Among the current challenges in space weather, one of the main ones is to forecast the internal magnetic configuration within interplanetary coronal mass ejections (ICMEs). The classification of such an arrangement is essential to predict geomagnetic disturbances. When a monotonic and coherent magnetic configuration is observed, it is associated with the result of a spacecraft crossing a large flux rope with the topology of helical magnetic field lines. This article applies machine learning and a current physical flux rope analytical model to identify and further understand the internal structure of ICMEs. We trained an image recognition artificial neural network with analytical flux rope data, generated from the range of many possible trajectories within a cylindrical (circular and elliptical cross-section) model. The trained network was then evaluated against the observed ICMEs from Wind during 1995–2015. The methodology developed in this article can classify 84% of simple real cases correctly and has a 76% success rate when extended to a broader set with 5% noise applied, although it does exhibit a bias in favor of positive flux rope classification. As a first step towards a generalizable classification and parameterization tool, these results are promising. With further tuning and refinement, our model presents a strong potential to evolve into a robust tool for identifying flux rope configurations from in situ data.

中文翻译：

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使用深度神经网络识别通量绳特征

在当前空间天气的挑战中，主要挑战之一是预测行星际日冕物质抛射 (ICME) 内的内部磁结构。这种布置的分类对于预测地磁扰动至关重要。当观察到单调且相干的磁配置时，它与航天器穿过具有螺旋磁力线拓扑结构的大磁通绳的结果有关。本文应用机器学习和当前的物理磁通绳分析模型来识别并进一步了解 ICME 的内部结构。我们使用分析通量绳数据训练了一个图像识别人工神经网络，这些数据是从圆柱形（圆形和椭圆形横截面）模型内的许多可能轨迹的范围内生成的。然后根据 1995 年至 2015 年期间从 Wind 观察到的 ICME 对经过训练的网络进行评估。本文中开发的方法可以正确分类 84% 的简单真实案例，并且在扩展到应用了 5% 噪声的更广泛的集合时有 76% 的成功率，尽管它确实表现出有利于正通量绳分类的偏差。作为通用分类和参数化工具的第一步，这些结果很有希望。通过进一步的调整和改进，我们的模型具有很大的潜力，可以发展成为一个强大的工具，用于根据原位数据识别磁通绳配置。尽管它确实表现出有利于正通量绳分类的偏见。作为通用分类和参数化工具的第一步，这些结果很有希望。通过进一步的调整和改进，我们的模型具有很大的潜力，可以发展成为一个强大的工具，用于根据原位数据识别磁通绳配置。尽管它确实表现出有利于正通量绳分类的偏见。作为通用分类和参数化工具的第一步，这些结果很有希望。通过进一步的调整和改进，我们的模型具有很大的潜力，可以发展成为一个强大的工具，用于根据原位数据识别磁通绳配置。