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Related Experiment Videos

Multiple solar particle event dose time profile predictions using Bayesian inference.

J S Neal1, L W Townsend

  • 1Department of Nuclear Engineering, The University of Tennessee, Knoxville, TN 37996-2300, USA. jsvbneal@comcast.net

Radiation Protection Dosimetry
|April 11, 2006
PubMed
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This study enhances space weather forecasting by developing a criterion to identify multiple solar particle events. This allows for more accurate predictions of radiation effects on humans and electronics.

Area of Science:

  • Space physics
  • Astrophysics
  • Radiation protection

Background:

  • Solar particle events pose significant risks to space missions and electronics.
  • Accurate prediction of these events and their effects is crucial for the National Aeronautics and Space Administration (NASA) and satellite operators.
  • Existing forecasting methods, while effective for single events, are limited in predicting multiple, complex solar particle events.

Purpose of the Study:

  • To generalize Bayesian inference methods for space weather prediction to accommodate multiple solar particle events.
  • To develop a criterion for characterizing solar particle events as single or multiple.
  • To improve the prediction accuracy of radiation dose and dose rate time profiles for all types of solar particle events.

Main Methods:

Related Experiment Videos

  • Analysis of nine large solar particle events, including single and multiple event types.
  • Integration of particle flux and fluence data with dose rate and dose calculations.
  • Application and extension of Bayesian inference for forecasting.

Main Results:

  • A criterion for distinguishing between single and multiple solar particle events was developed.
  • The generalized Bayesian methodology was applied to predict dose time profiles for the four sub-events of the October 1989 event.
  • The study successfully linked particle data with dose calculations to characterize event complexity.

Conclusions:

  • The developed criterion enables the generalization of Bayesian forecasting for all solar particle events, including complex multiple events.
  • Improved prediction capabilities enhance the safety of humans and electronics in space.
  • This research advances space weather forecasting by addressing the challenge of multiple solar particle events.