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 n 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 n Heliophysics
Abbreviated titleML-Helio 2019
CountryNetherlands
CityAmsterdam
Period16/09/1920/09/19
Internet address

Fingerprint

Artificial intelligence
Learning systems
Statistics
Fluxes
Deep neural networks

Cite this

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 n Heliophysics, Amsterdam, Netherlands.
Meyer, Karen A. ; Panayotov, Nikolay ; Elsayed, Salma Hamdy ; Kang, Kean Lee ; Mackay, Duncan. / Determining properties of solar Active Regions using Machine Learning. Abstract from Machine Learning n Heliophysics, Amsterdam, Netherlands.1 p.
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author = "Meyer, {Karen A.} and Nikolay Panayotov and Elsayed, {Salma Hamdy} and Kang, {Kean Lee} and Duncan Mackay",
year = "2019",
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language = "English",
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note = "Machine Learning n Heliophysics, ML-Helio 2019 ; Conference date: 16-09-2019 Through 20-09-2019",
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Meyer, KA, Panayotov, N, Elsayed, SH, Kang, KL & Mackay, D 2019, 'Determining properties of solar Active Regions using Machine Learning', Machine Learning n Heliophysics, Amsterdam, Netherlands, 16/09/19 - 20/09/19 pp. 50-51.

Determining properties of solar Active Regions using Machine Learning. / Meyer, Karen A.; Panayotov, Nikolay; Elsayed, Salma Hamdy; Kang, Kean Lee; Mackay, Duncan.

2019. 50-51 Abstract from Machine Learning n Heliophysics, Amsterdam, Netherlands.

Research output: Contribution to conferenceAbstract

TY - CONF

T1 - Determining properties of solar Active Regions using Machine Learning

AU - Meyer, Karen A.

AU - Panayotov, Nikolay

AU - Elsayed, Salma Hamdy

AU - Kang, Kean Lee

AU - Mackay, Duncan

PY - 2019/9/16

Y1 - 2019/9/16

N2 - 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.

AB - 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.

M3 - Abstract

SP - 50

EP - 51

ER -

Meyer KA, Panayotov N, Elsayed SH, Kang KL, Mackay D. Determining properties of solar Active Regions using Machine Learning. 2019. Abstract from Machine Learning n Heliophysics, Amsterdam, Netherlands.