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Category: physics – Page 208

A team of international scientists, including researchers from The Australian National University (ANU), have unveiled the largest number of gravitational waves ever detected.

The discoveries will help solve some of the most complex mysteries of the Universe, including the building blocks of matter and the workings of space and time.

The global team’s study, published on ArXiv, made 35 new detections of gravitational waves caused by pairs of merging or neutron stars and black holes smashing together, using the LIGO and Virgo observatories between November 2019 and March 2020.

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In a new report now published in Science, primary authors Zhen Zhu, Michal Papaj, and an international research team in physics, materials science, and condensed matter at the Jiao Tong University, China, Massachusetts Institute of Technology, U.S., and the Chinese Academy of Sciences discovered the Fermi surface of supercurrent-induced quasiparticles in a superconducting system for the first time. This discovery comes 50 years after the initial theoretical prediction was made by physicist Peter Fulde and revealed the impact of the finite Cooper pair momentum on the quasiparticle spectrum. In condensed matter physics, Cooper pairs are a pair of electrons with opposite spins loosely bound due to electron-lattice interactions. Superconductivity is based on their condensation to Bosonic states at low temperatures. The interplay of superconductivity and magnetic fields leads to the phenomenon of a ‘segmented Fermi surface. A leading author of this work, MIT Professor of Physics Liang Fu, outlined the significance of this discovery.

Supercurrent flow in a superconductor

Physicists assume that a sufficiently large supercurrent can close the energy gap in a superconductor and create gapless quasiparticles via the Doppler shift of quasiparticle energy. This is facilitated by the finite momentum of Cooper pairs in the presence of supercurrent flow in a superconductor, where the shift in Cooper pair momentum can result in a Doppler shift. In this work, Zhu et al. used quasiparticle interference to image the field-controlled Fermi surface of bismuth telluride (Bi2Te3) thin films proximitized by the superconductor niobium diselenide (NbSe2). A small applied in-plane magnetic field induced a screening supercurrent, which led to finite momentum pairing on the topological surface states of Bi2Te3. The scientists identified distinct interference patterns to indicate a gapless superconducting state with a segmented Fermi surface to reveal the strong impact of the finite Cooper pair momentum on the quasiparticle spectrum.

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The Big Bang still happened a very long time ago, but it wasn’t the beginning we once supposed it to be.

Where did all this come from? In every direction we care to observe, we find stars, galaxies, clouds of gas and dust, tenuous plasmas, and radiation spanning the gamut of wavelengths: from radio to infrared to visible light to gamma rays. No matter where or how we look at the universe, it’s full of matter and energy absolutely everywhere and at all times. And yet, it’s only natural to assume that it all came from somewhere. If you want to know the answer to the biggest question of all — the question of our cosmic origins — you have to pose the question to the universe itself, and listen to what it tells you.

Today, the universe as we see it is expanding, rarifying (getting less dense), and cooling. Although it’s tempting to simply extrapolate forward in time, when things will be even larger, less dense, and cooler, the laws of physics allow us to extrapolate backward just as easily. Long ago, the universe was smaller, denser, and hotter. How far back can we take this extrapolation? Mathematically, it’s tempting to go as far as possible: all the way back to infinitesimal sizes and infinite densities and temperatures, or what we know as a singularity. This idea, of a singular beginning to space, time, and the universe, was long known as the Big Bang.

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Our universe started with the big bang. But only for the right definition of “our universe”. And of “started” for that matter. In fact, probably the Big Bang is nothing like what you were taught.
A hundred years ago we discovered the beginning of the universe. Observations of the retreating galaxies by Edwin Hubble and Vesto Slipher, combined with Einstein’s then-brand-new general theory of relativity, revealed that our universe is expanding. And if we reverse that expansion far enough – mathematically, purely according to Einstein’s equations, it seems inevitable that all space and mass and energy should once have been compacted into an infinitesimally small point – a singularity. It’s often said that the universe started with this singularity, and the Big Bang is thought of as the explosive expansion that followed. And before the Big Bang singularity? Well, they say there was no “before”, because time and space simply didn’t exist. If you think you’ve managed to get your head around that bizarre notion then I have bad news. That picture is wrong. At least, according to pretty much every serious physicist who studies the subject. The good news is that the truth is way cooler, at least as far as we understand it.

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It’s one of the most fascinating aspects of the natural world: shapes repeat over and over. The branches of a tree extending into the sky look much the same as blood vessels extending through a human lung, if upside-down. The largest mammal, the whale, is a scaled-up version of the smallest, the shrew. Recent research even suggests the structure of the human brain resembles that of the entire universe. It’s everywhere you look, really. Nature reuses its most successful shapes.

Theoretical physicist Geoffrey West of the Santa Fe Institute in New Mexico is concerned with fundamental questions in physics, and there are few more fundamental than this one: why does nature continually reuse the same non-linear shapes and structures from the smallest scale to the very largest? In a new Big Think video (see above), West explains that the scaling laws at work are nothing less than “the generic universal mathematical and physical properties of the multiple networks that make an organism viable and allow it to develop and grow.”

“I think it’s one of the more remarkable properties of life, actually,” West added.

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We’ll soon be capable of building self-replicating robots. This will not only change humanity’s future but reshape the galaxy as we know it.

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American astrophysicists have used the Decadal Survey (DS)—also called Astro 2020 and produced by the National Academies of Science—to recommend a space telescope capable of photographing potentially habitable worlds.

The report recommends that a flagship space observatory will need a six-meter mirror to “provide an appropriate balance between scale and feasibility.”

An eight-meter aperture telescope of the scale of LUVOIR-B would be unlikely to launch before the late 2040… See more.

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DeepMind is mostly known for its work in deep reinforcement learning, especially in mastering complicated games and predicting protein structures. Now, it is taking its next step in robotics research.

According to a blog post on DeepMind’s website, the company has acquired the rigid-body physics simulator MuJoCo and has made it freely available to the research community. MuJoCo is now one of several open-source platforms for training artificial intelligence agents used in robotics applications. Its free availability will have a positive impact on the work of scientists who are struggling with the costs of robotics research. It can also be an important factor for DeepMind’s future, both as a science lab seeking artificial general intelligence and as a business unit of one of the largest tech companies in the world.

Simulation platforms are a big deal in robotics. Training and testing robots in the real world is expensive and slow. Simulated environments, on the other hand, allow researchers to train multiple AI agents in parallel and at speeds that are much faster than real life. Today, most robotics research teams carry out the bulk of training their AI models in simulated environments. The trained models are then tested and further fine-tuned on real physical robots.

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