About the Project
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 Bayesian inference. Of particular interest is how the natural satellites, rings, neutral gas and plasma interact with the planet’s atmosphere, and how these elements interact with the external boundary conditions provided by the solar wind.
Project goals
o Use machine learning to automatically recognise structures and events from in situ field and plasma measurements from the Cassini
spacecraft.
o Critically examine the efficacy of machine learning methods for automated magnetospheric data mining.
o Apply methods from Bayesian statistics to interpret observations and critically examine physically-motivated models for dynamics in
giant planet magnetospheres.
The Physics Department is holder of an Athena SWAN Silver award and JUNO Championship status and is strongly committed to fostering diversity within its community as a source of excellence, cultural enrichment, and social strength. We welcome those who would contribute to the further diversification of our department.
Interested candidates should contact Dr Licia Ray ([Email Address Removed]) for further information. For general information about PhD studies in Physics at Lancaster please contact our postgraduate admissions staff at [Email Address Removed]. You can apply directly at http://www.lancaster.ac.uk/physics/postgraduate/how-to-apply/ stating the title of the project and the name of the supervisor in your application. Applicants are normally expected to have the equivalent of a first (1) or upper second class (2.1) degree in Physics, Astrophysics or a related discipline.
Closing Date
Applications will be accepted until the post is filled.
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