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Scientists have found the most convincing evidence yet of a dark matter particle from an underground experiment, though it's a single unexplained event and not yet confirmed.
The largest dark matter detector has identified a single anomalous particle, marking a potential breakthrough in the search for dark matter.
Scientists at CERN have created quark-gluon plasma using smaller atomic nuclei like oxygen and neon, replicating conditions from the early universe to study its origins.
John Baez and collaborators present a new paper that uses exceptional algebraic structures, including E7 and Jordan algebras, to provide a mathematical explanation for the three generations of quarks and leptons in the Standard Model.
This paper introduces Certification-Driven Reinforcement Learning (CDRL), a framework that leverages symbolic reasoning to generate reusable constraints for improving reinforcement learning in combinatorial search spaces, demonstrated in neutrino flavor model discovery with higher valid model rates and efficiency.
The article details the history of neutrino detection experiments, from early work by Cowan and Reines to modern observatories like IceCube, highlighting the challenges and breakthroughs in capturing these elusive particles for scientific advancement.
Physicists at the BES III experiment have found convincing new evidence for glueballs, exotic particles made entirely of gluons, supporting predictions of the Standard Model of particle physics.
Physicists claim to have discovered a mysterious 'glueball' particle composed of force, as reported in a scientific study.
A global network of neutrino detectors is revealing the radioactive elements inside Earth's mantle, offering a new view of the planet's interior heat engine. Experiments like SNO+ and JUNO are detecting geoneutrinos to map the mantle's composition.
This paper demonstrates that scaling laws fit on small transformer models can accurately predict the loss of much larger models trained on particle physics jet data, enabling compute budgets to be translated into expected physics performance before large training runs. They release five pretrained models and the full training recipe.
New theoretical calculations resolve a 25-year muon g-2 puzzle, but create a clash with older experimental results, suggesting possible new physics.
CERN announces the Genesis Mission to develop and deploy self-improving AI models, aiming to advance artificial intelligence in scientific research.
The Large Hadron Collider concludes its final physics run and enters Long Shutdown 3, a major upgrade program to become the High-Luminosity LHC, scheduled to begin operation in 2030.
The article explores the complexity of counting elementary particles in the Standard Model, noting that the number can vary from 17 to many more depending on definitions and theoretical nuances.
Quanta Magazine recounts the 70-year history of neutrino detection, from Pauli's postulate to massive experiments like Super-Kamiokande and IceCube that solved the solar neutrino problem and revealed neutrino oscillation.
At CERN, the ALICE collaboration uses open source code on GitHub to analyze massive amounts of physics data, demonstrating how shared code and peer review enable global teamwork in scientific breakthroughs.
Physicists have reached the neutrino fog in dark matter detection, forcing a shift from WIMP searches to broader approaches like quantum sensors and atmospheric searches.
This paper presents a quantized, integer-only transformer implementation for jet tagging on AMD Versal AI Engines, including a reusable open-source framework that maps transformer layers to AIE tiles for low-latency trigger systems at CERN LHC.
Lex Fridman's podcast conversation with particle physicist Don Lincoln about major open questions in physics, including dark energy, dark matter, and the unification of laws.
Collider-Bench is a new benchmark that evaluates LLM agents on reproducing particle physics analyses from the Large Hadron Collider using only public papers and open software, requiring physical reasoning to fill missing implementation details.