Toggle light / dark theme

CERN discovers antihyperhelium-4, the heaviest antimatter particle to date.

Scientists at CERN’s Large Hadron Collider have discovered the heaviest antimatter particle ever observed: antihyperhelium-4.

This exotic particle, the antimatter counterpart of hyperhelium-4, contains two antiprotons, an antineutron, and an antilambda particle. The breakthrough offers insights into the extreme conditions of the early universe and sheds light on the baryon asymmetry problem — why our universe is dominated by matter despite matter and antimatter being created in equal amounts during the Big Bang.

The discovery was made using lead-ion collisions at the LHC, recreating the hyper-hot environment of the newborn universe. Machine learning models analyzed the data, identifying antihyperhelium-4 particles and precisely measuring their masses.

While the experiment confirmed that matter and antimatter are created in equal portions, the mystery of what tipped the cosmic balance remains unsolved. With ongoing upgrades to the LHC, more groundbreaking discoveries in antimatter research could be on the horizon.


Illustration of the production of antihyperhelium-4 (a bound state of two antiprotons, an antineutron and an antilambda) in lead–lead collisions. (Image: J. Ditzel with AI-assistance) Collisions between heavy ions at the Large Hadron Collider (LHC) create quark–gluon plasma, a hot and dense state of matter that is thought to have filled the Universe around one millionth of a second after the Big Bang. Heavy-ion collisions also create suitable conditions for the production of atomic nuclei and exotic hypernuclei, as well as their antimatter counterparts, antinuclei and antihypernuclei. Measurements of these forms of matter are important for various purposes, including helping to understand the formation of hadrons from the plasma’s constituent quarks and gluons and the matter–antimatter asymmetry seen in the present-day Universe.

In a bold new theory, researchers from Microsoft, Brown University, and other institutions suggest that the universe might be capable of teaching itself how to evolve. Their study, published on the preprint server arXiv, proposes that the physical laws we observe today may have emerged through a gradual learning process, akin to Darwinian natural selection or self-learning algorithms in artificial intelligence.

This radical idea challenges traditional cosmology by imagining a primitive early universe where physical laws like gravity were far simpler or even static. Over time, these laws “learned” to adapt into more complex forms, enabling the structured universe we observe today. For instance, gravity might have initially lacked distinctions between celestial bodies like Earth and the Moon. This progression mirrors how adaptable traits in biology survive through natural selection.

By the end of 2024, artificial intelligence (AI) and machine learning (ML) had established themselves as the main transformative forces behind recent technological advancements in healthcare. A report by Silicon Valley Bank states that in 2024, the amount of VC investment in health AI in the U.S. was expected to reach $11.1 billion, the highest number since 2021.

In my experience, the main driver behind the AI investment and adoption craze is the measurable value technology offers healthcare providers. A 2023 National Bureau of Economic Research study indicates that integrating AI can save the U.S. healthcare system up to $360 billion annually. A 2023 survey by the AMA shows that physicians see AI as a way to reduce the administrative burden of documentation (54%) and improve workflow efficiency (69%).

But do these positive changes reflect on the quality of care, and do patients benefit from AI and ML-powered solutions? In this article, I share my take on the transformative potential of AI and ML in the modern care delivery process.