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Activity Number: 220622 - Statistical and Machine Learning Efforts on Solar Flare Predictions II
Type: Topic Contributed
Date/Time: Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #317806
Title: Identifying Flux Rope Signatures Based on ‘Human’ and ‘Machine Learning’ Techniques
Author(s): Teresa Nieves-chinchilla*
Companies: Heliophysics Science Division of the Goddard Space Flight Center (GSFC)
Keywords:
Abstract:

The coherent and monotonic change in the magnetic field observed by space-based observatories is generally associated with the result of a spacecraft crossing a large flux rope embedded within the Interplanetary Coronal Mass Ejections (ICMEs). Thus, for many years, the traditional physics-based flux rope models have been used as a tool to infer the geometrical and physical properties of these large structures. These models are based on cylindrical geometry, ideal magnetic field helical topology and restrictive internal dynamical constrains. The departures from the expected in-situ observations have been interpreted as lack of spacecraft aiming or signatures of the evolution. However, this interpretation can lead a wrong modeling and therefore a misleading in the deduced properties and fatal consequences in Space Weather forecast. More than 20 years of observations by the Wind spacecraft has yielded a rich source of information about the internal magnetic structure of the ICMEs that can help to mitigate this problem. In the first part of this research, we have trained ourselves with an analytical flux rope model. Based on visual inspection, we have learned to identif


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