CAREER: Uncovering Solar Wind Composition, Acceleration, and Origin through Observations, Modeling, and Machine Learning Methods

Thirty-two years after the first sophisticated solar wind ion composition spectrometer was launched on the NASA Ulysses mission, we now have a wealth of information indicating that heavy ions play a key role in solar and heliospheric physical processes. Heavy ions act as important test particles and have unique responses to the environment around them: heavy ion composition is an imperative parameter for tracking the heliospheric structures to their sources on the Sun or to local sources in interplanetary space. As we are entering the new era of modern missions, solar wind science is at a crossroads where the science return from solar wind composition data in understanding of the inner heliosphere and beyond is maximized by integrating data, models, and machine learning techniques. This project is an innovative inter-disciplinary study to combine multiple techniques in understanding the solar wind. The broader impacts of the project include support of an early career scientist, support of two graduate students, the creation of annual workshops on “Heavy Ion Composition in the Heliosphere”, and outreach to Ann Arbor and Detroit area high schools. The following scientific questions will be addressed: (1) Where does the solar wind originate?; (2) How is the solar wind accelerated from the corona?; (3) How do the solar wind and heliosphere respond to the evolution of the solar cycle?; and (4) How can we better understand and use the composite solar wind data sources with Machine Learning (ML) and Artificial Intelligence (AI) technology? This research uses available in-situ observations from many instruments across multiple NASA space missions, including: NASA’s Ulysses, ACE, Wind, Parker Solar Probe, and Solar Orbiter. Space-based data will provide global solar context, magnetic field geometry and basic plasma diagnostics of the solar wind source regions. The Potential Field Source Surface (PFSS) model will be used to track the coronal magnetic field from the Sun to the Earth. In addition, ML/AI techniques will be applied to the solar wind composition data to categorize solar wind types more objectively, and to rank their importance by employing multiple ML feature selection algorithms. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.