Data Science in Giant Planet Magnetospheres

Each of the giant planets is surrounded by a planetary magnetosphere which contains a complex web of interacting elements, from a planet's neutral and ionised atmosphere, ring systems and natural satellites, populations of dust, neutral gas, plasma, and radiation belts, all embedded within the supersonic solar wind. Unravelling how these elements interact and what physical processes are at work over extremely large (million km) length scales is a formidable challenge and has been studied for the last four decades, starting with the Pioneer 10 flyby of Jupiter. The challenges of understanding these systems include processing and relating 100s of GB of heterogeneous datasets; accounting for sampling, resolution and other instrumental biases; and inferring the state of processes in large-scale systems with limited spacecraft trajectories. These are typical problems that are solved every day in the rapidly growing field of Data Science and which may enable a revolution in how we study planetary magnetospheres.

In this project, the student will study 'Saturn's magnetosphere' using data from the entire Cassini mission. In particular, relationships between in situ measurements (plasma, energetic particles, magnetic fields) and remote-sensing measurements (energetic neutral atoms, aurorae) will be used to infer the state of the magnetosphere, and the physical processes at work, using a combination of machine learning and mutual information theory. Of particular interest is how the signatures of solar wind activity can be determined through comparing multiple data sets with solar wind propogation models using mutual information theory. In particular, what natural proxies are available in plasma data that can inform of of the systems size, scale, and behavior.

Project goals
1. Use machine learning to automatically recognise structures and events from in situ field and plasma measurements from the Cassini spacecraft.
2. Critically examine the efficacy of machine learning methods for automated magnetospheric data mining.
3. Apply methods from mutual information theory to interpret observations and critically examine physically-motivated models for dynamics in giant planet magnetospheres.