超级神冈实验中大气中微子通量中Tau中微子的出现

Maitrayee Mandal
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摘要

超级神坎德实验(Super-K)是一个水切伦科夫探测器,以发现大气中微子的振荡而闻名。μ子中微子在大气中振荡的主要影响是τ中微子的出现。直接探测大气中微子通量中的ν τ将提供中微子振荡的明确证实。在Super-K上研究了νµ转变为νµ的次优势振荡模式,以确定质量顺序。目前,ν τ相互作用构成了Super-K分析中质量排序信号的最大背景。神经网络的机器学习技术在Super-K被用于分离ν τ带电电流相互作用与大气μ子和电子中微子的相互作用。通过扩大探测器的基准体积,可以将10%以上的事件添加到分析中。对Super-K运行周期(2008年至2018年)的蒙特卡罗模拟研究表明,我们可以期待在寻找tau中微子和抑制质量有序背景方面取得进展。
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Tau Neutrino Appearance in the Flux of Atmospheric Neutrinos at the Super-Kamiokande Experiment
The Super-Kamiokande experiment (Super-K) is a water Cherenkov detector noted for its discovery of the oscillation of atmospheric neutrinos. The dominant effect of the oscillation of muon neutrinos in the atmosphere is the appearance of tau neutrinos. Direct detection of ν τ in the atmospheric neutrino flux would provide a clear confirmation of neutrino oscillations. The sub-dominant ν µ oscillation mode, of ν µ changing to ν e , is studied at Super-K to determine mass ordering. Currently, ν τ interactions form the biggest background to the mass ordering signal in the Super-K analysis. Machine learning techniques of neural networks are used at Super-K to segregate ν τ charged-current interactions from the interactions of the atmospheric muon and electron neutrinos. 10% more events can be added to the analysis by expanding the fiducial volume of the detector. Studies on the Monte-Carlo simulations for a Super-K run period, between 2008 to 2018, suggest that we can expect improvements in the search for tau neutrinos and the suppression of mass-ordering backgrounds.
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