Neutrino interaction classification with a convolutional neural network in the DUNE far detector
Neutrino interaction classification with a convolutional neural network in the DUNE far detector
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DOI：
10.1103/physrevd.102.092003
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发表时间：
2020-11-09
影响因子：
5
通讯作者：
Zwaska, R.
中科院分区：
文献类型：
--
作者：
Abi, B.;Acciarri, R.;Zwaska, R.
深层地下中微子实验是下一代中微子振荡实验，旨在测量中微子部门的CP破坏，作为更广泛的物理计划的一部分。已经开发了一种基于卷积神经网络的深度学习方法，以提供电子中微子和μ子中微子带电电流相互作用的高效和纯选择。电子中微子（反中微子）选择效率的峰值为90%（94%），并超过85%（90%）的重建中微子能量之间的2-5 GeV。的μ子中微子（反中微子）事件选择被发现有96%（97%）的最大效率和超过90%（95%）的效率为2GeV以上的重建中微子能量。当考虑所有的电子中微子和反中微子相互作用的信号，选择纯度达到90%。这些事件的选择是至关重要的，以最大限度地提高实验的敏感性，CP违反的影响。
The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2-5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects.