Event reconstruction for KM3NeT/ORCA using convolutional neural networks

Event reconstruction for KM3NeT/ORCA using convolutional neural networks
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使用卷积神经网络对 KM3NeT/ORCA 进行事件重建

DOI:
10.1088/1748-0221/15/10/p10005
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发表时间:
--
影响因子:
1.3
通讯作者:
S. Aiello
S. Aiello
中科院分区:
工程技术4区
文献类型:
--
作者:
S. Aiello

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中微子的基本性质的精确测量是一个机会,可以让我们发现和理解存在于粒子物理学标准模型之外的物理学。中微子的探测,无论是对于基本粒子物理学还是高能天体物理学,可以通过ANTARES [1]和KM 3 NeT [2]合作开发的深海和光子探测技术来实现超大容量水切伦科夫探测器。KM 3 NeT/ORCA是KM 3 NeT的低能探测器,致力于确定中微子物理学的一个未知但基本的参数:中微子质量排序。该实验的重点是测量宇宙射线诱导的中微子的能量和天顶角相关的振荡模式,这些中微子具有几个GeV的能量,起源于大气层并穿过地球[3]。
Precision measurements of the fundamental properties of neutrinos are one of the opportunities that might allow us to discover and understand the physics that exists beyond the established Standard Model of particle physics.The detection of neutrinos, both for fundamental particle physics and high-energy astrophysics, can be achieved with the deep-sea and photon-detection technology that has been developed by the ANTARES [1] and KM3NeT [2] Collaborations for very-large-volume water-Cherenkov detectors. KM3NeT/ORCA, the low-energy detector of KM3NeT, addresses the determination of a still unknown, but fundamental parameter of neutrino physics: the neutrino mass ordering. The experiment focuses on the measurement of the energy-and zenith-angle-dependent oscillation patterns of cosmic-ray-induced neutrinos with a few-GeV energy that originate in the atmosphere and traverse the Earth [3].
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影响因子: 2.7
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影响因子: 5
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