Lifeboat Foundation

Jefferson Lab tests a next-generation data acquisition scheme

Nuclear physics experiments worldwide are becoming ever more data intensive as researchers probe ever more deeply into the heart of matter. To get a better handle on the data, nuclear physicists are now turning to artificial intelligence and machine learning methods to help sift through the torrent in real-time.

A recent test of two systems that employ such methods at the U.S. Department of Energy’s Thomas Jefferson National Accelerator Facility found that they can, indeed, enable real-time processing of raw data. Such systems could result in a streamlined data analysis process that is faster and more efficient, while also keeping more of the original data for future analysis than conventional systems. An article describing this work was recently published in The European Physical Journal Plus .

Writing in Nature Communications, a team led by Dr. Marcelo Lozada-Hidalgo based at the National Graphene Institute (NGI) used graphene as an electrode to measure both the electrical force applied on water molecules and the rate at which these break in response to such force. The researchers found that water breaks exponentially faster in response to stronger electrical forces.

The researchers believe that this fundamental understanding of interfacial water could be used to design better catalysts to generate hydrogen fuel from water. This is an important part of the U.K.’s strategy towards achieving a net zero economy. Dr. Marcelo Lozada-Hidalgo said, “We hope that the insights from this work will be of use to various communities, including physics, catalysis, and interfacial science and that it can help design better catalysts for green hydrogen production.”

A water molecule consists of a proton and a hydroxide ion. Dissociating it involves pulling these two constituent ions apart with an electrical force. In principle, the stronger one pulls the water molecule apart, the faster it should break. This important point has not been demonstrated quantitatively in experiments.

The work can lead to unprecedented calculations of polarons in large systems.

Sending rockets into space requires sacrificing expensive equipment, burning massive amounts of fuel, and risking potential catastrophe. So in the space race of the 21st century, some engineers are abandoning rockets for something more exciting: elevators. What would it take to build such a structure? Fabio Pacucci explores the physics behind modern space elevators. [Directed by Tjoff Koong Studios, narrated by Addison Anderson].

Elegant experiments with entangled light have laid bare a profound mystery at the heart of reality.

That’s because as a white dwarf draws material away from its hydrogen-burning partner, the stolen gas follows the star’s magnetic field lines in a big, curving arc toward its new home. And in the process, it drains energy from the stars’ whirling dance (so do the gravitational waves produced by their rotation). When that happens, both stars fall toward the shared center of gravity they’re orbiting. Closer orbits also mean shorter orbits, so it takes the stars less time to complete a single lap.

And the closer the stars get, the stronger the gravitational waves they produce, which drains away more energy, so they fall even closer together. By the time they’re close enough to complete an orbit in just a handful of minutes, the donor star has usually run out of hydrogen. That’s why the really close, fast-orbiting cataclysmic binaries tend to be a white dwarf and a helium-burning star.

This is perhaps the strangest thing about the arrow of time: “It only lasts for a little while,” says Carroll.

It’s very hard to picture what might happen if the arrow of time eventually vanishes. “When we think we produce heat in our neurons,” says Rovelli. “Thinking is a process in which the neuron needs entropy to work. Our sense of time passing is just what entropy does to our brain.”

Continue reading “Why does time go forwards, not backwards?” »

From the tiniest subatomic scales to the grandest cosmic ones, solving any of these puzzles could unlock our understanding of the Universe.

Deep generative models are a popular data generation strategy used to generate high-quality samples in pictures, text, and audio and improve semi-supervised learning, domain generalization, and imitation learning. Current deep generative models, however, have shortcomings such as unstable training objectives (GANs) and low sample quality (VAEs, normalizing flows). Although recent developments in diffusion and scored-based models attain equivalent sample quality to GANs without adversarial training, the stochastic sampling procedure in these models is sluggish. New strategies for securing the training of CNN-based or ViT-based GAN models are presented.

They suggest backward ODEsamplers (normalizing flow) accelerate the sampling process. However, these approaches have yet to outperform their SDE equivalents. We introduce a novel “Poisson flow” generative model (PFGM) that takes advantage of a surprising physics fact that extends to N dimensions. They interpret N-dimensional data items x (say, pictures) as positive electric charges in the z = 0 plane of an N+1-dimensional environment filled with a viscous liquid like honey. As shown in the figure below, motion in a viscous fluid converts any planar charge distribution into a uniform angular distribution.

A positive charge with z 0 will be repelled by the other charges and will proceed in the opposite direction, ultimately reaching an imaginary globe of radius r. They demonstrate that, in the r limit, if the initial charge distribution is released slightly above z = 0, this rule of motion will provide a uniform distribution for their hemisphere crossings. They reverse the forward process by generating a uniform distribution of negative charges on the hemisphere, then tracking their path back to the z = 0 planes, where they will be dispersed as the data distribution.