首页|Researchers from University of Roma Tre Describe Findings in Machine Learning (K inematic Variables and Feature Engineering for Particle Phenomenology)
Researchers from University of Roma Tre Describe Findings in Machine Learning (K inematic Variables and Feature Engineering for Particle Phenomenology)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting originating from Rome, Italy, by NewsRx correspondents, research stated, "Kinematic variables play an importa nt role in collider phenomenology, as they expedite discoveries of new particles by separating signal events from unwanted background events and allow for measu rements of particle properties such as masses, couplings, and spins. For the pas t ten years, an enormous number of kinematic variables have been designed and pr oposed, primarily for the experiments at the CERN Large Hadron Collider, allowin g for a drastic reduction of highdimensional experimental data to lower-dimensio nal observables, from which one can readily extract underlying features of phase space and develop better-optimized data-analysis strategies." Funders for this research include National Science Foundation (NSF), United Stat es Department of Energy (DOE), United States Department of Energy (DOE), Nationa l Research Foundation of Korea, United States Department of Energy (DOE), United States Department of Energy (DOE). Our news editors obtained a quote from the research from the University of Roma Tre, "Recent developments in the area of phase-space kinematics are reviewd, and new kinematic variables with important phenomenological implications and physic s applications are summarized. Recently proposed analysis methods and techniques specifically designed to leverage new kinematic variables are also reviewed. As machine learning is currently percolating through many fields of particle physi cs, including collider phenomenology, the interconnection and mutual complementa rity of kinematic variables and machine-learning techniques are discussed."
RomeItalyEuropeCyborgsEmerging T echnologiesEngineeringMachine LearningUniversity of Roma Tre
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