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Category: particle physics

Machine learning algorithm fully reconstructs LHC particle collisions

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly and precisely than traditional methods, helping physicists better understand LHC data. The paper has been submitted to the European Physical Journal C and is currently available on the arXiv preprint server.

Each proton–proton collision at the LHC sprays out a complex pattern of particles that must be carefully reconstructed to allow physicists to study what really happened. For more than a decade, CMS has used a particle-flow (PF) algorithm, which combines information from the experiment’s different detectors, to identify each particle produced in a collision. Although this method works remarkably well, it relies on a long chain of hand-crafted rules designed by physicists.

The new CMS machine-learning-based particle-flow (MLPF) algorithm approaches the task fundamentally differently, replacing much of the rigid hand-crafted logic with a single model trained directly on simulated collisions. Instead of being told how to reconstruct particles, the algorithm learns how particles look in the detectors, like how humans learn to recognize faces without memorizing explicit rules.

Measuring chaos: Researchers quantify the quantum butterfly effect

For the first time, researchers in China have accurately quantified how chaos increases in a quantum many-body system as it evolves over time. Combining experiments and theory, a team led by Yu-Chen Li at the University of Science and Technology of China showed that the level of chaos grows exponentially when time reversal is applied to these systems—matching predictions of their extreme sensitivity to errors. The research has been published in Physical Review Letters.

The butterfly effect is a well-known expression of chaos theory. It describes how a complex system can quickly become unpredictable as it evolves: make just a few small errors when specifying the system’s starting conditions, and it may look completely different from your calculations a short time later.

This effect is especially relevant in many-body quantum systems, where entanglement creates intricate webs of interconnection between particles—even in relatively small systems. As the system evolves, information about its initial state becomes increasingly dispersed across these connections.

Record-breaking photons at telecom wavelengths

A team of researchers from the University of Stuttgart and the Julius-Maximilians-Universität Würzburg led by Prof. Stefanie Barz (University of Stuttgart) has demonstrated a source of single photons that combines on-demand operation with record-high photon quality in the telecommunications C-band—a key step toward scalable photonic quantum computation and quantum communication. “The lack of a high-quality on-demand C-band photon source has been a major problem in quantum optics laboratories for over a decade—our new technology now removes this obstacle,” says Prof. Stefanie Barz.

The key: Identical photons on demand In everyday life, distinguishing features may often be desirable. Few want to be exactly like everyone else. When it comes to quantum technologies, however, complete indistinguishability is the name of the game. Quantum particles such as photons that are identical in all their properties can interfere with each other—much as in noise-canceling headphones, where sound waves that are precisely inverted copies of the incoming noise cancel out the background.

When identical photons are made to act in synchrony, then the probability that certain measurement outcomes occur can be either boosted or decreased. Such quantum effects give rise to powerful new phenomena that lie at the heart of emerging technologies such as quantum computing and quantum networking. For these technologies to become feasible, high-quality interference between photons is essential.

Silicon quantum processor detects single-qubit errors while preserving entanglement

Quantum computers are alternative computing devices that process information, leveraging quantum mechanical effects, such as entanglement between different particles. Entanglement establishes a link between particles that allows them to share states in such a way that measuring one particle instantly affects the others, irrespective of the distance between them.

Quantum computers could, in principle, outperform classical computers in some optimization and computational tasks. However, they are also known to be highly sensitive to environmental disturbances (i.e., noise), which can cause quantum errors and adversely affect computations.

Researchers at the International Quantum Academy, Southern University of Science and Technology, and Hefei National Laboratory have developed a new approach to detect these errors in a silicon-based quantum processor. This error detection strategy, presented in a paper published in Nature Electronics, was found to successfully detect quantum errors in silicon qubits, while also preserving entanglement after their detection.

Using light to probe fractional charges in a fractional Chern insulator

In some quantum materials, which are materials governed by quantum mechanical effects, interactions between charged particles (i.e., electrons) can prompt the creation of quasiparticles called anyons, which carry only a fraction of an electron’s charge (i.e., fractional charge) and fractional quantum statistics.

A well-known phenomenon characterized by the emergence of anyons is the so-called fractional quantum Hall effect (FQHE). This effect can emerge in two-dimensional (2D) electron gases under strong magnetic fields and is marked by quantum states in which electrons strongly interact with each other.

Recent studies showed that a similar effect can also arise in the absence of magnetic fields, known as fractional quantum anomalous Hall (FQAH) effect, in quantum phases of matter fractional Chern insulators (FCIs). The FQAH effect was realized for the first time using bilayer twisted molybdenum ditelluride (tMoTe₂)—a moiré superlattice that has a characteristic lattice pattern and a slight twist angle between constituent layers.

Amaterasu Particle That Broke Physics Has Finally Been Explained

A mysterious, extremely energetic particle, known as the Amaterasu particle, was detected coming from a distant region of space, and scientists have proposed explanations for its origin, potentially tracing it back to a starburst galaxy like Messier 82 ##

## Questions to inspire discussion.

Understanding Ultra-High Energy Cosmic Rays.

🔬 Q: What makes the Amaterasu particle exceptionally powerful? A: The Amaterasu particle detected in Utah in 2021 carries energy 40 million times higher than anything produced on Earth, equivalent to a baseball traveling at 100 km/h compressed into a single subatomic particle, making it one of the most energetic particles ever detected.

Solving the Origin Mystery.

🎯 Q: Where did scientists determine the Amaterasu particle actually originated? A: A 2026 study by Max Planck Institute scientists using approximate Bayesian computation and 3D magnetic field simulations traced the particle’s origin to a starburst galaxy like Messier 82, located 12 million light-years away, rather than the initially suspected local void with only six known galaxies.

Continue reading “Amaterasu Particle That Broke Physics Has Finally Been Explained” | >

Scientists Continue to Trace the Origin of the Mysterious “Amaterasu” Cosmic Ray Particle

When the Amaterasu particle entered Earth’s atmosphere, the TAP array in Utah recorded an energy level of more than 240 exa-electronvolts (EeV). Such particles are exceedingly rare and are thought to originate in some of the most extreme cosmic environments. At the time of its detection, scientists were not sure if it was a proton, a light atomic nucleus, or a heavy (iron) atomic nucleus. Research into its origin pointed toward the Local Void, a vast region of space adjacent to the Local Group that has few known galaxies or objects.

This posed a mystery for astronomers, as the region is largely devoid of sources capable of producing such energetic particles. Reconstructing the energy of cosmic-ray particles is already difficult, making the search for their sources using statistical models particularly challenging. Capel and Bourriche addressed this by combining advanced simulations with modern statistical methods (Approximate Bayesian Computation) to generate three-dimensional maps of cosmic-ray propagation and their interactions with magnetic fields in the Milky Way.

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