Predicting the Energetic Proton Flux with a Machine Learning Regression Algorithm

The need for real-time monitoring and alerting systems for space weather hazards has grown significantly in the last two decades. One of the most important challenges for space mission operations and planning is the prediction of solar proton events (SPEs). In this context, artificial intelligence a...

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Bibliographic Details
Published in:The Astrophysical journal Vol. 975; no. 1; pp. 8 - 19
Main Authors: Stumpo, Mirko, Laurenza, Monica, Benella, Simone, Marcucci, Maria Federica
Format: Journal Article
Language:English
Published: Philadelphia The American Astronomical Society 01.11.2024
IOP Publishing
Subjects:
Algorithms
Artificial intelligence
Deep space
Electron flux
Fluctuations
Interplanetary physics
Machine learning
Monitoring
Onboard equipment
Operational hazards
Predictions
Proton flux
Real time operation
Solar energetic particles
Solar protons
Solar radiation
Solar storms
Space exploration
Space missions
Space weather
Statistical analysis
Statistical forecasting
Weather hazards
ISSN:0004-637X, 1538-4357
Online Access:Get full text
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Summary:The need for real-time monitoring and alerting systems for space weather hazards has grown significantly in the last two decades. One of the most important challenges for space mission operations and planning is the prediction of solar proton events (SPEs). In this context, artificial intelligence and machine learning techniques have opened a new frontier, providing a new paradigm for statistical forecasting algorithms. The great majority of these models aim to predict the occurrence of an SPE, i.e., they are based on the classification approach. This work is oriented toward the successful implementation of onboard prediction systems, which is essential for the future of space exploration. We present a simple and efficient machine learning regression algorithm that is able to forecast the energetic proton flux up to 1 hr ahead by exploiting features derived from the electron flux only. This approach could be helpful in improving monitoring systems of the radiation risk in both deep space and near-Earth environments. The model is very relevant for mission operations and planning, especially when flare characteristics and source location are not available in real time, as at Mars distance.
Bibliography:AAS55896
The Sun and the Heliosphere
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ISSN:0004-637X
1538-4357
DOI:10.3847/1538-4357/ad7734