Project Abstract

Driven by a clear interest in space and the way solar wind affects Earth, this project aims to find out exactly how these powerful streams change our planet’s magnetic field. The main question we ask is simple: How does solar wind impact Earth’s magnetosphere? To answer this, we built a machine learning model known as XGBoost that uses basic physics equations to work through over ten years of data. Alongside this, we also tried out a neural network that includes more physical details to see if it can improve our predictions. The key findings from our work show that the model does a good job when dealing with normal solar wind conditions, but it has trouble when the disturbances are very strong, particularly when the Dst index goes above –100. This suggests that extra factors—like the weight of a proton and the constant of vacuum permeability—might play a bigger role than we first thought. In short, our study provides a straightforward yet valuable look at how solar wind interacts with Earth, offering new insights that can help push space weather research forward.

Keywords: Space Research, Solar Wind, Storm Prediction

 Conference Details

Session: B

Location: Sir Stanley Clarke Auditorium at 13:30 15:30

Markers: Jiaxiang Zhang, Oliver Kullmann

Course: BSc Computer Science 3yr FT

Future Plans: I’m looking for work