Advances in Machine Learning techniques mean that we now have the ability to analyse and derive statistics from large datasets of complex features. This project aims to train an artificial intelligence (including a deep neural network) to extract a consistent dataset of desired properties of solar Active Regions (ARs) from three decades of observed synoptic magnetograms. The methodology is initially developed using simulation data from a well-established flux transport model. This will then be adapted for application to observed synoptic maps, a subset of which have the desired AR properties catalogued, for verification of the technique.
|Number of pages||1|
|Publication status||Published - 16 Sep 2019|
|Event||Machine Learning in Heliophysics - The Royal Tropical Institute - Koninklijk Instituut voor de Tropen (KIT), Amsterdam, Netherlands|
Duration: 16 Sep 2019 → 20 Sep 2019
|Conference||Machine Learning in Heliophysics|
|Abbreviated title||ML-Helio 2019|
|Period||16/09/19 → 20/09/19|
Meyer, K. A., Panayotov, N., Elsayed, S. H., Kang, K. L., & Mackay, D. (2019). Determining properties of solar active regions using machine learning. 50-51. Abstract from Machine Learning in Heliophysics, Amsterdam, Netherlands.