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.

Hidden microplastic pollution in Swiss waters uncovered

Microplastics are plastic particles smaller than 5 millimeters that originate either from manufactured products or from the breakdown of larger plastic items. They can now be found everywhere, from oceans and rivers to soils ...

Sizing errors can hide true nanoparticle behavior

Nanoscience, which studies small objects, has a big problem. According to a team of scientists at the National Institute of Standards and Technology (NIST), the field confronts a pervasive data analysis error that can give ...

Nanoplastics: New method provides clearer picture of the risks

Micro- and nanoplastics are now popping up everywhere: in seawater, snow, food and even in our bodies. The very smallest particles, in particular, are difficult to measure, meaning we still know too little about their spread ...

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