论文标题
使用动态图卷积神经网络识别宇宙射线到达方向的模式
Identification of Patterns in Cosmic-Ray Arrival Directions using Dynamic Graph Convolutional Neural Networks
论文作者
论文摘要
我们提出了一种使用动态图卷积神经网络来识别来自源的超高能宇宙射线的新方法。这些网络旨在处理稀疏排列的对象并利用其短期和长期相关性。我们的方法在宇宙射线的到达方向上搜索模式，这预计会导致宇宙磁场中的连贯偏转。该网络通过仅在各向同性分布的宇宙射线的源标志的源签名来区分天体物理场景，并允许识别属于偏转模式的宇宙射线。我们使用模拟的天体物理场景，其中源密度是唯一显示如何得出密度限制的自由参数。我们将此方法应用于Agasa天文台的公共数据集。
We present a new approach for the identification of ultra-high energy cosmic rays from sources using dynamic graph convolutional neural networks. These networks are designed to handle sparsely arranged objects and to exploit their short- and long-range correlations. Our method searches for patterns in the arrival directions of cosmic rays, which are expected to result from coherent deflections in cosmic magnetic fields. The network discriminates astrophysical scenarios with source signatures from those with only isotropically distributed cosmic rays and allows for the identification of cosmic rays that belong to a deflection pattern. We use simulated astrophysical scenarios where the source density is the only free parameter to show how density limits can be derived. We apply this method to a public data set from the AGASA Observatory.
