Lifeboat Foundation

Physicists at the National Ignition Facility are learning how to better control crushingly violent “shots”.

The surprising detection of light 200 times more powerful than previous observations from the nearby pulsar Vela indicates hidden physics around dead stars.

Hurricane Lee wasn’t bothering anyone in early September, churning far out at sea somewhere between Africa and North America. A wall of high pressure stood in its westward path, poised to deflect the storm away from Florida and in a grand arc northeast. Heading where, exactly? It was 10 days out from the earliest possible landfall—eons in weather forecasting—but meteorologists at the European Centre for Medium-Range Weather Forecasts, or ECMWF, were watching closely. The tiniest uncertainties could make the difference between a rainy day in Scotland or serious trouble for the US Northeast.

Typically, weather forecasters would rely on models of atmospheric physics to make that call. This time, they had another tool: a new generation of AI-based weather models developed by chipmaker Nvidia, Chinese tech giant Huawei, and Google’s AI unit DeepMind.

Scientists may have made a major breakthrough in the quest to produce limitless energy. According to a new study published in the journal American Chemical Society, scientists are looking deeper at a molecule known as azulene, which is a blue-light emitting molecule that seems to flout the fundamental rules of photochemistry.

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The more physicists use artificial intelligence and machine learning, the more important it becomes for them to understand why the technology works and when it fails.

The advent of ChatGPT, Bard, and other large language models (LLM) has naturally excited everybody, including the entire physics community. There are many evolving questions for physicists about LLMs in particular and artificial intelligence (AI) in general. What do these stupendous developments in large-data technology mean for physics? How can they be incorporated in physics? What will be the role of machine learning (ML) itself in the process of physics discovery?

Before I explore the implications of those questions, I should point out there is no doubt that AI and ML will become integral parts of physics research and education. Even so, similar to the role of AI in human society, we do not know how this new and rapidly evolving technology will affect physics in the long run, just as our predecessors did not know how transistors or computers would affect physics when the technologies were being developed in the early 1950s. What we do know is that the impact of AI/ML on physics will be profound and ever evolving as the technology develops.

If you’ve been watching the recent UAP reporting or the US Congressional Committee Hearing on UAP, you already know that we have military and civilian pilot eyewitness accounts in volume, as well as footage of incidents like the “tic-tac” live sighting in 2004. There are many more incidents whose video recordings are still classified and not yet available to the public. There are reports and testimony from career Navy and Air Force officials who’ve reported similar sightings. Comments such as the one made by Cmdr. David Fravor (Ret), made after the 2004 incident are common among experienced military pilots.

We don’t have the kind of physics understanding, now or back then, that would allow us the ability to do what we’re seeing these UAP do. — US Navy Cmdr. David Fravor (Ret)

In research that could jumpstart interest into an enigmatic class of materials known as quasicrystals, MIT scientists and colleagues have discovered a relatively simple, flexible way to create new atomically thin versions that can be tuned for important phenomena. In work reported in Nature they describe doing just that to make the materials exhibit superconductivity and more.

The research introduces a new platform for not only learning more about quasicrystals, but also exploring exotic phenomena that can be hard to study but could lead to important applications and new physics. For example, a better understanding of superconductivity, in which electrons pass through a material with no resistance, could allow much more efficient electronic devices.

The work brings together two previously unconnected fields: quasicrystals and twistronics. The latter was pioneered at MIT only about five years ago by Pablo Jarillo-Herrero, the Cecil and Ida Green Professor of Physics at MIT and corresponding author of the paper.

John Baez University of California, Riverside The 20th century was, arguably, the century of physics. While there was immense progress on so-called fundam…

CERN’s data store has now crossed the remarkable capacity threshold of one exabyte, meaning that CERN has one million terabytes of disk space ready for data!

CERN’s data store not only serves LHC physics data, but also the whole spectrum of experiments and services needing online data management. This data capacity is provided using 111 000 devices, predominantly hard disks along with an increasing fraction of flash drives. Having such a large number of commodity devices means that component failures are common, so the store is built to be resilient, using different data replication methods. These disks, most of which are used to store physics data, are orchestrated by CERN’s open-source software solution, EOS, which was created to meet the LHC’s extreme computing requirements.

“We reached this new all-time record for CERN’s storage infrastructure after capacity extensions for the upcoming LHC heavy-ion run,” explains Andreas Peters, EOS project leader. “It is not just a celebration of data capacity, it is also a performance achievement, thanks to the reading rate of the combined data store crossing, for the first time, the one terabyte per second (1 TB/s) threshold.”