classical algorithms on the same task. It required only five qubits, making it feasible on today’s quantum machines. CSIRO’s Dr. Zeheng Wang notes that the quantum method found patterns classical models might miss in high-dimensional, small-data problems.

To validate the approach, the team fabricated new GaN devices using the model’s guidance; these chips showed improved performance. This confirmed that the quantum-assisted design generalized beyond its training data.

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