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Auroras on Ganymede and Earth share striking similarities

New observations of Ganymede reveal a striking similarity between the auroras on the largest moon in the solar system and those on Earth. The international team of astrophysicists, led by researchers from the University of Liège, has produced new results indicating that, despite different conditions, the fundamental physical processes that generate auroras are common to different celestial bodies, and not just planets.

A team of astrophysicists from the Laboratory of Atmospheric and Planetary Physics (LPAP) has observed for the first time the fine details of the auroras on Ganymede, the only moon in the solar system to have its own intrinsic magnetic field, similar to that of Earth. The observation of auroras is a cornerstone of space weather analysis, as it provides a comprehensive view of the characteristics and effects of space particle precipitation into atmospheres.

For centuries, humanity has witnessed a diffuse and changing glow that occasionally illuminates the night sky with red, green, purple and blue lights—known as the “aurora.” Auroras are typically observed at polar latitudes, although we have just passed the peak of the 11-year solar cycle, which is producing many instances of intense auroras at mid-latitudes.

Physicists dream up ‘spacetime quasicrystals’ that could underpin the universe

Spacetime obeys a rule known as Lorentz symmetry means that something is unchanged whether you’re sitting still or moving at close to the speed of light. For example, the laws of physics respect Lorentz symmetry: They don’t change for fast moving observers. Lorentz symmetry doesn’t hold for previously known quasicrystals, or for normal crystals either: An ant sitting still would observe a different structure than would a near light-speed ant. In relativity, observers traveling at high speeds observe an apparent shortening of objects, and that distorts the materials’ structure.

But the new spacetime quasicrystals obey Lorentz symmetry. They would appear the same to an ant sitting still as to one on a speeding rocket. The researchers mathematically formulated their quasicrystals by taking a four-dimensional slice through a grid of points in higher dimensions and projecting those points onto the slice. The slice has a slope that is an irrational number — one that can’t be written as a fraction of two whole numbers, such as pi. The irrational slope means the slice never directly intersects the points on the grid, and that helps produce the structure that never repeats.

Quasicrystals are a mathematical concept that shows up in the structure of real materials, but the concept could appear elsewhere. “The spacetime that we live in could be a quasicrystal,” says Sotiris Mygdalas of the Perimeter Institute in Waterloo, Canada, a coauthor of the study.

First 3D map of Uranus’s upper atmosphere created

“This is the first time we’ve been able to see Uranus’s upper atmosphere in three dimensions,” said Paola Tiranti.


What does the atmosphere of Uranus look like? This is what a recent study published in Geophysical Research Letters hopes to address as a team of researchers from the United States and United Kingdom investigated new data about the upper atmosphere of Uranus. This study has the potential to help scientists better understand the atmosphere of Uranus and establish new methods for exploring gas giant atmospheres.

For the study, the researchers analyzed data obtained from NASA’s James Webb Space Telescope (JWST) in January 2025 with its powerful infrared instruments. The motivation for the study was due to the lack of understanding of Uranus’s upper atmosphere, whose temperature and composition have remained elusive. The researchers focused on a region of the upper atmosphere known as the ionosphere, which is the region that interacts with the space environment and produces the auroras.

In the end, the researchers not only created the first 3D map of Uranus’s upper atmosphere, but found that atmospheric temperatures peak between 3,000 to 4,000 kilometers (1,864 to 2,485 miles) above the planet while the density of charged particles, where space radiation interacts with the atmosphere, peak at approximately 1,000 kilometers (621 miles) above the planet. Additionally, the researchers were surprised to discover that the charged particle density was weaker than longstanding models had predicted.

How Alien Would Aliens Behave?

Forget tentacles—what matters is the mind. We explore how alien behavior might emerge from evolution, culture, and technology, and why our biggest first contact risk may be misunderstanding.

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Credits:
How Would Aliens Behave?
July 13, 2025; Episode 732
Written, Produced & Narrated by: Isaac Arthur.
Select imagery/video supplied by Getty Images.
Music Courtesy of Epidemic Sound http://epidemicsound.com/creator.
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How space settlement can challenge consumerism

Apparently public interest in extraterrestrial settlement is steadily increasing.


It is impossible to think that anyone involved in thinking about the future of humanity in space can fail to be alarmed by the extent of overemphasis on technical requirements versus the lack of consideration of other key issues.

Space settlement should be developed by following or avoiding certain sets of ideas, doctrines, and philosophical guidelines. In other words, space settlement is in need of an ideology in order to be put in practice. The qualifications of such an ideology can enable us to foresee what a human society would look like, what its social structure and moral values would be, and ultimately whether or not they could survive.

This article is devoted to casting light on how the predominant ideology of consumerism will be challenged by human colonies in space, and in which ways extraterrestrial human culture might affect or reshape our way of thinking here on Earth.

Cosmic curveball: Distant system challenges planet-formation theory

An international team of astronomers has discovered a distant planetary system that challenges long-standing theories of how planets form. Across our galaxy, astronomers routinely observe a characteristic pattern in planetary systems: rocky planets orbiting close to their host star with gas giants farther away. Our own solar system follows this rule, with the inner planets: Mercury, Venus, Earth and Mars, composed of rock and iron, and the outer planets: Jupiter, Saturn, Uranus and Neptune being predominantly gaseous.

This pattern stems from a well-established theory of planet formation: intense radiation from the host star strips away gas accumulated by close-in planets, leaving behind bare rocky bodies. While further from the star, cooler conditions allow thick atmospheres to build, forming gaseous planets.

But a newly discovered planetary system orbiting the star LHS 1903 breaks this rule. The findings are published in Science.

‘Learn-to-Steer’ method improves AI’s ability to understand spatial instructions

Researchers from the Department of Computer Science at Bar-Ilan University and from NVIDIA’s AI research center in Israel have developed a new method that significantly improves how artificial intelligence models understand spatial instructions when generating images—without retraining or modifying the models themselves. Image-generation systems often struggle with simple prompts such as “a cat under the table” or “a chair to the right of the table,” frequently placing objects incorrectly or ignoring spatial relationships altogether. The Bar-Ilan research team has introduced a creative solution that allows AI models to follow such instructions more accurately in real time.

The new method, called Learn-to-Steer, works by analyzing the internal attention patterns of an image-generation model, effectively offering insight into how the model organizes objects in space. A lightweight classifier then subtly guides the model’s internal processes during image creation, helping it place objects more precisely according to user instructions. The approach can be applied to any existing trained model, eliminating the need for costly retraining.

The results show substantial performance gains. In the Stable Diffusion SD2.1 model, accuracy in understanding spatial relationships increased from 7% to 54%. In the Flux.1 model, success rates improved from 20% to 61%, with no negative impact on the models’ overall capabilities.

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