Predicting dose-time profiles of solar energetic particle events using Bayesian forecasting methods

John S. Neal; Lawrence W. Townsend

doi:10.1109/23.983163

Predicting dose-time profiles of solar energetic particle events using Bayesian forecasting methods

John S. Neal, Lawrence W. Townsend

Isotope Applications Research

Research output: Contribution to journal › Conference article › peer-review

15 Scopus citations

Abstract

Bayesian inference techniques, coupled with Markov chain Monte Carlo sampling methods, are used to predict dose-time profiles for energetic solar particle events. Inputs into the predictive methodology are dose and dose-rate measurements obtained early in the event. Surrogate dose values are grouped in hierarchical models to express relationships among similar solar particle events. Models assume nonlinear, sigmoidal growth for dose throughout an event. Markov chain Monte Carlo methods are used to sample from Bayesian posterior predictive distributions for dose and dose rate. Example predictions are provided for the November 8, 2000, and August 12, 1989, solar particle events.

Original language	English
Pages (from-to)	2004-2009
Number of pages	6
Journal	IEEE Transactions on Nuclear Science
Volume	48
Issue number	6 I
DOIs	https://doi.org/10.1109/23.983163
State	Published - Dec 2001
Event	2001 Nuclear and Sapce Radiation Effects Conference (NSREC) - Vancouver, BC, Canada Duration: Jul 16 2001 → Jul 20 2001

Funding

Manuscript received July 17, 2001. This work was supported in part by the National Aeronautics and Space Administration Graduate Student Researchers Program. J. S. Neal was with the University of Tennessee, Knoxville, TN 37996 USA. He is now with the Nuclear Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831 USA (e-mail: [email protected]). L. W. Townsend is with the Nuclear Engineering Department, University of Tennessee, Knoxville, TN 37996-2300 USA (e-mail: [email protected]). Publisher Item Identifier S 0018-9499(01)10682-9.

Keywords

Bayesian interface
Dose prediction
Solar particle events

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10.1109/23.983163

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abstract = "Bayesian inference techniques, coupled with Markov chain Monte Carlo sampling methods, are used to predict dose-time profiles for energetic solar particle events. Inputs into the predictive methodology are dose and dose-rate measurements obtained early in the event. Surrogate dose values are grouped in hierarchical models to express relationships among similar solar particle events. Models assume nonlinear, sigmoidal growth for dose throughout an event. Markov chain Monte Carlo methods are used to sample from Bayesian posterior predictive distributions for dose and dose rate. Example predictions are provided for the November 8, 2000, and August 12, 1989, solar particle events.",

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AB - Bayesian inference techniques, coupled with Markov chain Monte Carlo sampling methods, are used to predict dose-time profiles for energetic solar particle events. Inputs into the predictive methodology are dose and dose-rate measurements obtained early in the event. Surrogate dose values are grouped in hierarchical models to express relationships among similar solar particle events. Models assume nonlinear, sigmoidal growth for dose throughout an event. Markov chain Monte Carlo methods are used to sample from Bayesian posterior predictive distributions for dose and dose rate. Example predictions are provided for the November 8, 2000, and August 12, 1989, solar particle events.

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ER -

Predicting dose-time profiles of solar energetic particle events using Bayesian forecasting methods

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