NG004
Machine Learning in Space Weather III Posters

Tuesday, 15 December 2020: 04:00-20:59
Poster
Primary Convener:  Enrico Camporeale, University of Colorado at Boulder, Boulder, United States
Conveners:  Jacob Bortnik, University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, Tomoko Matsuo, University of Colorado Boulder, Boulder, CO, United States and Ryan Michael McGranaghan, Atmospheric and Space Technology Research Associates (ASTRA), Louisville, CO, United States
Primary Liaison:  Enrico Camporeale, University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, United States
Chairs:  Enrico Camporeale, University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, United States, Jacob Bortnik, University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, Tomoko Matsuo, University of Colorado Boulder, Boulder, CO, United States and Ryan Michael McGranaghan, Atmospheric and Space Technology Research Associates (ASTRA), Louisville, CO, United States
OSPA Liaison:  Enrico Camporeale, University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, United States
 
A Machine Learning Approach to Identify Solar Stokes Profiles in Flaring and Non-Flaring Active Regions (715784)
Vidya Venkatesan, Ana Cristina Cadavid, Kristine Romich and Debi Prasad Choudhary, California State University Northridge, Northridge, CA, United States
 
Continuous 3D model of ionospheric electron density based on machine learning (745212)
Artem Smirnov1, Irina Zhelavskaya1, Yuri Shprits2, Ruggero Vasile3, Matyas Szabo-Roberts1, Stefano Bianco1, Mainul Mohammed Hoque4 and Jens Berdermann4, (1)Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany, (2)GFZ German Research Centre for Geosciences, Potsdam, Germany, (3)Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Geosciences, Potsdam, Germany, (4)German Aerospace Center Neustrelitz, Neustrelitz, Germany
 
A Machine-learning Multispectral Time Series Data Set Prepared from the Solar and Heliospheric Observatory Mission (672713)
Carl Shneider1, Andong Hu1, Jannis Teunissen1 and Enrico Camporeale1,2, (1)Center for Mathematics and Computer Science (CWI), Multiscale Dynamics, Amsterdam, Netherlands, (2)University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, United States
 
Advanced Deep Learning Tool for Global Total Electron Contents Map Inpainting (668696)
Yang Pan, University of Texas at Arlington, Arlington, TX, United States, Mingwu Jin Sr, University of Texas Arlington, Arlington, United States, Shunrong Zhang, MIT Haystack Observatory, Westford, MA, United States and Yue Deng, University of Texas Arlington, Arlington, TX, United States
 
Applying Machine Learning \to Understanding the Physical Processes of Solar Flare Onset (742528)
Ward Manchester1, Hu Sun1, Yang Chen2, Yang Liu3 and Meng Jin4, (1)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (2)University of Michigan, Department of Statistics, Ann Arbor, MI, United States, (3)Stanford University, HEPL, Stanford, CA, United States, (4)SETI Institute, Mountain View, CA, United States
 
Automated Detection and Extraction of ELF/VLF Signals using Mask Regional Convolutional Neural Network (706623)
Vijay Harid1, Chao Liu2, Mark Golkowski1, Yan Pang2 and Akimun Jannat Alvina2, (1)University of Colorado Denver, Denver, CO, United States, (2)University of Colorado Denver, Electrical Engineering, Denver, CO, United States
 
Automatic Classification of Plasma Regions in Near-Earth Space with Supervised Machine Learning: Application to Magnetospheric Multi Scale 2016-2019 Observations (729502)
Hugo Breuillard, Laboratoire de Physique des Plasmas, Saint-Maur Des Fossés Cedex, France, Romain Dupuis, Katholieke Universiteit Leuven, Leuven, Belgium, Alessandro Retino, Laboratoire de Physique des Plasmas, Palaiseau, France, Olivier Le Contel, Laboratoire de Physique des Plasmas (UMR7648), CNRS/Ecole Polytechnique/UPMC/Univ. Paris Sud/Obs. de Paris, Paris, France, Jorge Amaya, KU Leuven, Leuven, Belgium and Giovanni Lapenta, Katholieke Universiteit Leuven, Department of Mathematics, Leuven, Belgium
 
Autonomous detection of whistler-mode chorus elements in the Van Allen radiation belts using morphological signal processing and pattern recognition techniques (754760)
Ananya Sengupta1, Ryan McCarthy2, Kawther Rouabhi1, Craig Kletzing2 and Ivar Christopher2, (1)University of Iowa, Department of Electrical and Computer Engineering, Iowa City, IA, United States, (2)University of Iowa, Iowa City, IA, United States
 
Data-Driven Modeling of Polar IonosphericElectrodynamics Using Convolutional Neural Networks (775793)
Willem Mirkovich, University of Colorado, Boulder, United States, Tomoko Matsuo, University of Colorado Boulder, Boulder, CO, United States and Liam M Kilcommons, University of Colorado at Boulder, Boulder, CO, United States
 
Deep Learning of Solar Wind Time–Frequency Representations for Predicting Local Ground Horizontal Magnetic Component (742161)
Sajila Wickramaratne, University of New Hampshire Main Campus, Durham, NH, United States, Md Shaad Mahmud, University of New Hampshire, Department of Electrical and Computer Engineering, Durham, United States and Amy M Keesee, University of New Hampshire, Physics and Space Science Center, Durham, NH, United States
 
Determining new representations of “Geoeffectiveness” using deep learning (701920)
Vishal Upendran1, Banafsheh Ferdousi2, Téo Bloch3, Panagiotis Tigas4, Yarin Gal4, Asti Bhatt5, Ryan Michael McGranaghan6, Chun Ming Mark Cheung7 and Siddha Ganju8, (1)IUCAA, Pune, India, (2)University of New Hampshire Main Campus, Durham, NH, United States, (3)University of Reading, Reading, United Kingdom, (4)University of Oxford, Department of Computer Science, Oxford, United Kingdom, (5)SRI International, Menlo Park, CA, United States, (6)Atmospheric and Space Technology Research Associates (ASTRA), Louisville, CO, United States, (7)Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, United States, (8)Nvidia, Santa Clara, CA, United States
 
Earth Topside Ionosphere Electron Density Prediction for the Advancement of Space Weather Forecasting (688394)
Shweta Dutta, Georgia Institute of Technology Main Campus, Atlanta, GA, United States
 
Estimating Material and Environmental Parameters for Spacecraft Charging using van allen probes Data (754553)
Humberto C Godinez1, Brendt Wohlberg2 and Gian Luca Delzanno1, (1)Los Alamos National Laboratory, Los Alamos, NM, United States, (2)Los Alamos National Laboratory, Theoretical Division, Los Alamos, NM, United States
 
Estimation of solar flare loop length by machine learning (770415)
Shohei Nishimoto1, Toshiki Kawai2, Kyoko Watanabe1 and Shinsuke Imada2, (1)National Defense Academy of Japan, Yokosuka, Japan, (2)Nagoya University, Nagoya, Japan
 
Fast Deep Learning-Based Stokes Vector Inversion with Confidence for SDO/HMI (684287)
Richard Higgins1, David Fouhey1, Spiro K Antiochos2, Graham Barnes3, Tamas I Gombosi4, Todd Hoeksema5, K D Leka3, Yang Liu5 and Peter W Schuck2, (1)University of Michigan Ann Arbor, Computer Science, Ann Arbor, MI, United States, (2)NASA GSFC, Silver Spring, MD, United States, (3)NorthWest Research Associates Boulder, Boulder, CO, United States, (4)University of Michigan, Department of Climate and Space, Center for Space Environment Modeling, Ann Arbor, MI, United States, (5)Stanford University, Stanford, United States
 
Forecasting global auroral particle precipitation and boundaries with novel multi-task deep learning techniques (710722)
Jack Ziegler, Atmospheric and Space Technology Research Associates, LLC, Boulder, CO, United States and Ryan Michael McGranaghan, Atmospheric and Space Technology Research Associates (ASTRA), Louisville, CO, United States
 
Forecasting global ionospheric total electron content (TEC) using deep learning (704100)
Lei Liu1,2, Shasha Zou3, Yibin Yao1 and Zihan Wang4, (1)Wuhan University, School of Geodesy and Geomatics, Wuhan, China, (2)University of Michigan, Ann Arbor, Ann Arbor, MI, United States, (3)University of Michigan, Climate and Space Sciences and Engineering, Ann Arbor, MI, United States, (4)University of Michigan Ann Arbor, Climate and Space Sciences and Engineering, Ann Arbor, MI, United States
 
Forecasting Kp Index Using a Hybrid Machine Learning Model Based on Random Forest and Sequence-to-sequence (689122)
Seungheon SHIN, Jihyeon Son, Kangwoo Yi and Yong-Jae Moon, School of Space Research, Kyung Hee University, Yongin, South Korea
 
Forecasting of Ionospheric real GPS TEC and SAMI3 model output parameters using the LSTM deep recurrent neural network (717484)
Gebreab Zewdie and Morris Cohen, Georgia Institute of Technology Main Campus, School of Electrical and Computer Engineering, Atlanta, GA, United States
 
Fuzzy based Approach for Recognition of Solar Coronal Holes in SDO/AIA Images (751460)
Sanmoy Bandyopadhyay1, Saurabh Das1 and Abhirup Datta2, (1)Indian Institute of Technology, Indore, Discipline of Astronomy, Astrophysics and Space Engineering, Indore, India, (2)Indian Institute of Technology Indore, Discipline of Astronomy, Astrophysics and Space Engineering, Indore, India
 
Global distribution and evolution of whistler mode chorus and hiss waves studied by a machine learning based model (686935)
Xiangning Chu1, Jacob Bortnik2, Wen Li3, Qianli Ma4, Xiaochen Shen3, Donglai Ma2, David Malaspina5 and Sheng Huang3, (1)Laboratory for Atmospheric and Space Physics, Boulder, CO, United States, (2)University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, (3)Boston University, Boston, MA, United States, (4)UCLA, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, (5)University of Colorado, Astrophysical and Planetary Sciences Department, Boulder, CO, United States
 
Hiss in the Plasmasphere and Plumes: Global Distribution From Machine Learning Technique and Their Effects on Global Loss of Energetic Electrons (687028)
Sheng Huang1, Wen Li1, Xiaochen Shen1, Qianli Ma1, Xiangning Chu2 and Luisa Capannolo1, (1)Boston University, Boston, MA, United States, (2)University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States
 
Identifying and Characterizing Whistler Waves in the Solar Wind Using Machine Learning (742917)
Samuel Fordin1, Michael A Shay1, Lynn B Wilson III2 and Bennett Maruca3, (1)University of Delaware, Newark, DE, United States, (2)NASA Goddard Space Flight Center, Heliospheric Physics Laboratory, Code 672, Greenbelt, MD, United States, (3)Center for Astrophysics, Berkeley, CA, United States
 
Identifying Flux Rope Signatures Using a Deep Neural Network (705086)
Luiz Fernando Guedes dos Santos1,2, Ayris Narock1,3, Teresa Nieves-Chinchilla1, Marlon Nunez4 and Michael S Kirk1,5, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)Catholic University of America, Physics, Washington, DC, United States, (3)ADNET Systems Inc. Greenbelt, Greenbelt, MD, United States, (4)Universidad de malaga, Malaga, Spain, (5)Atmospheric and Space Technology Research Associates, LLC, Boulder, CO, United States
 
Improving the Lead Time of Geomagnetic Index Forecasts using Solar Wind Forecasts and Deep Learning (751853)
Charles Topliff, Georgia Institute of Technology Main Campus, Atlanta, GA, United States and Morris Cohen, Georgia Institute of Technology Main Campus, School of Electrical and Computer Engineering, Atlanta, GA, United States
 
Interpretation of LSTM Prediction on Solar Flare Eruption (681346)
Hu Sun, University of Michigan Ann Arbor, Ann Arbor, MI, United States, Ward Manchester, University of Michigan, Climate and Space Sciences and Engineering, Ann Arbor, MI, United States, Zhenbang Jiao, University of Michigan Ann Arbor, Ann Arbor, United States, Xiantong Wang, University of Michigan, Ann Arbor, MI, United States and Yang Chen, University of Michigan, Department of Statistics, Ann Arbor, MI, United States
 
Investigating whether machine learning alone can predict solar wind parameters at L1 from solar images (723732)
Jannis Teunissen1, Andong Hu1, Carl Shneider2, Ajay Tiwari2 and Enrico Camporeale3, (1)Centrum Wiskunde & Informatica, Multiscale Dynamics, Amsterdam, Netherlands, (2)Centrum Wiskunde & Informatica, Amsterdam, Netherlands, (3)University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, United States
 
Magnetic Field topology reconstruction in a 3-D simulation box using Gaussian Process Regression (761589)
Ramiz A Qudsi1, Mike Richardson2, Haley DeWeese2, Jeffersson A Rueda3, Federica Bianco4, Riddhi Bandyopadhyay1, Alexandros Chasapis5, Rohit Chhiber1,6, Bennett Maruca7, William H Matthaeus1, David Miles8, David J Sundkvist9, Daniel Verscharen10, Sarah K. Vines11, Joseph H Westlake12 and Robert T Wicks13, (1)University of Delaware, Department of Physics and Astronomy, Newark, DE, United States, (2)University of Delaware, Physics and Astronomy, Newark, DE, United States, (3)University College London, London, United Kingdom, (4)University of Delaware, Newark, United States, (5)Laboratory for Atmospheric and Space Physics, Boulder, CO, United States, (6)NASA Goddard Space Flight Center, Greenbelt, DE, United States, (7)Center for Astrophysics, Berkeley, CA, United States, (8)University of Alberta, Edmonton, AB, Canada, (9)UC Berkeley, Berkeley, CA, United States, (10)University of New Hampshire Main Campus, Durham, NH, United States, (11)University of Texas at San Antonio, San Antonio, TX, United States, (12)JHUAPL, Laurel, MD, United States, (13)Northumbria University, Newcastle-Upon-Tyne, United Kingdom
 
Magnetic Signatures of Sympathetic Flares (765687)
Lucas Pauker, Stanford University, Department of Physics, Stanford, CA, United States, Monica Bobra, Stanford University, W.W Hansen Experimental Physics Laboratory, Stanford, CA, United States and Eric Jonas, University of Chicago, Department of Computer Science, Chicago, IL, United States
 
Modeling low-latitude ionospheric vertical drifts using the random forest technique (673040)
Sam Alexander Shidler and Fabiano S Rodrigues, University of Texas at Dallas, Richardson, TX, United States
 
Modeling the dynamic variability of the outer radiation belt fluxes using machine learning (708559)
Donglai Ma1, Xiangning Chu2, Jacob Bortnik1, Seth G Claudepierre3, Harlan E. Spence4, Daniel N Baker2, Shrikanth G Kanekal5 and Hong Zhao2, (1)University of California Los Angeles, Department of Atmospheric and Oceanic Sciences, Los Angeles, CA, United States, (2)Laboratory for Atmospheric and Space Physics, Boulder, CO, United States, (3)The Aerospace Corporation, Santa Monica, CA, United States, (4)University of New Hampshire, Durham, NH, United States, (5)NASA GSFC, Greenbelt, MD, United States
 
Multi-Channel Auto-Calibration for the Atmospheric Imaging Assembly instrument with Deep Learning (756969)
Souvik Bose1, Luiz Fernando Guedes dos Santos2, Valentina Salvatelli3, Brad Neuberg4, Chun Ming Mark Cheung5, Miho Janvier6, Meng Jin3, Yarin Gal7 and Atılım Güneş Baydin8, (1)University of Oslo, Rosseland Center for Solar Physics, Oslo, Norway, (2)Catholic University of America, Physics, Washington, DC, United States, (3)SETI Institute, Mountain View, CA, United States, (4)SETI Institute, Mountain View, United States, (5)Lockheed Martin Solar and Astrophysics Laboratory, Palo Alto, CA, United States, (6)Université Paris-Saclay, CNRS, Institut d'Astrophysique Spatiale, Orsay, France, (7)University of Oxford, Department of Computer Science, Oxford, United Kingdom, (8)University of Oxford, Department of Engineering Science, Oxford, United Kingdom
 
Predicting Ground Magnetic Field Fluctuations from Geomagnetic Storm Data Using a Novel Transformer-Based Model (701443)
Swathi Hari, University of New Hampshire Main Campus, Durham, NH, United States, Jeremiah W Johnson, University of New Hampshire, Manchester, United States, Victor A Pinto, University of New Hampshire Main Campus, Institute for the Study of Earth, Oceans and Space, Durham, NH, United States, Michael Coughlan, University of New Hampshire, Physics, Durham, NH, United States, Amy M Keesee, University of New Hampshire, Physics and Space Science Center, Durham, NH, United States and Hyunju KIM Connor, University of Alaska Fairbanks, Fairbanks, AK, United States
 
SOLSTICE: Space Weather Modeling Meets Machine Learning (663116)
Tamas I Gombosi1, Natalia Y Ganushkina1, Spiro K Antiochos2, Yang Chen3, Alfred O Hero4, David Fouhey5, Enrico Landi6, K D Leka7, Yang Liu8, Ward Manchester9, Gabor Toth10, Shasha Zou9 and SOLSTICE Team, (1)University of Michigan Ann Arbor, Ann Arbor, MI, United States, (2)NASA GSFC, Silver Spring, MD, United States, (3)University of Michigan, Department of Statistics, Ann Arbor, MI, United States, (4)University of Michigan Ann Arbor, Ann Arbor, United States, (5)University of Michigan Ann Arbor, Computer Science, Ann Arbor, MI, United States, (6)University of Michigan, Ann Arbor, MI, United States, (7)NorthWest Research Associates, Boulder, CO, United States, (8)Stanford University, HEPL, Stanford, CA, United States, (9)University of Michigan, Climate and Space Sciences and Engineering, Ann Arbor, MI, United States, (10)University of Michigan, Department of Climate and Space, Center for Space Environment Modeling, Ann Arbor, MI, United States
 
Spread-F Detection and Forecasting Using CNN Autoencoder (771587)
Christopher Luwanga, Nanyang Technological University, Singapore, Singapore, Tzu-Wei Fang, NOAA, Space Weather Prediction Center, Boulder, CO, United States and Amal Chandran, University of Colorado at Boulder, Boulder, CO, United States
 
Using dimensionality reduction and clustering techniques to classify space plasma regimes: electron magnetotail populations (667059)
Mayur Bakrania, Mullard Space Science Laboratory, University College London, London, United Kingdom, Jonathan Rae, Northumbria University, Newcastle, United Kingdom, Andrew P Walsh, European Space Astronomy Centre - ESA-ESAC, Villanueva de la Cañada, Spain, Daniel Verscharen, University of New Hampshire Main Campus, Durham, NH, United States; University College London, Mullard Space Science Laboratory, Dorking, United Kingdom and Andrew W Smith, Mullard Space Science Laboratory, Dorking, United Kingdom
 
Visual Explanation of a Deep Learning Solar Flare Forecast Model and Its Relationship with Physical Parameters (687813)
Kangwoo Yi1, Yong-Jae Moon2, Daye Lim2 and Eunsu Park3, (1)Kyung Hee University, Yongin, Korea, Republic of (South), (2)School of Space Research, Kyung Hee University, Yongin, South Korea, (3)Department of Astronomy & Space Science, Kyung Hee University, Yongin, South Korea
 
What are the solar wind parameters that control fluxes of relativistic electrons at GEO? (745702)
Inaki Esnaola1, Ke Sun2, Richard Boynton1 and Michael A Balikhin3, (1)University of Sheffield, Sheffield, S10, United Kingdom, (2)University of Sheffield, Sheffield, United Kingdom, (3)Univ Sheffield, Sheffield, United Kingdom
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