Upcoming Seminars

October 27, 2020

Kiri Wagstaff
NASA Jet Propulsion Laboratory /
Oregon State University

Machine Learning for Spacecraft at Europa: Enabling In-Situ Discoveries to Maximize Science Return [2019 paper], [2020 paper]

Upcoming missions to remote destinations like Jupiter's moon Europa will operate at extreme distances from the Earth where direct human oversight is impossible. The combination of extreme distance, limited lifetime due to high radiation, and limited data downlink creates an urgent need for reliable autonomous operations. Machine learning can help by analyzing data for features of interest as it is collected. Data with positive detections can be marked for high-priority downlink to Earth for mission planning. For Europa, such features include thermal anomalies, active icy plumes, and unusual surface mineral deposits. This talk describes data analysis and machine learning methods that can operate onboard to increase the rate of exploration and discovery.

Matthew Argall
University of New Hampshire

The MMS SITL Ground Loop: Automating the Burst Selection Process [paper], [book chapter]

Global-scale energy flow throughout Earth's magnetosphere is catalyzed by processes that occur at Earth's magnetopause (MP). Magnetic reconnection is one process responsible for solar wind entry into and global convection within the magnetosphere, and the MP location, orientation, and motion have an impact on the dynamics. Statistical studies that focus on these and other MP phenomena and characteristics inherently require MP identification in their event search criteria, a task that can be automated using machine learning. We introduce a Long-Short Term Memory (LSTM) Recurrent Neural Network model into the operational data stream of the Magnetospheric Multiscale (MMS) mission to free up mission operation costs, detect MP crossings, and assist studies of energy transfer into the magnetosphere.

November 24, 2020

No seminar scheduled for December, resumes January.

Kiley Yeakel
The Johns Hopkins University Applied Physics Laboratory

Mario Morvan
University College London

January 26, 2021

Special Theme: Model Metrics and Assessment

Michael Liemohn
University of Michigan

Sophie Murray
Dublin Institute of Advanced Studies

February 23, 2021

Tadhg Garton
University of Southampton /
Alan Turing Institute

Lior Rubanenko
Stanford University

Past Seminars

September 22, 2020

Abigail Azari & Caitriona Jackman
ML4PSP Organizers

Introductions to the ML4PSP Series &
Integrating ML for Planetary Science In the Next Decade [paper]

The ML4PSP organizers will discuss the series and provide introductions before summarizing a recent white paper submitted to the NRC Planetary and Astrobiology decadal on integrating machine learning into planetary science.

Matthew K. James
University of Leicester

3D modelling of Mercury's magnetosphere
using the new MESSENGER FIPS proton moments [paper]

A new MESSENGER FIPS dataset is introduced, where the 𝜅-distribution function is fitted numerically to proton spectra, providing more accurate estimates of density and temperature than previous Maxwellian fits. The quality of the fitted distribution functions are then assessed using modular artificial neural networks in order to remove badly fitted spectra. The new moments are then used to train a deep artificial neural network in order to create a scalable 3D proton model for Mercury's magnetosphere.