Bayesian prediction of solar flares
Summary
Large solar flares pose risks to our local space weather environment but they occur unpredictably; this project aims to improve flare prediction using Bayesian methods.
Supervisor(s)
Associate Professor Mike Wheatland
Research Location
Program Type
Masters/PHD
Synopsis
The largest solar flares produce hazardous "space weather" conditions near the Earth. They appear to occur at random, but there are many indicators that flares might occur. Can we combine these indicators to make a more accurate forecast? What limits the predictability of flares? This project will apply state-of-the-art techniques from Bayesian inference to these key problems.
Additional Information
Suitable for: Honours or Ph.D.
Techniques involved in the project: Bayesian theory, Markov chain Monte-Carlo methods, predictive discrimination, numerical methods, data analysis, visualisation
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Keywords
physics, Bayesian probability, inference, prediction, magnetic fields, computation, numerical methods, Monte-Carlo, markov chain, solar physics, Sun, data analysis
Opportunity ID
The opportunity ID for this research opportunity is: 724
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