Upcoming Seminars

February 23, 2021

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.

March 23, 2021

Lasse Clausen
University of Oslo

Hannah Kerner
University of Maryland

April 27, 2021

Jacob Bortnik
University of California, Los Angeles

Téo Bloch
University of Reading

May 25, 2021

Ryan McGranaghan
Atmospheric and Space Technology Research Associates (ASTRA) LLC

Giacomo Nodjoumi
Jacobs University, Bremen

Past Seminars

See previous talks on YouTube.

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.

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
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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

Kiley Yeakel
The Johns Hopkins University
Applied Physics Laboratory

Machine Learning Algorithms for Automated Detection of Boundary Crossings: A Case Study from Cassini

As increasingly data-intensive sensors are developed for downlink-constrained deep-space missions, scientists face a future in which only a small portion of the science data collected by the spacecraft can be sent back to Earth. There’s a rapidly increasing need to develop “smart” autonomous algorithms capable of rudimentary science analysis on-board the spacecraft so that the downlink bandwidth can be optimized for the most relevant observations. Here, we present one such case study from the Cassini mission where we have utilized machine learning (ML) algorithms to classify whether the spacecraft was in the magnetosphere, magnetosheath or solar wind, utilizing a set of labeled magnetopause and bow shock crossings spanning from 2004 – 2016. We analyze the overall accuracy of various ML algorithms – Recurrent Neural Networks (RNNs) and Gaussian Mixture Models (GMMs) – utilizing combinations of features from the magnetometer (MAG), Charge Mass Spectrometer (CHEMS) and Low-Energy Magnetospheric Measurement System (LEMMS).

Mario Morvan
University College London
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Using Deep Learning for Precision Photometry in Exoplanetary Science [2020a paper], [2020b preprint]

Disentangling the planetary signal from the stellar and instrumental noise is a major data and modelling challenge with inevitable repercussions for transits detection and characterisation. Here we consider approaches to leverage the power of deep learning and help tackling this challenge. After discussing a LSTM-based method to model the noise in Spitzer light curve observations, we present preliminary studies aiming at developing an end-to-end differentiable pipeline combining the flexibility and scalability of neural networks with the precision and domain knowledge borne by physical models.

January 26, 2021

Special Theme: Model Metrics and Assessment

Michael Liemohn
University of Michigan

One is Not Enough
Thoughts on Choosing Data-Model Comparison Metrics

The magnetospheric physics research community uses a broad array of quantitative data-model comparison methods – metrics – when conducting their research investigations. It is often the case, though, that any particular study will only use one or two metrics. Because metrics are designed to test a specific aspect of the data-model relationship, limiting the comparison to only one or two metrics reduces the physical insights that can be gleaned from the analysis, restricting the possible findings from such studies. Additional physical insights can be obtained when many types of metrics are applied. A few best practices for choosing metrics for space physics studies are presented and discussed.

Sophie Murray
Dublin Institute of Advanced Studies

Finding the Right Metric
Solar Flare Forecast Evaluation [paper], [paper], [flare scoreboard]

One essential component of operational space weather forecasting is the prediction of solar flares. Early flare forecasting work focused on statistical methods based on historical flaring rates, and more complex machine learning based methods have been implemented in recent years. A multitude of flare forecasting methods are now available for operational use, and proper evaluation of these products is crucially important for model developers, forecasters, end-users, and stakeholders because it facilitates an understanding of the strengths and weaknesses of the forecasting process. This talk will outline current collaborative efforts in solar flare forecasting that are driving international standards based on terrestrial weather forecasting practices, such as defining evaluation metrics, climatological benchmarking, and ensemble requirements.