Determining properties of solar active regions using machine learning

Karen A. Meyer*, Nikolay Panayotov, Salma Hamdy Elsayed, Kean Lee Kang, Duncan Mackay

*Corresponding author for this work

Research output: Contribution to conferenceAbstract

Abstract

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.
Original languageEnglish
Pages50-51
Number of pages1
Publication statusPublished - 16 Sep 2019
EventMachine Learning in Heliophysics - The Royal Tropical Institute - Koninklijk Instituut voor de Tropen (KIT), Amsterdam, Netherlands
Duration: 16 Sep 201920 Sep 2019
https://ml-helio.github.io/

Conference

ConferenceMachine Learning in Heliophysics
Abbreviated titleML-Helio 2019
CountryNetherlands
CityAmsterdam
Period16/09/1920/09/19
Internet address

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