Technical Papers – Machine Learning for SDA Applications

A Common Task Framework for Testing and Evaluation at the Space Domain Awareness Tools, Applications, and Processing Lab

Imène Goumiri, Lawrence Livermore National Laboratory; Luc Peterson, Lawrence Livermore National Laboratory; Ashley Cocciadiferro, Lawrence Livermore National Laboratory; Ryan Lee, Lawrence Livermore National Laboratory; Jason Bernstein, Lawrence Livermore National Laboratory

Keywords: Space domain awareness, test and evaluation, machine learning, conjunction, RPO

Action-Free Inverse Reinforcement Learning for Evaluating Satellite Similarity and Anomaly Detection

David Witman, Slingshot Aerospace; Timothy Olson, Slingshot Aerospace; Brian Williams, Slingshot Aerospace; Dylan Kesler, Slingshot Aerospace; Belinda Marchand, Slingshot Aerospace

Keywords: Machine Learning, Anomaly Detection, Constellations

Backbone Architectures for Space Domain Awareness

Kyle Merry, Sandia National Laboratories; John Ossorgin, Sandia National Laboratories; Zachary Mekus, Sandia National Laboratories

Keywords: ML/AI, SDA, Transformer, Swin Transformer, Convolutional Neural Network, CNN, Feature Extraction, Backbone

Building Trust in Human-Machine Teaming for Autonomous Space Sensing

Garrett Fitzgerald, U. S. Space Force; Rachel Morris, Applied Decision Science; Laura G. Militello, Applied Decision Science; Justin Fletcher, USSF/SSC

Keywords: Human-Machine Teaming, Cognitive Task Analysis, Space Domain Awareness, Trust in Autonomy, Telescope C2

Data-Driven Identification of Main Behavioural Classes and Characteristics of Resident Space Objects in LEO Through Unsupervised Learning

Marta Guimaraes, Neuraspace; Claudia Soares, FCT-UNL; Chiara Manfletti, Neuraspace

Keywords: Space Situational Awareness, Space Debris, Unsupervised Learning, Clustering

Deep Reinforcement Learning Applications to Space Situational Awareness Scenarios

Benedict Oakes, University of Liverpool; Jason F. Ralph, University of Liverpool; Jordi Barr, Defence Science Technology Laboratory

Keywords: Deep Reinforcement Learning, Machine Learning for SDA Applications, Space Situational Awareness

Detecting Satellites with Object Detection: Challenges of Implementing Deep Learning Techniques for Space-based Images

Shane Ryall, Defense Research & Development Canada

Keywords: Image Processing, Deep Learning, SSA, NEOSSat

Enhancing Unknown Near-Earth Object Detection with Synthetic Tracking and Convolutional Neural Networks

Kevin Phan, EO Solutions; Justin Fletcher, Space Systems Command (A&AS)

Keywords: Satellite Detection, Synthetic Tracking, Convolutional Neural Networks, Signal-to-Noise Ratio, Machine Learning, Near-Earth Object Detection, Automation.

Integrating LLMs with SatSim for Enhanced Satellite Tracking and Identification

Enrique De Alba, EO Solutions; Marco de Lannoy Kobayashi, EO Solutions; Alex Cabello, EO Solutions

Keywords: Large Language Models, Space Domain Awareness, Synthetic Data Generation, Natural Language Processing, Space Surveillance Simulation, Automation in SDA

Machine Learning for E-O Data and Imagery Event Detection

John Ebeling, Data Fusion & Neural Networks, LLC; Duane DeSieno, Data Fusion & Neural Networks, LLC; Jacob Hansen, Data Fusion & Neural Networks, LLC; Christopher Tschan, Data Fusion & Neural Networks, LLC; Carolyn Sheaff, Air Force Research Laboratory/RIED

Keywords: Electro-Optical (E-O), space surveillance observations, space surveillance imagery, space domain awareness, machine learning

Machine Learning for Space Domain Awareness Sensor Scheduling

Neil Dhingra, Auria; Cameron DeJac, Auria; Clayton McGuire, Auria

Keywords: Sensor Resource Management, Sensor Tasking, Space Domain Awareness, Space Situational Awareness, Global Optimality, NP Hard Problems, Machine Learning, Supervised Learning, Reinforcement Learning

Neural Network Enhanced Numerical Propagation to Enhance SSA/SDA

Duane DeSieno, Data Fusion & Neural Networks, LLC

Keywords: Neural networks, Enhanced orbit propagation, Trajectory propagation, Machine Learning

Rapid and Uncertainty Quantified Orbital Propagation Using Uncertainty-Aware AI

Kevin Vanslette, Raytheon BBN; Alexander Lay, Raytheon BBN; David Kusterer, Virginia Tech Institute for National Security; Kevin Schroeder, Virginia Tech Institute for National Security

Keywords: Artificial Intelligence, Orbital Propagation, Uncertainty

Regularizing Training of Physics Informed Neural Networks (PINNs) for Cislunar Orbit Determination via Transfer Learning

Gregory Badura, Georgia Tech Research Institute; Miguel Velez-Reyes, University of Texas at El Paso; Brian Gunter, Georgia Institute of Technology; Christopher Valenta, Georgia Tech Research Institute; Koki Ho, Georgia Institute of Technology

Keywords: Machine Learning, Physics-Informed Machine Learning, Cislunar, Space Domain Awareness, Orbit Determination

Resolved Hyperspectral Imaging

Kimmy Chang, SSC/SZGA; Lauren Fisher, KBR; Zach Gazak, SSC/SZGA; Justin Fletcher, SSC/SZGA

Keywords: Spectroscopy, Machine Learning, Small Telescopes, Hyperspectral, Convolutional Neural Networks, Segmentation, Classification