PRESAGE: PREdicting Solar Activity using machine learning on heteroGEneous data
ANR JCJC PRESAGE 2021-2025
Summary
PRESAGE brings together data scientists and solar physicists to develop new machine learning methods that support a deeper understanding of the mechanisms of solar activity in order to predict its events. The solar physics community is currently facing a deluge of data, which is too widely varied and complex to allow an overall analysis leading to a global understanding of solar activity. We propose to solve this problem by developing new machine learning algorithms that exploit these heterogeneous data, to:
- study the properties in 3D of objects of the solar atmosphere (filaments, sunspots…),
- model their evolutions and behaviors,
- study the correlations between many indicators of solar activity (inc. solar objects and their behaviors), solar activity events (flares, CMEs…), and their resulting terrestrial impacts (geomagnetic indices…), and
- use these new insights to predict the events of solar activity and their effects on Earth.
Partner: Observatoire de Paris
Funding body: ANR
Role in the project: Principal Investigator
Preliminary study: PhD co-supervision: Localisation of solar active regions from multispectral images, Majedaldein AlMahasneh, 2017-2022