P017
Machine Learning and Data Science in Planetary Science

Session ID#: 159461

Session Description:
Many facets of research in planetary science rely on analyzing large volumes of in-situ and spacecraft data. Traditionally, these data were collected and analyzed manually. In recent decades, developments in the field of machine learning (ML) have begun to gradually augment traditional techniques, answering the need for automatic methods that can rapidly and intelligently extract information from large datasets in a useful manner.

This session will be dedicated to data driven research that leverages ML and data science to enhance our scientific understanding and return from planetary data and missions. Topics may encompass data analysis on the ground or onboard spacecraft for existing and future missions and instruments. Submissions are welcome for applications across science and engineering, including but not limited to: spacecraft operations and mission planning; surface, atmosphere, and space environment characterization; object detection and classification; change and novelty detection; interpretable methods; and other studies that apply ML and data science methods to planetary science.

Index Terms:

0520 Data analysis: algorithms and implementation [COMPUTATIONAL GEOPHYSICS]
0540 Image processing [COMPUTATIONAL GEOPHYSICS]
0555 Neural networks, fuzzy logic, machine learning [COMPUTATIONAL GEOPHYSICS]

Primary Convener:  Lior Rubanenko, Stanford University, Geological Sciences, Stanford, CA, United States
Conveners:  Hannah Rae Kerner, University of Maryland, College Park, Geography, College Park, MD, United States, Mario D'Amore, German Aerospace Center DLR Berlin, Berlin, Germany, Klaus-Michael Aye, Laboratory for Atmospheric and Space Physics, Boulder, CO, United States and Abigail Azari, University of California Berkeley, Berkeley, CA, United States
 
Detecting Bistability in Planetary Systems (1046117)
Morgan Underwood, Rice University, Houston, TX, United States, Adrian Lenardic, Rice University, Earth, Environmental and Planetary Sciences, Houston, TX, United States and Johnny Seales Jr, Rice University, Department of Earth, Environmental, and Planetary Sciences, Houston, TX, United States
 
Rare Event Sampling Boosted with Artificial Intelligence to Assess Solar System Stability (1053288)
Dorian S Abbot1, J D Laurence-Chasen2, Sam Hadden3, David M. Hernandez4, Teodora Szasz2, Robert J Webber5, Georgios Afentakis6 and Jonathan Weare7, (1)University of Chicago, Department of the Geophysical Sciences, Chicago, IL, United States, (2)The University of Chicago, Research Computing Center, Chicago, United States, (3)Canadian Institute for Theoretical Astrophysics, Toronto, Canada, (4)Harvard-Smithsonian, Center for Astrophysics, Cambridge, United States, (5)CalTech, Department of Computing and Mathematical Sciences, Pasadena, United States, (6)The University of Chicago, Physics, Chicago, United States, (7)New York University, Courant Institute of Mathematical Sciences, New York, United States
 
Ice to Meet You: Machine Learning Classification of Ice Blocks in Europa’s Chaos Regions (1060493)
Ethan Duncan1,2, Conor A Nixon3, Douglas Trent4, John Santerre2, Alyssa Mills5, Ian Neidel1,6, Andrew Larsen7 and Catherine C Walker8, (1)NASA Goddard Space Flight Center, Greenbelt, United States, (2)University of California Berkeley, School of Information, Berkeley, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)NASA Langley Research Center, Hampton, United States, (5)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (6)Yale University, New Haven, United States, (7)Southern Methodist University, Dallas, United States, (8)NASA Headquarters, Washington, United States
 
Characterisation of explosive volcanism on Mercury with a three-dimensional convolutional autoencoder (1071697)
Mireia Leon Dasi, Paris Observatory, LESIA, Paris, France, Sebastien Besse, Aurora Technology B.V., European Space Astronomy Centre/ESA, Madrid, Spain., Villanueva De La Canada, Spain and Alain Doressoundiram, LESIA, Observatoire de Paris, Université PSL, CNRS, Sorbonne Université, Université Paris Cité, 5 place Jules Janssen, 92195 Meudon, France., Paris, France
 
Using Coordinated, Multi-Agent Platforms for Dynamic Ocean Worlds Science (Invited) (1072536)
Bethany Purdin Theiling1, Matthew A Brandt2, Lily A Clough3, Gary Crum4, Evana Gizzi2, Cheryl J Gramling2, Michael Johnson1, Christopher Green2, James P Mackinnon1, Asmita Korde-Patel2, Brett McKinney5, Lynnae C Quick1, Robert Morgenstern2, Conrad Schiff2, Daniel C Rogers2, Mahmooda Sultana1, Elaine Stewart2, Sabrina Thompson1, Orenthal J Tucker1 and Wayne Yu2, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NASA Goddard Space Flight Center, Greenbelt, United States, (3)University of Tulsa, Geosciences, Tulsa, United States, (4)NASA GSFC, Greenbelt, MD, United States, (5)University of Tulsa, Tandy School of Computer Science, Tulsa, United States
 
Mars events detection with Hybrid Recurrent Scattering Neural Network using InSight mission data (1073634)
Grégory Sainton1, Salma Barkaoui1, Matthieu Plasman1, Angel Bueno Rodriguez2, Maarten V De Hoop3, Philippe Henri Lognonné4, Taichi Kawamura4, Keisuke Onodera5, Eleonore Stutzmann1, Naomi Murdoch6, Rudy Morel7, Aymeric Spiga8 and Antoine Lucas1, (1)Université de Paris Cité, Institut de physique du globe de paris, CNRS, Paris, France, (2)University of Granada, Signal Theory, Telematic and Communications, Granada, Spain, (3)Rice University, Computational and Applied Mathematics, Houston, TX, United States, (4)Université Paris Cité, Institut de physique du globe de Paris, CNRS, Paris, France, (5)Earthquake Research Institute, University of Tokyo, Tokyo, Japan, (6)Institut Supérieur de l’Aéronautique et de l’Espace, Toulouse Cedex 04, France, (7)Ecole Normale Supérieure Paris, Paris, France, (8)LMD/IPSL, Sorbonne Université, Paris, France - Institut Universitaire de France, France, Palaiseau Cedex, France
 
Machine Learning-Driven Detection of Boulders on the Martian Surface (1073722)
Brian Amaro, Stanford University, Stanford, United States, Nils C C Prieur, Stanford University, Stanford, CA, United States, Emiliano Gonzalez, California State Polytechnic University Pomona, Geological Sciences, Pomona, United States and Mathieu Gaetan Andre Lapotre, Stanford University, Geological Sciences, Stanford, United States
 
Deep Learning for Boulder Detection on Planetary Surfaces (1075650)
Nils C C Prieur, Stanford University, Stanford, CA, United States, Brian Amaro, Stanford University, Stanford, United States, Emiliano Gonzalez, California State Polytechnic University Pomona, Geological Sciences, Pomona, United States, Lior Rubanenko, Stanford University, Geological Sciences, Stanford, CA, United States, Hannah Rae Kerner, University of Maryland, College Park, Geography, College Park, MD, United States, Zhiyong Xiao, Sun Yat-Sen University, Guangzhou, China, Stephanie C. Werner, University of Oslo, Centre for Earth Evolution and Dynamics (CEED), Oslo, Norway and Mathieu Gaetan Andre Lapotre, Stanford University, Geological Sciences, Stanford, United States
 
Peering into Permanently Shadowed Regions on the Moon using Machine Learning (Invited) (1082376)
Benjamin Moseley, University of Oxford, Oxford, United Kingdom, Valentin Tertius Bickel, Geological Institute, ETH Zurich, Zurich, Switzerland and Ignacio López-Francos, NASA Ames Research Center, Moffett Field, United States
 
Applying a convolutional neural network approach to catalog impact craters and volcanic structures on the surface of Venus (1094252)
Dominik Rzeszutek1, Patricia M Gregg2 and Matthew Head2, (1)University of Illinois at Urbana-Champaign, Urbana, IL, UNITED STATES, (2)University of Illinois at Urbana-Champaign, Urbana, IL, United States
 
Identifying Fundamental Upstream Drivers of Ion Escape at Mars Using Machine Learning Methods (1095719)
Yaxue Dong1, Robin Ramstad2, David Brain3, Xiaohua Fang4, Suranga Ruhunusiri5, James P McFadden6, Jasper S Halekas7, Jared R Espley8 and Francis Gerard Eparvier4, (1)University of Colorado Boulder, Boulder, CO, UNITED STATES, (2)Laboratory for Atmospheric and Space Physics, University of Colorado Boulder, Boulder, CO, United States, (3)Laboratory for Atmospheric and Space Physics, University of Colorado Boulder, Boulder, United States, (4)University of Colorado at Boulder, Boulder, United States, (5)Department of Physics and Astronomy, University of Iowa, Iowa City, IA, United States, (6)Univ California Berkeley, Berkeley, CA, United States, (7)University of Iowa, Department of Physics and Astronomy, Iowa City, IA, United States, (8)Goddard Space Flight Center, Greenbelt, United States
 
LROCNet: Detecting Impact Ejecta and Older Craters on the Lunar Surface (1097171)
Emily Dunkel1, Steven Lu1, Kevin Grimes2, James McAuley1 and Kiri L. Wagstaff1, (1)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, United States, (2)Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States
 
Cataloging Jovian Vortices: A Crowd-Sourced Deep Learning approach (1103322)
Ramanakumar Sankar1, Shawn Brueshaber2, Lucy Fortson1, Candice J. Hansen3, Chris Lintott4, Kameswara Mantha1, Glenn Orton5 and Cooper Nesmith6, (1)University of Minnesota Twin Cities, Minneapolis, United States, (2)Jet Propulsion Laboratory/California Institute of Technology, Pasadena, CA, United States, (3)Planetary Science Institute, Tucson, AZ, United States, (4)University of Oxford, Oxford, United Kingdom, (5)Jet Propulsion Laboratory, Pasadena, CA, United States, (6)Tufts University, Medford, United States
 
Determining the shape of hot ion injections in Saturn’s magnetosphere with robust data-model comparison metrics analysis (1108069)
Michael Warren Liemohn, University of Michigan Ann Arbor, Climate and Space Sciences and Engineering, Ann Arbor, United States, Abigail Azari, University of California Berkeley, Berkeley, CA, United States and Jenna Syposs, SUNY at Geneseo, Geneseo, NY, United States
 
Autonomous Astrobiology Analysis Pipeline for Biosignature Detection from Ocean World Mass Spectrometry: Data-prioritization and Machine Learning (1116557)
Lily A Clough1, Brett McKinney2, Bethany P Theiling3, Victoria Da Poian4,5, Jingyi Chen1, Jonathan Major6 and Lauren Marie Seyler7, (1)University of Tulsa, Geosciences, Tulsa, United States, (2)University of Tulsa, Tandy School of Computer Science, Tulsa, United States, (3)NASA Goddard Space Flight Center, Planetary Environments Laboratory, Greenbelt, United States, (4)Microtel LLC, Greenbelt, MD, United States, (5)NASA Goddard Space Flight Center, Planetary Environments Laboratory, Greenbelt, MD, United States, (6)University of South Florida, School of Geosciences, Tampa, United States, (7)Stockton University, Biology Program, Galloway, United States
 
Can we use network science to identify planetary biosignatures? (1128456)
Anirudh Prabhu1, Michael L Wong2, Jason R Williams1, Shaunna M Morrison1 and Robert Hazen1, (1)Carnegie Institution for Science, Earth and Planets Laboratory, Washington, DC, United States, (2)Geophysical Laboratory of the Carnegie Institution for Science, Washington, DC, United States
 
An Interpretable Deep Learning Model of Proton Auroras on Mars (1156112)
Dattaraj Dhuri1, Ahmed Alhantoobi2 and Dimitra Atri1, (1)New York University Abu Dhabi, Abu Dhabi, United Arab Emirates, (2)student, Khalifa university, Abu Dhabi, United Arab Emirates
 
Application of Symbolic Regression and Dimensionality Reduction to Forward Models of Radiative Transfer in Planetary Atmospheres (1163751)
Katia Ivanova Matcheva1, Konstantin Matchev2 and Alexander Roman2, (1)Univ of Florida-Physics, Gainesville, United States, (2)University of Florida, Physics Department, Gainesville, FL, United States
 
Deep Learning for Direct Exoplanet Detection (1164315)
Zahra Ahmed1, Simone D'Amico2, Renyu Hu3 and Mario Damiano3, (1)Stanford University, Stanford, United States, (2)Stanford University, Stanford, CA, United States, (3)NASA Jet Propulsion Laboratory, Pasadena, United States
 
Choosing and Using Multispectral Filters for Data-Limited Dynamic Planetary Surface Exploration with Linear Discriminant Analysis (1166644)
Roger Stabbins1, Peter M Grindrod1, Sara Motaghian1, Elyse Allender2 and Claire Rachel Cousins2, (1)Natural History Museum, London, United Kingdom, (2)University of St Andrews, School of Earth and Environmental Sciences, St Andrews, United Kingdom
 
Modeling the Topography of Jupiter’s Moon Europa (1170085)
Caroline Riggall, University of Florida, Department of Physics, Ft Walton Beach, FL, United States, Emma Mackie, Stanford University, Department of Geophysics, Stanford, CA, United States and Gregor Steinbruegge, NASA JPL, Pasadena, United States
 
Using Machine Learning to Aid in the Interpretation of MS Imaging Datasets: Multimodal Analysis of a Geologic Sample Containing Organic Molecular Biosignatures (1174113)
M Joseph Pasterski, University of Illinois at Chicago, Earth and Environmental Sciences, Chicago, IL, United States, Matthias Lorenz, Oak Ridge National Laboratory, Center for Nanophase Materials Sciecnes, Oak Ridge, United States, Anton V. Ievlev, Oak Ridge National Laboratory, Center for Nanophase Materials Sciences, Oak Ridge, United States, Raveendra Wickramasinghe, University of Illinois at Chicago, Department of Chemistry, Chicago, United States, Luke Hanley, University of Illinois at Chicago, Department of Chemistry, Chicago, IL, United States and Fabien P H Kenig, University of Illinois at Chicago, Earth and Environmental Sciences, Chicago, United States
 
DETECTION OF BOULDER BANDING ON LOBATE DEBRIS APRONS USING MASK R-CNN: DECIPHERING MARTIAN GLACIAL HISTORY FROM HiRISE IMAGE DATA (1184595)
Fairuz Ishraque, Princeton University, Princeton, NJ, United States, Joseph Levy, Colgate University, Hamilton, NY, United States and Ching-Yao Lai, Princeton University, Princeton, United States
 
Molecular Complexity to Biosignatures: A Machine Learning Pipeline that Connects Mass Spectrometry to Molecular Synthesis and Reaction Networks (1186669)
Jian Gong, Massachusetts Institute of Technology, Earth, Atmospheric, and Planetary Sciences, Cambridge, MA, United States, Aaron C Bell, Insight Edge Inc., 2-3-2 Otemachi, Chiyoda-ku, Tokyo-to, Japan, Timothy Gebhard, Max Planck Institute for Intelligent Systems, Empirical Inference, Tübingen, Germany; Max Planck ETH Center for Learning Systems, Max-Planck-Ring 4, Tübingen, Germany, Jaden J. A. Hastings, Weill Cornell Medicine, Dept of Biophysics and Physiology, New York, United States; Weill Cornell Medicine, The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, United States, Atılım Güneş Baydin, University of Oxford, Department of Engineering Science, Oxford, United Kingdom, Kimberly Warren-Rhodes, SETI Institute Mountain View, Mountain View, United States, Michael Phillips, Applied Physics Laboratory Johns Hopkins, Laurel, United States, Matthew Fricke, University of New Mexico, Albuquerque, United States, Nathalie A Cabrol, SETI Institute, Mountain View, CA, United States, Scott A. Sandford, NASA Ames Research Center, Moffett Field, CA, United States and Massimo Mascaro, Google, Applied AI, Mountain View, United States
 
Creating Autonomous Scientific Stations for Occultations Using Machine-Learning and Smart Telescopes (1187351)
Dorian Cazeneuve1, Franck Marchis2, Guillaume Blaclard3, Victor Martin3, Paul A Dalba4 and Joe Asencio5, (1)SETI Institute Mountain View, Mountain View, CA, United States, (2)SETI Institute, Mountain View, CA, United States, (3)Unistellar, Marseille, France, (4)SETI Institute Mountain View, Mountain View, United States, (5)SETI Institute, Mountain View, United States
 
Exploring Olivine in Astromaterials: Classification of Chondritic Components based on Major/Minor Oxides and Oxygen Isotopes. (1188730)
Alexandra Ostroverkhova1, Anirudh Prabhu2, Asmaa Boujibar3, Daniel R Hummer4, Annika Johansson5, Lucia Profeta6, Jennifer Mays6, Kerstin Lehnert6, Robert Hazen2 and Shaunna M Morrison2, (1)Carnegie Institution for Science, Earth and Planets Laboratory, Washington, United States, (2)Carnegie Institution for Science, Earth and Planets Laboratory, Washington, DC, United States, (3)Western Washington University, Department of Geology, Department of Physics and Astronomy, Bellingham, WA, United States, (4)Southern Illinois University Carbondale, Department of Geology, Carbondale, IL, United States, (5)Columbia University, Lamont-Doherty Earth Observatory, Palisades, NY, United States, (6)Columbia University, Lamont-Doherty Earth Observatory, Palisades, United States
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