Can solar eruptions be forecast using a novel combination of observations and machine learning techniques?

The Sun's character is determined by its dynamic and evolving magnetic field, particularly in regions of intense magnetism known as active regions.

When active regions are young, they are the source of the most violent and energetic events in the Solar System - coronal mass ejections and solar flares.

When active regions die, they disperse their magnetic field across the Sun and remnants of this field become the input for the next solar cycle. But these dying active regions continue to produce coronal mass ejections. These ejections are of intense interest as they can drive major space weather impacts at Earth.

For example, the changes they produce in the near-Earth space environment can ultimately lead to disruptions to electricity distribution, communications, and navigation systems.

Knowing why and when these ejections will occur is therefore centrally important to understanding how the Sun operates but also for developing an ability to make accurate space weather forecasts.

Currently, although many advances have been made in our understanding of coronal mass ejections, there is no reliable way to predict their occurrence in advance.

This project will use state-of-the-art machine learning image analysis techniques and use these to build a predictive model for coronal mass ejections.