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Category: particle physics

Quantum tunnelling

In physics, quantum tunnel ling, barrier penetration, or simply tunnel ling is a quantum mechanical phenomenon in which an object such as an electron or atom passes through a potential energy barrier that, according to classical mechanics, should not be passable due to the object not having sufficient energy to pass or surmount the barrier.

Tunnelling is a consequence of the wave nature of matter and quantum indeterminacy. The quantum wave function describes the states of a particle or other physical system and wave equations such as the Schrödinger equation describe their evolution. In a system with a short, narrow potential barrier, a small part of wavefunction can appear outside of the barrier representing a probability for tunnel ling through the barrier.

Since the probability of transmission of a wave packet through a barrier decreases exponentially with the barrier height, the barrier width, and the tunnel ling particle’s mass, tunnel ling is seen most prominently in low-mass particles such as electrons tunnel ling through atomically narrow barriers. However tunnel ling has been observed with protons and even atoms and tunnel ling has been used to explain physical effects with particles this large.

Listening to the sun reveals previously hidden changes to solar cycle

Internal changes due to the sun’s “active biorhythm” have become increasingly “skin-deep” over the past four solar activity cycles, according to a new study.

Publishing its findings in Monthly Notices of the Royal Astronomical Society, an international team led by the University of Birmingham reveals solar magnetic activity is being squeezed into an increasingly shallow layer just below the visible surface, signposting long-term changes to the sun’s active behavior.

Solar activity rises and falls in 11‑year cycles, producing solar flares, and ejections of highly charged particles and coronal mass ejections that give rise to space weather. This activity, and its cyclic variation, has its origins in the sun’s interior, in processes that regenerate and reorganize the sun’s magnetic field.

After 100 years, scientists finally uncover hidden rule behind cosmic rays

A mysterious new cosmic pattern discovered by the DAMPE space telescope may finally crack the century-old mystery of cosmic rays. Scientists studying mysterious ultra-powerful cosmic rays have uncovered a surprising hidden pattern that could finally help explain where these particles come from. Using the DAMPE space telescope, researchers found that cosmic ray particles—from tiny protons to heavy iron nuclei—all begin fading away more sharply at the exact same point, hinting at a universal rule governing their behavior across the galaxy.

For more than 100 years, scientists have been trying to understand cosmic rays, incredibly powerful particles that travel across the universe at extreme energies. Despite decades of research, many questions about where they come from and how they are accelerated remain unanswered. Now, researchers working with the DAMPE (Dark Matter Particle Explorer) space telescope have uncovered an important new clue. Their findings, published in Nature, reveal a common feature shared by these mysterious particles and could help scientists better understand their origins.

Cosmic rays are the highest energy particles ever observed in nature. They carry far more energy than particles produced by even the most advanced accelerators on Earth. Scientists believe they are created by some of the universe’s most violent events, including supernova explosions, jets from black holes, and pulsars.

ATLAS observes new Bc meson excited state

Protons and neutrons—the building blocks of matter—belong to a huge class of particles called hadrons. Hadrons are composite particles made of quarks that are bound together by the strong force. They are classified into two groups: baryons, which consist of three quarks (like protons and neutrons), and mesons, which are formed by a quark–antiquark pair.

Despite decades of study, many aspects of the strong force remain poorly understood, particularly the way it binds quarks together inside hadrons. Mesons made of heavy quarks—such as charm or bottom quarks—can provide an important laboratory for testing theoretical descriptions of these effects. Of particular interest to physicists are Bc+ mesons, as they contain two types of heavy quarks: a charm quark and a bottom antiquark (b̅c).

In a new result presented at the Large Hadron Collider Physics 2026 conference, physicists from the ATLAS Collaboration report the first observation of a particle with properties consistent with the Bc*+ meson, the lowest excited Bc+ meson. The paper is available on the arXiv preprint server.

Data-driven model captures dynamics of turbulence at scale

Whether the dust borne on the violent winds of a tornado or the sugar grains in a swirled cup of coffee, the behavior of particles carried along in turbulence is subject to some similarities—all of them difficult to predict at scale. As described in a recent publication in the Proceedings of the National Academy of Sciences, a research team led by Los Alamos National Laboratory scientists has developed a first-of-its-kind machine learning framework that models chaotic particle motions in a turbulent flow.

“Modeling turbulence is a big, open problem, and it’s probably the hardest problem in classical physics,” said Daniel Livescu, Los Alamos scientist and one of the leaders of the work. “A subset of that challenge is modeling particle motions within turbulence. To meet that challenge, our artificial intelligence approach offers an innovative theoretical construct tested with a real-world application.”

The team has developed and applied the first data-driven, auto-regressive machine learning framework to capture the dynamics of turbulence at scale. The research demonstrates that machine learning can overcome longstanding barriers in modeling chaotic particle motions.

Teaching thermodynamic laws to AI unlocks a polymer modeling challenge

For more than half a century, materials scientists have struggled with how to simulate the complexity of polymer materials. An individual chain can comprise tens of thousands of atoms, a melt or composite contains billions, and the properties engineers actually care about, such as how an adhesive grips a surface, how a self-assembling block copolymer locks into a nanostructure, or how a biopolymer film stretches without tearing, emerge only over length and time scales that forcible atomistic simulation cannot reach.

The standard workaround is coarse-graining: replacing groups of atoms with simpler mesoscopic particles so the model is fast enough to run. The catch is that this compression almost always sacrifices physics. Conventional coarse-grained polymer models can usually reproduce equilibrium structure or large-scale dynamics, but rarely both, and they routinely fail to capture the entropic and viscous forces that govern how polymers actually flow, relax, and dissipate energy. Those are the forces that dictate mechanical performance, and they are the forces that traditional machine-learning approaches, despite their flexibility, also tend to break.

A research paper recently published in Proceedings of the National Academy of Sciences introduces a new machine-learning framework that lets coarse-grained models achieve both at once. A team from Carnegie Mellon University and the University of Pennsylvania has built an AI architecture that learns coarse-grained dynamics directly from data, whether simulated or experimental, while being mathematically incapable of violating the laws of thermodynamics.

Large Hadron Collider detects strange particle behavior that could rewrite physics

Scientists working at CERN’s Large Hadron Collider may be seeing the strongest hints yet of physics beyond the Standard Model — the decades-old theory that explains the fundamental particles and forces of the universe. By studying incredibly rare particle transformations called “penguin decays,” researchers found behavior that doesn’t fully match theoretical predictions, raising the possibility that unknown particles or forces are influencing the results.

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