首页|Study Results from Texas A&M University in the Area of Machine Lear ning Reported (Machine Learning Techniques for Intermediate Mass Gap Lepton Part ner Searches At the Large Hadron Collider)
Study Results from Texas A&M University in the Area of Machine Lear ning Reported (Machine Learning Techniques for Intermediate Mass Gap Lepton Part ner Searches At the Large Hadron Collider)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Machine Learning are discuss ed in a new report. According to news reporting originating in College Station, Texas, by NewsRx journalists, research stated, “We consider machine learning tec hniques associated with the application of a boosted decision tree (BDT) to sear ches at the Large Hadron Collider (LHC) for pair-produced lepton partners which decay to leptons and invisible particles. This scenario can arise in the minimal supersymmetric Standard Model (MSSM), but can be realized in many other extensi ons of the Standard Model (SM).” Financial supporters for this research include United States Department of Energ y (DOE), Department of Atomic Energy (DAE), National Science Foundation (NSF), I nstituto Nazionale di Fisica Nucleare (INFN) through the project of the InDark I NFN Special Initiative, National Science Foundation (NSF).
College StationTexasUnited StatesN orth and Central AmericaCyborgsDark MatterEmerging TechnologiesMachine L earningPhysicsTexas A&M University
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