Upcoming Seminar
2024 Seminar Series
4th June 2024
Tuesday at 9 AM US Pacific
Michael Holland Brigham Young University
This Crater Does Not Exist: How Synthetically Created Craters Can Help Us Understand Crater Formation and Key Crater Features.
In the study of impact cratering machine learning has historically been used for crater segmentation and the discovery of new craters. With the advent of new and improved techniques, deep learning can also be used to enhance the current understanding of crater geomorphology. State-of-the-art machine vision techniques allow scientists to process and understand satellite imagery on scales previously thought impossible. With the use of unsupervised generative algorithms such as Diffusion Models [1], Generative Adversarial Networks (GANS) [2], and other contemporary generative models, higher-order features of impact cratering can be learned and explicitly modeled.
Andreas Bechtold Austrian Academy of Sciences and University of Vienna
A simulation-based deep learning training approach for autonomous detection of shatter cones in Mars rover images.
Shatter cones are rocks with characteristic features that can form during high-velocity impact events of asteroids or comets and are the only known macroscopic indicator for shock metamorphism. They have been involved in the identification of impact craters on Earth, and were noted in a few meteorites. Although the Moon and Mars are largely covered with impact structures, shatter cones have never been unambiguously identified during any space missions to these planetary bodies. The specific morphological features that characterize shatter cones may represent an interesting criterion in terms of image recognition. In a feasibility study we, therefore, explored how deep learning algorithms can be trained towards an automatic detection of shatter cones in images from Mars rover cameras using a simulation-based training approach. Deep learning requires large amounts of training data. As images of shatter cones from the surface of Mars do not exist, we have virtually placed shatter cones in a real contextual environment to produce the necessary training images. The feasibility study has shown that the approach with shatter cones in artificial Mars rover scenes is in principle suitable to train neural networks for automatic detection of shatter cones and provided indications of what can be done to improve the results.
Previous Seminars
SEMINAR SERIES 2024
April 9, 2024
Opal Issan University of California San Diego
Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction
The ambient solar wind plays a significant role in propagating interplanetary coronal mass ejections and is an important driver of space weather geomagnetic storms. A computationally efficient and widely used method to predict the ambient solar wind radial velocity near Earth involves coupling three models: Potential Field Source Surface, Wang-Sheeley-Arge (WSA), and Heliospheric Upwind eXtrapolation. However, the model chain has 11 uncertain parameters that are mainly non-physical due to empirical relations and simplified physics assumptions. Therefore, we propose a comprehensive uncertainty quantification (UQ) framework that can successfully quantify and reduce parametric uncertainties in the model chain. The UQ framework utilizes variance-based global sensitivity analysis followed by Bayesian inference via Markov chain Monte Carlo to learn the posterior densities of the most influential parameters. The sensitivity analysis results indicate that the five most influential parameters are all WSA parameters. Additionally, we show that the posterior densities of such influential parameters vary greatly from one Carrington rotation to the next. The influential parameters are trying to overcompensate for the missing physics in the model chain, highlighting the need to enhance the robustness of the model chain to the choice of WSA parameters. The ensemble predictions generated from the learned posterior densities significantly reduce the uncertainty in solar wind velocity predictions near Earth.
February 6, 2024
Dattaraj Dhuri New York University Abu Dhabi
Characterizing proton auroras on Mars using explainable Machine Learning
Proton auroras occur frequently on the dayside of Mars. These are seen as an intensity enhancement of Ly‐alpha (121.6 nm) emission between ~ 110 – 150~km altitudes. These auroras are triggered primarily by electron stripping and charge exchange between solar wind protons and the neutral hydrogen in the corona; forming the energetic neutral hydrogen atoms (ENA) that penetrate down to the thermosphere. Once in the thermosphere, these ENAs de-excite by collisions with the atmosphere and release the aurora emission. Recent observations of “patchy” proton auroras suggest additional mechanisms could be at play, possibly depositing solar wind protons directly in Mars’ thermosphere to affect these spatially localized proton auroras. In this talk, I will discuss our work on developing a data-driven, artificial neural network (ANN) model to characterize proton aurora occurrences. The ANN is developed using Mars Atmosphere and Volatile EvolutioN (MAVEN) in-situ observations and limb scans of Ly-alpha emissions between 2014 -- 2022. We show that the ANN reproduces individual Ly-alpha intensities with a Pearson correlation of ~ 95% along with a faithful reconstruction of the observed Ly-alpha emission altitude profiles. We use SHapley Additive exPlanations (SHAP) analysis to discover relationships between the inputs and the modeled Ly-alpha intensities. We find that Solar Zenith Angle, seasonal CO2 atmosphere variability, solar wind temperature, and density are the most important features for the modeled proton auroras. We also demonstrate that such a data-driven model can serve as an inexpensive tool for simulating and characterizing Ly-alpha response under a variety of seasonal and upstream solar wind conditions.
SEMINAR SERIES 2022 - 2023
October 3, 2023
Serina Diniega & Umaa Rebbapragada JPL, NASA
Creating a Global Map of Martian Frost, Using Visible Images and Thermal Data
The winter-time formation, evolution, and retreat of surface frost provide the best global information about the carbon dioxide and water cycles on present-day Mars. Such frost has previously been mapped using coarse-resolution thermal data, but these maps generally miss small-scale patches of frost in the mid- and lower-latitudes. High resolution imagery and spectral frost detections have been studied within a few individual sites, but manual inspection of such datasets over the full globe is too labor-intensive. In our study, we utilize data science techniques to detect frost within all these datasets so as to globally map present-day Martian seasonal frost cycle, with a high-number of ties to landform-scale (10s-meter-scale) environments. This presentation will focus on our work with visible datasets (i.e., >20k HiRISE images and >30k CTX images), where we detect frost via convolutional neural networks (CNNs) with pre-labeled datasets. Furthermore, we will touch on how we use statistical methods to combine the visible frost map with other datasets, decreasing the ambiguity of visible detections and bridging between the different resolutions.
September 12, 2023
Mireia Leon-Dasi Observatoire de Paris, LESIA
Using deep learning to characterise explosive volcanism on Mercury
The existence of explosive volcanic eruptions on Mercury has been evidenced by the MESSENGER mission through the detection of volcanic vents and associated pyroclastic deposits. However, the diversity of these features in terms of morphology, spectral properties and location, complicates the global characterisation and timing of the volcanic eruptions. In this talk, we introduce the application of an unsupervised deep learning technique (the 3D convolutional autoencoder) to capture the signature of these deposits in the spectral and spatial domains. With this technique we define the extent of the deposits, overcoming the limitations of the traditional visual interpretation approach, and we illustrate how this highlights the differences within and between deposits. Moreover, we dive into the latent space of the architecture to understand the information extracted, using deep learning as an exploratory technique.
June 13, 2023
Abby Azari University of California, Berkeley
A Virtual Solar Wind Monitor for Mars Using Gaussian Processes
As a spacecraft orbits around a planet it travels through multiple regions and it is often a long time between measurements of any particular region. This is a problem if we wish to understand how the physical processes occurring in one region might affect a different region as we never measure two regions at the same time. I address this problem for understanding how the solar wind affects Mars' space environment by developing a virtual solar wind monitor for Mars using Gaussian processes with MAVEN spacecraft data. In this presentation I discuss how use of this method allows for uncertainty quantification of the solar wind's influence on physical processes in Mars' space environment and discuss potential future implementation at other planets
Alex Barrett & Jack Wright The Open University & The European Space Agency
NOAH-H: Characterizing Mars rover landing sites using deep learning
The Novelty or Anomaly Hunter – HiRISE (NOAH-H) is a deep learning convolutional neural network intended to classify high-resolution satellite images of Mars. It was trained to classify terrain at the two finalist European Space Agency ExoMars Rosalind Franklin Rover mission landing sites, Oxia Planum (final selection) and Mawrth Vallis (runner-up). In this talk, a sequel to one given in this seminar series in November 2021, we will introduce NOAH-H and give an update on the latest results from the project. We will show: the NOAH-H Oxia Planum map published in 2022; the transfer of NOAH-H to Jezero crater, our qualitative comparison between NOAH-H and Perseverance rover and Ingenuity helicopter images, our quantitative comparison with a human-made map, and; the NOAH-H map of the Mawrth Vallis landing site, which we have recently submitted for publication.
March 7, 2023
Laura Breitenfeld Stony Brook University
Quantifying the Mineralogy of the Asteroid Bennu Using Thermal Emission Spectra and Machine Learning Multivariate Model Applications
Bennu, the target of the OSIRIS-REx mission, is an asteroid with compositions analogous to low petrologic type CI, CM, CR, and/or ungrouped carbonaceous chondrites. Asteroids like Bennu provide information about the building blocks of the early Solar System. Through the collection of a spectral training set and machine learning multivariate analysis models (partial least squares), this work analyzes the remote sensing thermal emission spectral data from the OSIRIS-REx mission to inform mineral quantification on Bennu.
Philippe Garnier University Toulouse
Ranking the Drivers of the Martian Bow Shock Location
The Martian interaction with the solar wind leads to the formation of a bow shock upstream of the planet. The shock dynamics appears complex, due to the combined influence of external and internal drivers. In this talk, we will compare the influence of the main drivers of the Martian shock location, based on several methods and published datasets from Mars Express (MEX) and Mars Atmosphere Volatile EvolutioN (MAVEN) missions. We include here the influence of the crustal fields, extreme ultraviolet fluxes, solar wind dynamic pressure, magnetosonic Mach number and Interplanetary Magnetic Field parameters. The bias due to the cross-correlations among the possible drivers is investigated with a partial correlations analysis. Several model selection methods (Akaike Information Criterion and Least Absolute Shrinkage Selection Operator regression) are also used to rank the relative importance of the physical parameters. We will also mention several recent works including machine learning, such as the martien shock detection or the identification of magnetic reconnection Electron Diffusion Regions at the Earths magnetopause.
May 2, 2023
Cai Ytsma Cai Consulting / University College London
Fundamental analyses of quantification accuracy in rock spectra using machine learning
With a focus on laser-induced breakdown spectroscopy (LIBS) applications on Mars with ChemCam, I have studied various influences on elemental quantification in rocks using a suite of LIBS spectra from almost 3,000 geological standards. Rock spectra are notoriously complex and it is difficult to build robust and accurate predictive models for unknowns on remote surfaces. From this work I have discovered pros and cons of various machine learning methods, effects of data set size and diversity, and compared results to traditional methods. The moral of the story is: the more data, the better!
SEMINAR SERIES 2021 - 2022
September 28, 2021
Special Seminar - Cross-Listed with the OpenPlanetary Seminar Series
Annie Didier NASA Jet Propulsion Laboratory
Incepting Interplanetary “Google Search” through Machine Learning
Spacecraft can produce a far greater volume of data than can be downlinked. Though interplanetary communication rates have grown in orders of magnitude since early missions, they are far surpassed by the growth in data volume produced by on-board instruments. To bypass the communication bottleneck between spacecraft and ground while optimizing scientific yield, we propose the concept of ‘Interplanetary Google Search,’ a novel approach to spacecraft data retrieval inspired by the Google search engine. We envision a selective downlink capability with on-board indexing and search where scientists can query a spacecraft’s on-board database for specific, relevant information. To realize this on-board data storage and indexing vision, we must first introduce the means to extract features relevant to scientific interest from historic data payloads. The key to our approach is utilizing machine learning to extract features and summarize data. We have demonstrated this capability using image segmentation and image captioning models of MSL RGB imagery. Such methods would, for instance, enable scientists to download a full textual summary of the imagery taken by a rover and use this information to downlink data with specific features for further analysis. This concept has even more potential for a wider range of deep-space missions with more data-intensive instruments (e.g. ground-penetrating radar and hyperspectral imagers), and can be realized with the application of data science.
October 26, 2021
Mayur Bakrania University College London
Applying unsupervised learning and outlier detection methods to characterise magnetotail electrons
Collisionless space plasma environments are characterised by distinct particle populations that typically do not mix. Although moments of their velocity distributions help in distinguishing different plasma regimes, the distribution functions themselves provide more comprehensive information about the plasma state. Unlike moments, however, distributions are not easily characterised by a small number of parameters, making their classification more difficult. To perform this classification, we distinguish between the different plasma regions by applying dimensionality reduction and clustering methods to electron distributions in pitch angle and energy space. The automated classification of different regions in space plasma environments provides a useful tool to identify the physical processes governing particle populations in near-Earth space. Using outlier detection methods, we can identify anomalous distributions in the magnetotail that are consistent with simulations of the tearing instability.
Jay Laura U.S. Geological Survey
Planetary Spatial Data Infrastructure and Analysis Ready Data -
Spatial data efficacy is a primary concern for any scientists making use of spatial data products. The field of planetary spatial data infrastructure (PSDI) includes work to classify and identify foundational data products with well communicated spatial accuracies to ensure appropriate data use. Under the auspices of PSDI, the nascent push for analysis ready data (ARD) provides a rich opportunity space where data sets are both made available in machine learning ready formats and the ML community is actively engaged to improve the discoverability of said data. I will present a brief overview of PSDI and planetary ARD as they relate to potential ML activities.
November 23, 2021
Matthew Cheng University College London
Automated bow shock and magnetopause boundary detection with Cassini
The Cassini mission spent 13 years in orbit around Saturn collecting a wealth of data about its magnetic field and plasma populations. A catalogue of bow shock and magnetopause boundary crossings is needed to study the structure of Saturn’s magnetosphere and fundamental plasma processes like instabilities. However, a very challenging aspect is the manual identification of thousands of crossings. It is both time-consuming and prone to human error. This calls for ways to standardize the detection of these boundaries through automation. A range of techniques is explored from traditional time-series analysis on the magnetic field and plasma moments data to modern machine learning techniques like evidential deep learning which quantify classification uncertainty applied to electron energy spectrograms.
Alexander Barrett The Open University
Characterizing the ExoMars landing site at Oxia Planum using deep learning
In this study a Deep Learning Convolutional Neural Network was trained to characterize the landscape of Oxia Planum, Mars. This work was conducted as part of the preparation for the European Space Agency’s upcoming ExoMars Rosalind Franklin Rover mission. The aim was to develop an ontology of terrain classes, which could be applied across the landing site and beyond, to form a component to traversability analysis, when combined with engineering information about the rover, and with “ground truth” information about how the classes look once Rosalind Franklin lands. Studies combining high resolution images with large spatial extents, such as the full 3-sigma potential landing ellipse, present significant challenges. Mapping or surveying large areas at full resolution becomes massively time consuming, or requires very large teams to do effectively. One solution is using machine learning systems to classify images by semantic segmentation. This approach can provide a useful tool to augment the workflow of human geomorphologists, “triaging” extremely large, high resolution datasets to identify concentrations of certain textures or landforms. I will discuss the process of developing the Oxia planum training dataset, and our assessment of the results.
January 18, 2022
Anirudh Koul Pinterest, NASA Frontier Development Lab
SpaceML Worldview Search: The NoCode Earth & Natural Disaster Dataset Curator from Unlabeled Petabyte Scale Imagery
AI modeling for Earth events at NASA is often limited by the availability of labeled examples. For example, training classifiers to detect forest fires from satellite imagery requires curating a massive and diverse dataset of example forest fires, a tedious multi-month effort requiring careful review of over 196 million square miles of data per day for 20 years. While such images might exist in abundance within 40 petabytes of unlabeled satellite data, finding these positive examples to include in a training dataset for a machine learning model is extremely time-consuming and requires researchers to "hunt" for positive examples, like finding a needle in a haystack. In this presentation, we showcase a no-code open-source tool built by an international team of citizen scientists whose goal is to minimize the amount of human manual image labeling needed to achieve a state-of-the-art classifier. The pipeline, purpose-built to take advantage of the massive amount of unlabeled images, consists of (1) self-supervision training to convert unlabeled images into meaningful representations, (2) search-by-example to collect a seed set of similar images, (3) human-in-the-loop active learning to iteratively ask for labels on uncertain examples and train on them. In initial experiments, the system has yielded orders of magnitude reduction in time and cost of data labeling efforts and has shown the potential to multiply the efficiency of the researcher's data curation efforts.
February 22, 2022
Doğacan Su Öztürk University of Alaska: Fairbanks
Towards a predictive high-resolution model of high-latitude ionospheric convection using SuperDARN Radars
Investigations of the meso-scale structures garnered significant interest in recent years due to their significant contribution to the global energy budget of the Ionosphere-Thermosphere system. Perturbations at such scales are more difficult to predict due to their intricate driver dependencies and the higher spatiotemporal resolutions required to model their behaviour. In this study, we leverage the seven-year grid data from the Prince George Radar which is operated by the University of Saskatchewan to predict latitudinal and longitudinal ionospheric convection velocities. This talk will focus on the development of the prediction model and the lessons learnt that will be used towards building a global multi-scale prediction model of ionospheric convection.
Nithin Sivadas NASA Goddard Space Flight Center
Uncertainty in solar wind propagation leads to biased estimates of the ionospheric response
Solar wind measurements are mainly made at the L1 libration point, 230 Earth radii upstream from the Earth. To study their impact on the Earth, we need to estimate conditions closer to Earth. The best estimates of solar wind propagation times from L1 to Earth have an uncertainty of about 10 to 50 minutes. Also, solar wind features have scale lengths ranging from 30 to 80 RE transverse to the Sun-Earth line. Shocks and rotational discontinuities propagate through the solar wind, changing its structure as a function of time. We show that these uncertainties lead to a surprising non-linear bias in the regression slopes of ionospheric response with solar wind drivers. Data-based modelers need to be aware of how this bias affects the interpretation of their results and the coupling of their model with physics-based models. Finally, we demonstrate that we can correct this bias using Bayesian techniques and prior knowledge of the uncertainties.
April 12, 2022
Bharat Kunduri Virginia Tech
An examination of HF propagation modes using machine learning guided by ray-tracing
The Super Dual Auroral Radar Network (SuperDARN) is a network of HF radars that are typically used for monitoring plasma convection in the Earth's ionosphere. Due to the complex nature of HF propagation, the radars also observe backscatter from other sources such as ground and meteor trails. However, it is not straightforward to distinguish between these different modes of backscatter observed due to the similarities in measured parameters. In this presentation, I will discuss a new machine learning algorithm that is guided by ray-tracing for identifying different backscatter modes in SuperDARN. The new model can distinguish between meteor scatter, E-/F-region ionospheric, and ground scatter. The model is validated by comparing predicted elevation angles with those measured at the radar, and the differences between these quantities can further be used to quantify the uncertainties in ionospheric parameters such as NmF2.
Ameya Daigavane Google Research India
Unsupervised Detection of Magnetic Field Boundary Crossings for Responsive Instrumentation
We investigate unsupervised time-series analysis techniques for real-time identification of magnetic field boundaries in data collected by the Cassini Plasma Spectrometer (CAPS) on the Cassini mission to Saturn. Previous research has sought to identify these boundaries either in a supervised setting, where a subset of labeled boundary crossing events are available to the algorithms, or using magnetometer data in which these boundaries are significantly clearer. We find that Saturn bow shock transitions can be reliably detected by the methods we consider, while magnetopause transitions are harder to identify across different years, indicating that generalization of algorithm parameters remains a challenge. We identify the Plasma Instrument for Magnetic Sounding (PIMS) on the upcoming Europa Clipper Mission as a promising beneficiary of the use of similar processing methods.
May 24, 2022
Corey Cochrane NASA Jet Propulsion Laboratory
Single-Pass Subsurface Ocean Detection at Triton using the Principal Components of Magnetic Induction Measurements
There are many moons in the solar system thought to potentially harbor hidden oceans; Neptune's moon of Triton is a prime example. Magnetic induction detection is very promising due to the presence of Neptune's strong and highly dynamic magnetic field. One complication is the confounding presence of an intense ionosphere which can also generate an induced magnetic field that can mask the induction response from the putative ocean. Additionally, due to the uncertainty of Neptune's rotation rate, the phase of the synodic magnetic wave will be unknown until arrival. In this presentation, we demonstrate an ocean detection methodology based on principal component analysis that is resilient to these factors, in addition to various other noise sources. We also show how constrained ocean characterization can be achieved through a multi-pass mission concept.
David Fouhey University of Michigan
Synthesizing Magnetograms of the Sun's Photosphere with Deep Learning -
The Sun's magnetic field is the source of many high-energy space weather events like solar flares and so it is monitored by a set of instruments, each of which offers trade-offs between cadence, spatial and spectral resolution, and field of view. For instance, Hinode/SOT-SP is extremely accurate but has a slow cadence and limited field of view, and SDO/HMI is less spectrally capable but produces fast cadence, full-disk data. Conventionally, data from each instrument is used in isolation to infer the state of the Sun's photospheric magnetic field by inverting a forward model that maps magnetic field conditions to expected polarized light. I'll show SynthIA, a deep-learning based system that links data from multiple instruments to produce synthetic magnetograms capturing the best aspects of each instrument. In particular, I'll show that SynthIA can synthesize magnetograms with the cadence and field of view of SDO/HMI as well as the quality resembling those of the spectrally capable Hinode/SOT-SP pipeline.
June 28, 2022
Don Hood Baylor University
Automated Boulder Detection on the Martian Surface with MBARS
Boulder-sized clasts are common on the surface of Mars, and many are sufficiently large to be resolved by the High Resolution Imaging Science Experiment (HiRISE) camera aboard the Mars Reconnaissance Orbiter (MRO). The size, number, and location of boulders on the surface and their spatial distribution can reveal the processes that have operated on the surface, including boulder erosion, burial, impact excavation, and other mechanisms of boulder transport and generation. However, quantitative analysis of statistically significant boulder populations which could inform these processes entails prohibitively laborious manual segmentation, granulometry and morphometry measurements over large areas. We have developed an automated tool to locate and measure boulders on the martian surface: the Martian Boulder Automatic Recognition System (MBARS). The open-source Python-based toolkit autonomously measures boulder diameter and height in HiRISE images enabling rapid and accurate assessments of boulder populations.
Yu Tao University College London
Super-3D: Subpixel-Scale Topography Retrieval of Mars Using Deep Learning
High-resolution digital terrain models (DTMs) play an important role in studying the formation processes involved in generating a modern-day planetary surface such as Mars. However, it has been a common understanding that DTMs derived from a particular imaging dataset can only achieve a lower, or at the best, similar effective spatial resolution compared to the input images, due to the various approximations and/or filtering processes introduced by the photogrammetric and/or photoclinometric pipelines. With recent successes in deep learning techniques, it is now become feasible to improve the effective resolution of an image using super-resolution restoration (SRR) networks, retrieving pixel-scale topography using single-image DTM estimation (SDE) networks, and subsequently, combine the two techniques to produce subpixel-scale topography from only a single-view input image. In this work, we demonstrate with the UCL MARSGAN SRR and the MADNet SDE systems to produce single-input-image-based DTMs at subpixel-scale spatial resolution using the 4 m/pixel ESA Trace Gas Orbiter Colour and Stereo Surface Imaging System (CaSSIS) “PAN” band images and the 25 cm/pixel NASA Mars Reconnaissance Orbiter High Resolution Imaging Science Experiment (HiRISE) “RED” band images. Co-authors: J-P. Muller (UCL) S. J. Conway (CNRS)
SEMINAR SERIES 2020 - 2021
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 -
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 -
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 EnoughThoughts 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 MetricSolar 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.
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 -
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
Auroral images: Automatic classification and geomagnetic predictions [paper]
We use a pre-trained deep neural network to automatically extract features from auroral images. Using a manually labelled training dataset, we are then able to automatically classify images into one of the following classes: "clear", "cloudy", "moon", "arc", "diffuse", and "discrete". As a next step, we show some initial results from our attempts to use the extracted features to predict magnetometer observations.
Hannah Kerner University of Maryland
Novelty-Guided Target Selection for Mars Rovers
Mars rover operations currently consists of pre-scripted commands determined by the rover science and engineering operations teams on a day-to-day (or sol-to-sol) basis. Automated instrument targeting systems, which determine which surface features to target based on automated rather than pre-scripted decision-making, could help increase the science return from current and future exploration missions. To enable automatic follow-up observation of novel targets—i.e., targets that differ substantially from those observed previously in the mission—we propose to use novelty detection algorithms for ranking candidate targets detected in rover images. In this talk, I will present our proposed onboard novelty detection framework and illustrate its utility using diverse scenarios of novel geology found in Mars rover images.
April 27, 2021
Jacob Bortnik University of California, Los Angeles
Machine learning reconstruction of the inner magnetosphere
While the volumes of space physics data continue to rise exponentially, our analysis techniques have not kept pace with this rapid growth, and often do not exploit the full potential of the data. In this talk, we discuss how machine learning might provide the solution to this problem, and in particular we will show how a (sparse) time-series of point observations of some quantity can be converted into a 3-dimensional time-varying model of that quantity with the use of neural networks. As an example, we show a three-dimensional dynamic electron density (DEN3D) model in the inner magnetosphere, that can provide full coverage of the inner magnetosphere and in fact is sufficiently accurate that it points the way to new physical discoveries. The talk will be concluded with a few emerging ideas of how machine learning can be applied in the physical sciences.
Téo Bloch University of Reading
Deep-Ensemble Modelling of Electron Flux at the Radiation Belt’s Outer Boundary With Bayesian Neural Networks [website]
As space-based infrastructure becomes more ubiquitous, modelling the radiation belts is increasingly important. Most radiation belt models require an accurate outer boundary condition, as this helps to drives the simulation. Our work aims to characterise the flux-energy distribution at the outer boundary location using an ensemble of Bayesian neural networks. Each model in the ensemble predicts 11 values of flux, and the associated variance, for each set of inputs. The model performs well, predicting fluxes within a factor of 2.5 for the lower energies and within a factor of 4 for the higher energies (with a correlation between 0.5-0.8).
May 25, 2021
Ryan McGranaghan Atmospheric and Space Technology Research Associates (ASTRA)
Advancing space physics research through machine learning and information representation: A powerful and demonstrable use case through solar wind-magnetosphere-ionosphere coupling
The connection between the Sun and the Earth is a complex one, involving interactions and variabilities across a dizzying spectrum of scales and systems. The result is a relationship between us and our star that is observable only through a fleet of instruments, methods, and technologies yet creates weather in the near-Earth space environment colloquially known as space weather. Space weather is the impact of solar energy on society and is a powerful use case for demonstrating the ability of data science to converge disciplines. A key to understanding it is the way that regions of space between the Sun and the Earth’s surface are connected, particularly via particles transferred from the solar wind to the magnetosphere to the upper atmosphere—a problem that remains one of the great challenges in space physics. We will first present a new ML model that better captures the dynamics of the particle precipitation from a large volume of data. We will then share a new framework to evaluate and understand these models. We will generalize this progress as suggestive of trends that reverberate across all scientific disciplines and that even tie science to engineering, art, and design. We will raise generative questions about how we currently do AI/ML research in space physics, about the role of information representation in progressing space physics discovery (e.g., knowledge networks), and to provide insight to and spark discussion for this cross-disciplinarity community around the concepts of convergence and antidisciplinary.
Giacomo Nodjoumi Jacobs University Bremen
Detecting Cave Entrance Candidates on Mars using Deep Learning Computer Vision
In the framework of geological exploration of terrestrial planets, sinkhole-like landforms (pit craters, pit chains and skylights), as a potential direct access to the subsurface, are one of the most promising environments to focus our research. Detecting, mapping, and describing those types of landforms is a challenging process since a set of tedious tasks must be conducted manually by researchers, usually on a small set of available data. These tasks vary from data collection (in which areas with high probability of occurrences are selected and downloaded) to manual analysis that requires viewing the images in detail, mapping all occurrences with GIS, and extracting morphometric parameters. For the Moon and Mars, databases of cave candidates exist (see MGC^3 Cushing, 2012) but all of these databases are focused on small regions, rather than at planetary scale. Thus there are possible missing correlations between the presence of these landforms and the area of detection that can be related to past and present processes. To achieve data analyses at planetary scale, machine learning and deep learning algorithms are extremely valuables techniques, capable of automatically analysing large datasets. The main problem is that often it is necessary to develop specific tools and pipelines for this task. Two jupyter notebooks have been developed around FOSS Object Detection packages and used with a small dataset of 130 high resolution images acquired by the HiRISE camera from the Mars Reconnaissance Orbiter. The aim of these is to create an user-friendly environment for training and evaluating an object detection model; not only for cave candidates but also for other types of landforms. These results have been compared to available databases with preliminary promising results.
June 29, 2021
Victor Pinto University of New Hampshire
Reproducibility in Space Sciences Do we really need to publish our codes?
Reproducibility is without a doubt one of the fundamental pillars of science, and just as science has evolved through time, so has the concept of what is necessary to ensure results are reproducible. Historically, the publication of source code used for analysis, modeling, and even for figures has never been a requirement in Space Sciences and it has rarely been encouraged as a necessary practice. A possible explanation may be the assumption that reproducibility is “baked in” the physics of the systems or in the data. However, as Machine Learning becomes an integral part of Space Sciences research, the requirement that the codes developed for research are publicly available, or at least can be obtained on demand grows in popularity. Today, many publishers are encouraging code sharing, or even straight up requiring it now or in the near future. This talk will stray away from the traditional discussions on techniques and applications of machine learning for space and planetary sciences and focus on the topics of whether code sharing is the best practice to ensure reproducibility, with the idea of starting a conversation on whether we should push for or against code sharing as a community, and how should we prepare for it if it becomes the norm moving forward.
Stéphane Aicardi Observatoire de Paris
Deep Learning on Jovian Decametric Emissions -
Jupiter's decametric radio emissions have long been a valuable tool for understanding the magnetic properties of Jupiter and its interactions with its major satellites. Decametric emissions are variable on many timescales, but high-resolution observations produce huge data that can't be stored for long. Using convolutional neural networks, we try to detect, classify and locate Jovian emissions in the observations by the Nançay decameter array. This will lead to an automated method to select the high resolution data to be archived. See predictions from this network online.
July 27, 2021
John Biersteker Massachusetts Institute of Technology -
Probing Europa's interior with Bayesian inference and magnetic induction
Exploring subsurface oceans on icy moons is a key goal in the search for habitable environments. Because these moons are embedded in the time-varying magnetosphere of their host planet, their saltwater oceans generate an induced magnetic field detectable by spacecraft. Such measurements provide a tool to detect and characterize these ocean worlds. As part of the design of the upcoming Europa Clipper mission, we have developed a technique for ocean characterization using spacecraft magnetometry and Bayesian inference. This approach allows for the recovery of the ocean parameters with robust uncertainties and enables incorporating multiple datasets into a self-consistent view of Europa's interior. I will discuss the application of this technique to the upcoming Europa Clipper mission, archival data from Galileo, and possible future missions to the ice giants.
Hazel Bain CIRES University of Colorado Boulder NOAA Space Weather Prediction Center
How Well Can We Forecast Solar Radiation Storms?
Solar energetic particles (SEPs) are a driver of space weather, the effects of which can impact high-frequency communications systems, satellite systems and pose a radiation hazard for astronauts, as well flight crew and passengers on polar flight routes. The National Oceanic and Atmospheric Administration's Space Weather Prediction Center (NOAA/SWPC) issues space weather forecasts and products for energetic protons at Earth. I will discuss a recent verification study to assess our ability to forecast these storms. It is hoped that this study will serve as a benchmark for the development and validation of physics-based and machine learning SEP models. Co-authors: R. Steenburgh (NOAA SWPC), T. Onsager (NOAA SWPC), E. M. Stitely (Millersville University)