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Predicting Solar Wind Conditions with Machine Learning – Team Helios

INSTITUTION

Arizona State University (Tempe Campus)

CLASS

Cobalt Class (2019 – 2020)

STUDENT TEAM

Joshua Broas, Computer Science, Finance (Double Major)
Ian Carver, Computer Science
Jamshid Niroomand, Computer Science
Igor Ristanovic, Computer Science (Cybersecurity)

SCIENTIFIC & TECHNICAL GUIDANCE

Dr. Rona Oran, Research Scientist, Department of Earth, Atmospheric, and Planetary Sciences, MIT

ACADEMIC GUIDANCE

Dr. Ming Zhao, Associate Professor, School of Computing, Informatics, and Decision Systems Engineering, ASU

PROJECT DESCRIPTION

Using machine learning (or other predictive methods) teams are developing a tool predicting solar wind conditions at a given location, training the software on existing databases of solar wind data measured at given solar system “neighborhoods” (such as the Earth, Mars, and Jupiter), with the goal of building a model that can predict the conditions at any random location. The focus is on predicting the conditions only at distances from the Sun that are within those already explored with spacecraft and for which abundant solar wind data was observed.