文献类型：期刊文献

英文题名：Improving heavy Dirac neutrino prospects at future hadron colliders using machine learning

作者：Feng, Jie[1,7]; Li, Mingqiu[2]; Yan, Qi-Shu[2,3]; Zeng, Yu-Pan[1,6]; Zhang, Hong-Hao[1]; Zhang, Yongchao[4]; Zhao, Zhijie[3,5]

机构：[1] School of Physics, Sun Yat-Sen University, Guangzhou, 510275, China; [2] School of Physics Sciences, University of Chinese, Academy of Sciences, Beijing, 100049, China; [3] Center for Future High Energy Physics, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China; [4] School of Physics, Southeast University, Nanjing, 211189, China; [5] Deutsches Elektronen-Synchrotron DESY, Hamburg, 22607, Germany; [6] School of Electronics and Information Engineering, Guangdong Ocean University, Zhanjiang, 524088, China; [7] Moved to Massachusetts Institute of Technology [MIT], Cambridge, MA, 02139, United States

年份：2021

外文期刊名：arXiv

收录：EI(收录号：20220003940)

语种：英文

外文关键词：Bosons - Germanium compounds - Hadrons - Machine learning - Neutrons - Tellurium compounds

外文摘要：In this work, by using the machine learning methods, we study the sensitivities of heavy pseudo-Dirac neutrino N in the inverse seesaw at the high-energy hadron colliders. The production process for the signal is (Equation presented) while the dominant background is (Equation presented). We use either the Multi-Layer Perceptron or the Boosted Decision Tree with Gradient Boosting to analyse the kinematic observables and optimize the discrimination of background and signal events. It is found that the reconstructed Z boson mass and heavy neutrino mass from the charged leptons and missing transverse energy play crucial roles in separating the signal from backgrounds. The prospects of heavy-light neutrino mixing (Equation presented) (with = e, μ) are estimated by using machine learning at the hadron colliders with √s = 14 TeV, 27 TeV, and 100 TeV, and it is found that (Equation presented) can be improved up to O(10?6) for heavy neutrino mass mN = 100 GeV and O(10?4) for mN = 1 TeV. ? 2021, CC BY.