AUTHOR=Raptis Savvas , Aminalragia-Giamini Sigiava , Karlsson Tomas , Lindberg Martin TITLE=Classification of Magnetosheath Jets Using Neural Networks and High Resolution OMNI (HRO) Data JOURNAL=Frontiers in Astronomy and Space Sciences VOLUME=7 YEAR=2020 URL=https://www.frontiersin.org/journals/astronomy-and-space-sciences/articles/10.3389/fspas.2020.00024 DOI=10.3389/fspas.2020.00024 ISSN=2296-987X ABSTRACT=

Magnetosheath jets are transient, localized dynamic pressure enhancements found downstream of the Earth's bow shock in the magnetosheath region. Using a pre-existing database of magnetosheath jets we train a neural network to distinguish between jets found downstream of a quasi-parallel bow shock (θBn<45o) and jets downstream of a quasi-perpendicular bow shock (θBn>45o). The initial database was compiled using MMS measurements in the magnetosheath (downstream) to identify and classify them as “quasi-parallel” or “quasi-perpendicular,” while the neural network uses only solar wind (upstream) measurements from the OMNIweb database. To evaluate the results, a comparison with three physics-based modeling approaches is done. It is shown that neural networks are systematically outperforming the other methods by achieving a ~93% agreement with the initial dataset, while the rest of the methods achieve around 80%. The better performance of the neural networks likely is due to the fact that they use information from more solar wind quantities than the physics-based models. As a result, even in the absence of certain upstream properties, such as the IMF direction, they are capable of accurately determining the jet class.