Identifying Flux Rope Signatures Using a Deep Neural Network

• URL: http://arxiv.org/abs/2008.13294v1
• Date: Sun, 30 Aug 2020 23:23:57 GMT
• Title: Identifying Flux Rope Signatures Using a Deep Neural Network
• Authors: Luiz F. G. dos Santos, Ayris Narock, Teresa Nieves-Chinchilla, Marlon Nu\~nez, Michael Kirk
• Abstract summary: This paper applies machine learning and a current physical flux rope analytical model to identify and further understand the internal structures of ICMEs. We trained an image recognition artificial neural network with analytical flux rope data, generated from the range of many possible trajectories. The trained network was then evaluated against the observed ICMEs from WIND during 1995-2015.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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). Currently, a monotonic and coherent magnetic configuration observed is associated with the result of a spacecraft crossing a large flux rope with helical magnetic field lines topology. The classification of such an arrangement is essential to predict geomagnetic disturbance. Thus, the classification relies on the assumption that the ICME's internal structure is a well organized magnetic flux rope. This paper applies machine learning and a current physical flux rope analytical model to identify and further understand the internal structures 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 paper 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 show promise. 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.

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