Cosmic-Ray Composition analysis at IceCube using Graph Neural Networks

P. Koundal
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引用次数: 1

Abstract

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.
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利用图神经网络分析冰立方的宇宙射线成分
冰立方中微子天文台是一个深埋在南极冰中的多组件探测器。本程序将讨论从冰立方及其表面阵列冰顶的综合操作分析,以估计宇宙射线组成。这项工作将描述一种新的基于图神经网络的方法来估计初级宇宙射线的质量,该方法利用信号足迹信息和重建的宇宙射线空气阵雨参数。此外,这项工作还将引入新的成分敏感参数,以改进对宇宙射线成分的估计,并有可能提高我们对宇宙射线空气阵雨中高能μ子含量的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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Properties of Cosmic Beryllium Isotopes Mass Composition and More: Results from the Auger Engineering Radio Array Status and prospects of the Auger Radio Detector The 27th European Cosmic Ray Symposium – General remarks Unique Properties of Cosmic Rays: Results from the Alpha Magnetic spectrometer
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