Lifeboat Foundation

Primordial black holes older than the big bang could rewrite cosmology by providing evidence for a previous universe. It’s a wild idea, but some physicists think we’ve got a chance of finding them.

By Bernard Carr

Basically we are nearing if not already in the age of infinity. What this means is that full automation can be realized imagine not needing really to work to survive bit we could thrive and work on harder things like new innovative things. Basically we could automate all work so we could automate the planet to get to year million or year infinity maybe even days or months once realized full automation could lead to more even for physics where one could finally find the theory of everything or even master algorithm. 😀 Really in the age of infinity anything could be possible from solving impossible problems to nearly anything.

These assistants won’t just ease the workload, they’ll unleash a wave of entrepreneurship.

A white dwarf star can explode as a supernova when its mass exceeds the limit of about 1.4 solar masses. A team led by the Max Planck Institute for Extraterrestrial Physics (MPE) in Garching and involving the University of Bonn has now found a binary star system in which matter flows onto the white dwarf from its companion.

The system was found due to bright, so-called super-soft X-rays, which originate in the nuclear fusion of the overflowed gas near the surface of the white dwarf. The unusual thing about this source is that it is helium and not hydrogen that overflows and burns. The measured luminosity suggests that the mass of the white dwarf is growing more slowly than previously thought possible, which may help to understand the number of supernovae caused by exploding white dwarfs. The results have been published in the journal Nature.

Exploding white dwarfs are not only considered the main source of iron in the universe, they are also an important tool for cosmology. As so-called Type Ia supernovae (SN Ia), they all become roughly equally bright, allowing astrophysics a precise determination of the distance of their host galaxies.

A team of Rutgers University scientists dedicated to pinpointing the primordial origins of metabolism – a set of core chemical reactions that first powered life on Earth – has identified part of a protein that could provide scientists clues to detecting planets on the verge of producing life.

The research, published on March 10 in the journal Science Advances.

<em>Science Advances</em> is a peer-reviewed, open-access scientific journal that is published by the American Association for the Advancement of Science (AAAS). It was launched in 2015 and covers a wide range of topics in the natural sciences, including biology, chemistry, earth and environmental sciences, materials science, and physics.

Just a couple of years earlier, in 1963, New Zealand mathematician Roy Kerr found a solution to Einstein’s equation for a rotating black hole. This was a “game changer for black holes,” Giorgi noted in a public lecture given at the virtual 2022 International Congress of Mathematicians. Rotating black holes were much more realistic astrophysical objects than the non-spinning black holes that Karl Schwarzschild had solved the equations for.

“Physicists really had believed for decades that the black hole region was an artifact of symmetry that was appearing in the mathematical construction of this object but not in the real world,” Giorgi said in the talk. Kerr’s solution helped establish the existence of black holes.

In a nearly 1,000-page paper, Giorgi and colleagues used a type of “proof by contradiction” to show that Kerr black holes that rotate slowly (meaning they have a small angular momentum relative to their mass) are mathematically stable. The technique entails assuming the opposite of the statement to be proved, then discovering an inconsistency. That shows that the assumption is false. The work is currently undergoing peer review. “It’s a long paper, so it’s going to take some time,” Giorgi says.

Argentinian-born mathematician Luis Caffarelli has won the 2023 Abel Prize — one of the most coveted awards in mathematics — for his work on equations that are important for describing physical phenomena, such as how ice melts and fluids flow. He is the first person born in South America to win the award.

Caffarelli’s results “are technically virtuous, covering many different areas of mathematics and its applications”, says a statement by Helge Holden, a mathematician at the Norwegian University of Science and Technology in Trondheim who chairs the Abel Committee.

The winner says receiving the news was an emotional moment, because “it shows that people have some appreciation for me and for my science”.

“This new advancement would also allow the encoding of data on ultrafast laser pulses.”

A team of international physicists, led by the University of Arizona, was able to switch a light signal optically at attosecond rates in order to achieve hitherto unreachable data transfer speeds: one quintillionth of a second is an attosecond.

Continue reading “Record-breaking optical switch study paves way for ultrafast electronics” »

PBS Member Stations rely on viewers like you. To support your local station, go to: http://to.pbs.org/DonateSPACE

Sign Up on Patreon to get access to the Space Time Discord!
https://www.patreon.com/pbsspacetime.

Continue reading “What If We Live in a Superdeterministic Universe?” »

One of the oldest tools in computational physics — a 200-year-old mathematical technique known as Fourier analysis — can reveal crucial information about how a form of artificial intelligence called a deep neural network learns to perform tasks involving complex physics like climate and turbulence modeling, according to a new study.

The discovery by mechanical engineering researchers at Rice University is described in an open-access study published in the journal PNAS Nexus, a sister publication of the Proceedings of the National Academy of Sciences.

“This is the first rigorous framework to explain and guide the use of deep neural networks for complex dynamical systems such as climate,” said study corresponding author Pedram Hassanzadeh. “It could substantially accelerate the use of scientific deep learning in climate science, and lead to much more reliable climate change projections.”