Lifeboat Foundation

Year 2020 o.o!

Explorations into the nature of reality have been undertaken across the ages, and in the contemporary world, disparate tools, from gedanken experiments [1–4], experimental consistency checks [5,6] to machine learning and artificial intelligence are being used to illuminate the fundamental layers of reality [7]. A theory of everything, a grand unified theory of physics and nature, has been elusive for the world of Physics. While unifying various forces and interactions in nature, starting from the unification of electricity and magnetism in James Clerk Maxwell’s seminal work A Treatise on Electricity and Magnetism [8] to the electroweak unification by Weinberg-Salam-Glashow [9–11] and research in the direction of establishing the Standard Model including the QCD sector by Murray Gell-Mann and Richard Feynman [12,13], has seen developments in a slow but surefooted manner, we now have a few candidate theories of everything, primary among which is String Theory [14]. Unfortunately, we are still some way off from establishing various areas of the theory in an empirical manner. Chief among this is the concept of supersymmetry [15], which is an important part of String Theory. There were no evidences found for supersymmetry in the first run of the Large Hadron Collider [16]. When the Large Hadron Collider discovered the Higgs Boson in 2011-12 [17–19], there were results that were problematic for the Minimum Supersymmetric Model (MSSM), since the value of the mass of the Higgs Boson at 125 GeV is relatively large for the model and could only be attained with large radiative loop corrections from top squarks that many theoreticians considered to be ‘unnatural’ [20]. In the absence of experiments that can test certain frontiers of Physics, particularly due to energy constraints particularly at the smallest of scales, the importance of simulations and computational research cannot be underplayed. Gone are the days when Isaac Newton purportedly could sit below an apple tree and infer the concept of classical gravity from an apple that had fallen on his head. In today’s age, we have increasing levels of computational inputs and power that factor in when considering avenues of new research in Physics. For instance, M-Theory, introduced by Edward Witten in 1995 [21], is a promising approach to a unified model of Physics that includes quantum gravity. It extends the formalism of String Theory. There have been computational tools relating to machine learning that have lately been used for solving M-Theory geometries [22]. TensorFlow, a computing platform normally used for machine learning, helped in finding 194 equilibrium solutions for one particular type of M-Theory spacetime geometries [23–25].

Artificial intelligence has been one of the primary areas of interest in computational pursuits around Physics research. In 2020, Matsubara Takashi (Osaka University) and Yaguchi Takaharu (Kobe University), along with their research group, were successful in developing technology that could simulate phenomena for which we do not have the detailed formula or mechanism, using artificial intelligence [26]. The underlying step here is the creation of a model from observational data, constrained by the model being consistent and faithful to the laws of Physics. In this pursuit, the researchers utilized digital calculus as well as geometrical approach, such as those of Riemannian geometry and symplectic geometry.

The ability to transmit and manipulate, with minimal loss, the smallest unit of light—the photon—plays a pivotal role in optical communications as well as designs for quantum computers that would use light rather than electric charges to store and carry information.

Now, researchers at the National Institute of Standards and Technology (NIST) and their colleagues have connected, on a single microchip, quantum dots—artificial atoms that generate individual photons rapidly and on-demand when illuminated by a laser—with miniature circuits that can guide the light without significant loss of intensity.

To create the ultra-low-loss circuits, the researchers fabricated silicon-nitride waveguides—the channels through which the photons traveled—and buried them in silicon dioxide. The channels were wide but shallow, a geometry that reduced the likelihood that photons would scatter out of the waveguides. Encapsulating the waveguides in silicon dioxide also helped to reduce scattering.

Pity the poor astronomer. Biologists can hold examples of life in their hands. Geologists can fill specimen cabinets with rocks. Even physicists get to probe subatomic particles in laboratories built here on Earth. But across its millennia-long history, astronomy has always been a science of separation. No astronomer has stood on the shores of an alien exoplanet orbiting a distant star or viewed an interstellar nebula up close. Other than a few captured light waves crossing the great void, astronomers have never had intimate access to the environments that spur their passion.

Until recently, that is. At the turn of the 21st century, astrophysicists opened a new and unexpected era for themselves: large-scale laboratory experimentation. High-powered machines, in particular some very large lasers, have provided ways to re-create the cosmos, allowing scientists like myself to explore some of the universe’s most dramatic environments in contained, controlled settings. Researchers have learned to explode mini supernovas in their labs, reproduce environments around newborn stars, and even probe the hearts of massive and potentially habitable exoplanets.

How we got here is one of the great stories of science and synergy. The emergence of this new large-scale lab-based astrophysics was an unanticipated side effect of a much broader, more fraught, and now quite in-the-news scientific journey: the quest for nuclear fusion. As humanity has worked to capture the energy of the stars, we’ve also found a way to bring the stars down to Earth.

Water on the Moon has been a hot topic in the research world lately. Since its first unambiguous discovery back in 2008. Since then, findings of it have ramped up, with relatively high concentration levels being discovered, especially near the polar regions, particularly in areas constantly shrouded in shadow. Chang’e 5, China’s recent sample return mission, didn’t land in one of those permanently shadowed areas. Still, it did return soil samples that were at a much higher latitude than any that had been previously collected. Now, a new study shows that those soil samples contain water and that the Sun’s solar wind directly impacted that water.

The amount of water on the lunar surface varies widely both based on the time of the lunar day and the latitude it is located at. There is so much variability that the water content of the lunar soil can be 200 ppm higher or lower at different times of the day. With that much variability, it seems clear that the Sun plays a significant role in the hydrological cycle there is on the Moon.

Continue reading “The Solar Wind is Creating Water on the Surface of the Moon” »

Year 2021 face_with_colon_three

“This [study] shows us the evolution of the QGP and eventually [could] suggest how the early universe evolved in the first microsecond after the Big Bang,” said co-author You Zhou, an associate professor at the Niels Bohr Institute, University of Copenhagen in Denmark in an official statement.

“First the plasma that consisted of quarks and gluons was separated by the hot expansion of the universe. Then the pieces of quark reformed into so-called hadrons. A hadron with three quarks makes a proton, which is part of atomic cores. These cores are the building blocks that constitutes earth, ourselves and the universe that surrounds us.”

Continue reading “Primordial plasma from the Big Bang recreated in particle accelerator experiments” »

The Linac Coherent Light Source II X-ray laser will be so fast and bright that it will allow people to create movies of atoms moving inside molecules.

Do you want to get started with Quantum Machine Learning? Have a look at Hands-On Quantum Machine Learning With Python.

In a new study, scientists say they can explain dark matter by positing a particle that links to a fifth dimension.

(Updated version of the previous article.)

While the “warped extra dimension” (WED) is a trademark of a popular physics model first introduced in 1999, this research, published in The European Physical Journal C, is the first to cohesively use the theory to explain the long-lasting dark matter problem within particle physics.

Although dark matter is a central part of the standard cosmological model, it’s not without its issues. There continue to be nagging mysteries about the stuff, not the least of which is the fact that scientists have found no direct particle evidence of it.

Despite numerous searches, we have yet to detect dark matter particles. So some astronomers favor an alternative, such as modified Newtonian dynamics (MoND) or modified gravity model. And a new study of galactic rotation seems to support them.

The idea of MoND was inspired by galactic rotation. Most of the visible matter in a galaxy is clustered in the middle, so you’d expect that stars closer to the center would have faster orbital speeds than stars farther away, similar to the planets of our solar system. What we observe is that stars in a galaxy all rotate at about the same speed. The rotation curve is essentially flat rather than dropping off. The dark matter solution is that galaxies are surrounded by a halo of invisible matter, but in 1983 Mordehai Milgrom argued that our gravitational model must be wrong.