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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.

Collective vibrations unlock fast ion flow in superionic crystals

In the race to develop safer, faster-charging solid-state batteries and more efficient thermoelectric conversion technologies, engineers and scientists have long faced a fundamental challenge: how to ensure ions move through hard, solid materials as quickly as they do in liquids?

A team led by Prof. Zhou Yanguang, Associate Professor in the Department of Mechanical and Aerospace Engineering (MAE) at The Hong Kong University of Science and Technology (HKUST), discovered a novel mechanism for rapid ion transport in solids, opening new avenues for materials design.

The study shows that the ionic transport is governed by collective dynamics. The results were published in the journal Physical Review Letters, titled “Fast Ionic Transport Governed by Collective Vibrational Dynamics.”

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.

Why ‘football’ beats ‘shamrock’ when your brain is dismantling every word at lightning speed

Before you even know what a word means, your brain is already playing a rapid-fire game of linguistic LEGO. Discover how our minds secretly dissect words, piece by orthographic piece, in the blink of an eye.

Imagine catching a flash of the word football on a screen. Before you even register its meaning (“a game” or “a ball”), your brain may have already parsed it into “foot” + “ball.” A clever new experiment used red-and-blue anaglyph glasses and split-second word flashes to probe this. It found that real compound words (like football) are recognized much faster than lookalikes (like shamrock), suggesting our eyes and brain latch onto word form almost instantly.

In the lab, volunteers wore 3D-style red/blue glasses while words appeared for just 60 milliseconds under a mask. Each word was painted half red and half blue, splitting it either at a meaningful break or in the middle of a syllable. For example, “FOOT” might be blue and “BALL” red, or vice versa, sending “foot” to one hemisphere and “ball” to the other. Participants then quickly reported if what they saw was a real word or a made-up one.

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