摘要
一位新闻记者兼机器人与机器学习每日新闻的工作人员新闻编辑在一份新的报告中讨论了机器学习的研究结果。根据NewsRx记者在德克萨斯州College Station发表的新闻报道，研究称：“我们考虑了机器学习技术，它与应用提升决策树(BDT)在大型强子对撞机(LHC)上对产生的轻子伙伴进行SEAR CHES相关，这些伙伴衰变为轻子和不可见粒子。这种情况可以出现在最小超对称标准模型(MSSM)中。但可以在标准型号(SM)的许多其他扩展中实现。这项研究的资金支持者包括美国能源部(DOE)、原子能部(DAE)、国家科学基金会(NSF)、国家科学基金会(INFN)、国家科学基金会(NSF)。
Abstract
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).
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