论文标题
使用图神经网络在ICECUBE上的宇宙射线组成分析
Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks
论文作者
论文摘要
Icecube Neutrino天文台是一种多组分检测器，该检测器嵌入了南部冰中的深处。该程序将讨论来自IceCube及其表面阵列ICETOP的综合操作的分析，以估计宇宙射线的组成。这项工作将描述一种基于图形神经网络的新方法，用于估计原发性宇宙射线的质量，该方法利用了信号 - 足迹信息和重建的宇宙射线淋浴参数。此外，这项工作还将引入新的成分敏感参数，以改善宇宙射线成分的估计，从而有可能提高我们对宇宙射线空气淋浴中高能MUON含量的理解。
The IceCube Neutrino Observatory is a multi-component detector embedded deep within the South-Pole Ice. This proceeding will discuss an analysis from an integrated operation of IceCube and its surface array, IceTop, to estimate cosmic-ray composition. The work will describe a novel graph neural network based approach for estimating the mass of primary cosmic rays, that takes advantage of signal-footprint information and reconstructed cosmic-ray air shower parameters. In addition, the work will also introduce new composition-sensitive parameters for improving the estimation of cosmic-ray composition, with the potential of improving our understanding of the high-energy muon content in cosmic-ray air showers.
