Automating the Detection of Magnetic Flux Ropes Using Data Mining Techniques
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Our reliance on space-borne and ground-based technological systems has made us susceptible to the adverse conditions in the space environment. These conditions can cause disruption of satellite operations, electric power distribution grids, and oil pipelines, leading to a variety of socioeconomic losses. In order to mitigate the impact, we need to better understand the planetary magnetic field and their responses to the solar wind. In particular, magnetic reconnection is a widely-studied product of the Earth-Sun interaction and it is believed to be the ultimate driver of extreme phenomena such as eruptive solar flares, coronal mass ejection, geomagnetic storms, and magnetospheric substorms. To better understand the birth and the evolution of magnetic reconnection, one can study the dynamics of the magnetic field in the magnetotail and look for events such as magnetic flux ropes. This paper aims to investigate the GEOTAIL mission magnetic data as a function of time and location and to search for magnetic flux ropes. It further explores the sensitivity and the specificity of automating the process of magnetic flux rope detection. In conclusion, our approach indicated satisfying degrees of accuracy (~ 80%), yet work needs to be done to better train the algorithms to increase the accuracy (> 95%) of the detection process.