Purpose

Building connections to advance applications

Machine learning (ML), and other large-data analysis techniques, have become essential tools for analyzing the ever-increasing sizes of scientific data returned from planetary missions. In addition, ML methods have demonstrated potential to assist in mission planning and operations. Planetary space physics (or the study of plasmas around planets) has often been a field defined by sparse data concerns and extreme environments. These aspects provide a uniquely challenging, and rewarding field to apply ML methods.

This seminar series aims to bring together researchers in planetary science, space physics, machine learning, and other domain applications of ML methods to advance applications of large-data methods. We welcome presentations from a broad array of fields including: Earth based and planetary science applications, educational efforts, and basic ML research.

Upcoming Seminar

February 23 2021
9 AM Pacific / 12 PM Eastern / 5 PM Dublin

Tadhg Garton
University of Southampton /
Alan Turing Institute

Machine Learning identification of signatures in 1D magnetospheric timeseries

The products of magnetic reconnection in Saturn's magnetotail are identified in magnetometer observations primarily through characteristic deviations in the north-south component of the magnetic field. Identification of these features has long been performed by human observers, however with the advent of sophisticated computational methods, it is time to automate our search for these reconnection signatures. Here, we present a fully automated, supervised learning, feed forward neural network model to identify evidence of reconnection in the Kronian magnetosphere with the three magnetic field components observed by the Cassini spacecraft in Kronocentric radial-theta-phi (KRTP) coordinates as input. Furthermore, we present methods to validate results of machine learning algorithms when they are applied to extended datasets that originate in differing background environments than those trained, tested and validated against.

Lior Rubanenko
Stanford University
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Automatic detection of barchan dunes on Mars employing an instance segmentation neural network

The surface of Mars is riddled with dunes created by accumulating sand particles that are carried by the wind. When the sand supply is limited and the wind is approximately unidirectional, dunes take the form of crescents termed barchan dunes, whose slip faces are oriented in the dominant wind direction. Consequently, analyzing the morphometrics of barchan dunes can help characterize the winds that form them. Previously, local circulation patterns were derived by analyzing individual images of barchan dunes near the North Pole of Mars [1]. However, repeating this analysis on a global scale remains a challenge, as manually mapping dunes is largely impractical and traditional computer vision algorithms are largely ineffective at identifying the outlines of dunes from images. Here we employ Mask R-CNN [2], an instance segmentation convolutional neural network, to map dunes across the surface of Mars. Training on ~1000 images, our model achieves a mean average detection precision (mAP) of 80%, for IoU = 0.5. In the talk, I will describe the Mask R-CNN neural network and its vast space of hyperparameters, and how those can be employed for object detection and analysis by incorporating traditional computer vision techniques.

Full seminar series schedule with abstracts.

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Abigail Azari

Post-Doctoral Scholar, Space Sciences Lab, University of California Berkeley

Caitriona Jackman

Honorary Professor, Dublin Institute for Advanced Studies