Predicting Solar Wind Conditions with Machine Learning – Team Helios


Arizona State University (Tempe Campus)


Cobalt Class (2019 – 2020)


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


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


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


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.

This work was created in partial fulfillment of Arizona State University Capstone Courses “CSE 485-486”. The work is a result of the Psyche Student Collaborations component of NASA’s Psyche Mission ( “Psyche: A Journey to a Metal World” [Contract number NNM16AA09C] is part of the NASA Discovery Program mission to solar system targets. Trade names and trademarks of ASU and NASA are used in this work for identification only. Their usage does not constitute an official endorsement, either expressed or implied, by Arizona State University or National Aeronautics and Space Administration. The content is solely the responsibility of the authors and does not necessarily represent the official views of ASU or NASA.