Scientists are stunned: how a 3,000-year-old disk was crafted with such astonishing precision - Futura, le média qui explore le monde

Voyager imaging team member joins The Reg's Kettle podcast to talk about his role in the mission as probe approaches light day's distance from Earth It's been nearly half a century since the Voyager probes launched from Earth on a mission to explore the outer solar system, and they're still running. You can listen to the latest episode of The Kettle on this page, Spotify, Apple Podcasts, or YouTube. You can also subscribe on those platforms to be notified when a new episode goes live.

Scientists have discovered a surprising ability of light that allows it to develop spinning patterns as it moves through empty space, without relying on special lenses, mirrors, or materials. The finding reveals that light possesses a hidden geometric structure that can influence its behavior in ways researchers previously thought were difficult to achieve. The discovery comes from an international team led by researchers at the University of East Anglia (UEA) in the United Kingdom, working with scientists in South Africa. Their experiments demonstrate that carefully prepared light can be controlled through its own internal geometry, offering a new approach to manipulating how light spins and twists.

Currently, about 16,000 satellites are orbiting Earth, providing everything from GPS service and weather reports to phone service in remote locations to people across the globe. In contrast, the highest-ever number of active satellites in orbit around Mars, one of the best-explored other planets in our solar system, is eight.

The only thing we can be sure of about the future is that it will be absolutely fantastic. If what I say seems reasonable to you, then I have failed completely, declared Arthur C. Clarke back in 1964 on the BBC. In the legendary BBC Horizon documentaries more than 60 years ago, Clarke made several predictions—including some about the Internet that proved uncannily accurate.

AcknowledgementsThe authors thank UCLA's Institute for Digital Research and Education (IDRE), Michigan Technological University's MRI computing clusters, and the National Energy Research Scientific Computing Center (NERSC) for providing computational resources. The authors thank Abhishek Keripale for assistance with the development of later versions of the CrFeCoNi machine-learning models. The authors used GPT-5 (OpenAI) for language editing and proofreading and subsequently reviewed and edited all content. FundingThis work was primarily supported by the U. S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), under Award DE-SC0023432, which provided primary support for the overall project conception and integrated KS-DFT–ML investigations. The University of California National Laboratory Fees Research Program under Grant L25CR9003 supported data generation and post-processing at UCLA (ASB). The National Science Foundation under Award 2442313 supported data-pruning and dimensionality analyses at Michigan Technological University (SG, SH). This research also used resources of the National Energy Research Scientific Computing Center (NERSC), a U. S.