NASA's Artemis Human Landing Systems

Watson-Morgan, Lisa; Hawkins, Lakiesha V.; Jacobs, Bill; Polsgrove, Tara; Krupp, D.; Percy, Thomas K.; Vermette, Joseph

doi:10.1109/aero53065.2022.9843313

Cited by 1 publication

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“…Accurate prediction of SEP events is crucial to ensure the safety and success of space missions and interplanetary exploration. In light of the National Aeronautics and Space Administration's (NASA) ambitious plans to explore the Moon and Mars in the coming decades (Smith et al., 2020; Watson‐Morgan et al., 2023), the urgency of developing advanced monitoring and predictive systems for SEP events cannot be overstated.…”

Section: Introductionmentioning

confidence: 99%

Toward Enhanced Prediction of High‐Impact Solar Energetic Particle Events Using Multimodal Time Series Data Fusion Models

Hosseinzadeh,

Filali Boubrahimi,

Hamdi

2024

Space Weather

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Solar energetic particle (SEP) events, originating from solar flares and Coronal Mass Ejections, present significant hazards to space exploration and technology on Earth. Accurate prediction of these high‐energy events is essential for safeguarding astronauts, spacecraft, and electronic systems. In this study, we conduct an in‐depth investigation into the application of multimodal data fusion techniques for the prediction of high‐energy SEP events, particularly ∼100 MeV events. Our research utilizes six machine learning (ML) models, each finely tuned for time series analysis, including Univariate Time Series (UTS), Image‐based model (Image), Univariate Feature Concatenation (UFC), Univariate Deep Concatenation (UDC), Univariate Deep Merge (UDM), and Univariate Score Concatenation (USC). By combining time series proton flux data with solar X‐ray images, we exploit complementary insights into the underlying solar phenomena responsible for SEP events. Rigorous evaluation metrics, including accuracy, F1‐score, and other established measures, are applied, along with K‐fold cross‐validation, to ensure the robustness and generalization of our models. Additionally, we explore the influence of observation window sizes on classification accuracy.

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Section: Introductionmentioning

confidence: 99%