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Automatic analysis of solar eruptions

 

Astronomical observation missions collect data in such large quantities that they can no longer be analysed manually, but only automatically. The present project uses methods of machine learning in solar research to better understand and predict solar eruptions.

Portrait / project description (ongoing research project)

The IRIS Mission has been collecting data from various layers of the sun’s atmosphere since 2013. The resulting data set will provide us with a deeper knowledge of the physics of the sun. But first a way must be found to automatically characterise and search the enormous quantities of data. We are developing methods that enable computers to learn to recognise patterns in the IRIS archive and to characterise the temporal progression of the solar eruptions observed. Building on this, we would like to significantly improve our understanding of and ability to predict solar eruptions.

Background

The regular eruptions that take place on the sun can cause disturbances on Earth – for instance in radio and GPS positioning systems – as well as power outages. To date, scientists have been unable to explain the physical cause of solar eruptions, nor can they reliably predict them. The fact that solar eruptions occur in diverse complex spatial and temporal patterns makes it considerably more difficult to systematically analyse these phenomena.

Aim

The objective of this project is to gain a better understanding of the physics of the sun and to develop methods for predicting solar eruptions. We will be using the huge data archive compiled by IRIS (Interface Region Imaging Spectrograph), NASA’s latest solar satellite. We will create machine learning algorithms to evaluate the data for spatial and temporal patterns.

Relevance/application

Since solar eruptions can cause widespread interference on Earth, a great deal of importance is attached to being able to predict them. This would be helpful for flight planning and for the operation of satellites and power grids and, accordingly, reduce any damage from eruptions. Moreover, the algorithms and image processing methods we develop could be used for the analysis of other data sets in science or industry.

Original title

Machine Learning based Analytics for Big Data in Astronomy

Project leaders

  • Professor Svyatoslav Voloshynovskiy, Centre Universitaire d'Informatique, Université de Genève
  • Prof. Samuel Krucker, Fachhochschule Nordwestschweiz, Hochschule für Technik, Windisch
  • Prof. Martin Melchior, Fachhochschule Nordwestschweiz, Hochschule für Technik, Windisch

 

 

Further information on this content

 Contact

Professor Svyatoslav Voloshynovskiy Centre Universitaire d'Informatique
Université de Genève
Bâtiment Battelle A
Route de Drize 7 1227 Carouge svolos@unige.ch

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