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arXiv 2109.08152astro-ph.HEastro-ph.IMhep-ex
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用卷积神经网络重构GeV标度IceCube中微子的能量

Using Convolutional Neural Networks to Reconstruct Energy of GeV Scale IceCube Neutrinos

Jessie Micallef

摘要:

IceCube中微子天文台位于南极冰层下1.4公里处,用5,160个光学模块检测来自中微子相互作用的切伦科夫辐射。更密集的仪器中心DeepCore旨在探测10 GeV尺度的大气中微子,以改善对中微子基本特性(如振荡参数)的重要测量,并寻找非标准相互作用。对振荡参数的灵敏度取决于中微子能量(L/E)的传播距离,在IceCube中受到到达角(决定L)和能量(E)的分辨率的限制。因此,事件重建的改进可以直接导致振荡结果的进步。这项工作使用卷积神经网络(CNN)来重建IceCube中10-GeV尺度中微子事件的能量,提供具有竞争力的分辨率和比以前基于可能性的方法更快的运行时间的结果。

英文摘要:

The IceCube Neutrino Observatory, located under 1.4 km of Antarctic ice, instruments a cubic kilometer of ice with 5,160 optical modules that detect Cherenkov radiation originating from neutrino interactions. The more densely instrumented center, DeepCore, aims to detect atmospheric neutrinos at 10-GeV scales to improve important measurements of fundamental neutrino properties such as the oscillation parameters and to search for non-standard interactions. Sensitivity to oscillation parameters, dependent on the distance traveled over the neutrino energy (L/E), is limited in IceCube by the resolution of the arrival angle (which determines L) and energy (E). Event reconstruction improvements can therefore directly lead to advancements in oscillation results. This work uses a Convolutional Neural Network (CNN) to reconstruct the energy of 10-GeV scale neutrino events in IceCube, providing results with competitive resolutions and faster runtimes than previous likelihood-based methods.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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