Event Details

Space and Planetary Physics seminar

Abstract: The earliest interplanetary missions launched in the 1970s returned a few hundred gigabytes of data to Earth. In contrast, planetary missions today are collecting multiple terabytes of data. These missions are transforming the field of planetary science rapidly into an observationally rich field. These data volumes and their variety, have prompted the use of data science methods, including machine learning, to study planetary systems.

Data science methods are particularly useful in providing system-wide perspectives, furthering our understanding of current, and past, planetary environments. At Mars for example, we understand that the magnetic environment impacts atmospheric loss processes, both at present and over geologic timescales, but we are still understanding exactly how these magnetic fields affect such processes. The magnetic field morphology is complex, as Mars’ magnetic field environment is driven by various factors including the non-uniform distribution of localized crustal fields, solar wind dynamics, and ionospheric currents. In this presentation, we use the MAVEN mission dataset, now with over 15,000 orbits to develop a global perspective on the magnetic field morphology.

Developing system-wide perspectives like this using spacecraft data can be challenging. Spacecraft data, like other geoscience data, are inherently spatially and temporally organized. This represents valuable information, but data like these can present challenges when applying data science methods without adjustment. Furthermore, use of these data often focuses on answering scientifically motivated questions about the physical world. Answering such questions benefits from the use of models which are understandable to humans. These “interpretable”¬ models aim to allow for this understanding by following underlying physical rules or constraints or a simple functional form.

In this seminar, I will focus on addressing these challenges applying interpretable models and other data science methods to characterize of Mars’ magnetic field response to varying solar wind conditions. We find globally asymmetric responses of the magnetic field under different solar wind interplanetary magnetic field directions. I will then discuss the implications of these findings for Mars’ space environment.

If someone from outside of the SPP group would like to join the webinar, please send a request to n.rogers1@lancaster.ac.uk for the Teams link.