Research news on Particle data analysis

Particle data analysis is a computational technique used to process and interpret data from particle detectors in high-energy, nuclear, or astroparticle physics experiments. It encompasses event reconstruction, track finding, vertex reconstruction, particle identification, and energy/momentum estimation from raw detector signals. The method relies on statistical inference, likelihood fits, multivariate classification, and unfolding procedures to extract physical observables from detector-level quantities while correcting for efficiencies, resolutions, and backgrounds. Particle data analysis pipelines are typically implemented within specialized software frameworks and are essential for testing theoretical models and determining parameters such as cross sections, lifetimes, and branching ratios.

ATLAS sets record limits on Higgs boson's self-interaction

One of the biggest open questions in particle physics today is how the Higgs boson interacts with itself. This "self-coupling" could help explain the evolution of the early universe and the mechanism that gives mass to elementary ...

Dark matter experiment reaches ultracold milestone

An international collaboration, including Northwestern University, has reached a critical milestone in the search for dark matter, the mysterious substance that makes up about 85% of all matter in the universe. Located two ...

The Amaterasu particle: Cosmic investigation traces its origin

Cosmic rays are extremely fast, charged particles that travel through space at nearly the speed of light. The Amaterasu particle was detected in 2021 by the Telescope Array experiment in the U.S. It is the second-highest-energy ...

AI streamlines deluge of data from particle collisions

Scientists at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory have developed a novel artificial intelligence (AI)-based method to dramatically tame the flood of data generated by particle detectors at ...

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