Ethan Wicks
Research Mentor: Liang Zhao
Mentor Department: CLaSP, Engineering
Author(s): Ethan Wicks, Liang Zhao
Session: Session 6 (3:00 PM – 3:50 PM)
Presentation Type: Poster 133
Abstract
This project was created to identify hidden signatures in in-situ solar wind plasma measurements. This relates to current research in the heliophysics field because organizations such as NASA send probes into space to determine solar wind plasma properties and how they might impact Earth’s magnetic field. This is a very innovative project because we use programming languages such as Python to analyze organize all of the data measurements by current space missions such as Advanced Composition Explorer (ACE) and Parker Solar Probe (PSP) to form histograms, scatter plots, or any other form of graphs. This helps us see the 30000+ data points all in one picture. The materials used for the project is data provided by scientific probes such as ACE/SWICS and PSP/FIELD, and programming tools. The project helps us understand the physical processes that accelerate solar wind plasma in the Sun’s corona and govern its properties in the heliosphere which remains a long-standing challenge. This area has relied heavily on the analysis of solar wind measurements obtained from past and current space missions. Adding machine learning and artificial intelligence into solar wind data analysis will lead to deeper and more insightful scientific understanding. The target audience is usually scientists, especially during scientific conferences. The project aims to inform the younger generation of researchers and students, but during certain outreach events, the target audience is everyone.
