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Title:
SoFAST: Automated Flare Detection with the PROBA2/SWAP EUV Imager
Authors:
Bonte, K.; Berghmans, D.; De Groof, A.; Steed, K.; Poedts, S.
Affiliation:
AA(Centre for mathematical Plasma-Astrophysics, Department of Mathematics, KU Leuven), AB(Royal Observatory of Belgium), AC(European Space Agency), AD(Centre for mathematical Plasma-Astrophysics, Department of Mathematics, KU Leuven), AE(Centre for mathematical Plasma-Astrophysics, Department of Mathematics, KU Leuven)
Publication:
Solar Physics, Volume 286, Issue 1, pp.185-199 (SoPh Homepage)
Publication Date:
08/2013
Origin:
SPRINGER
Keywords:
Sun: observations, Sun: flares, Sun: instrumentation and data management, Sun: EUV, Sun: corona, Techniques: image processing, PROBA2, SWAP
Abstract Copyright:
(c) 2013: Springer Science+Business Media Dordrecht
DOI:
10.1007/s11207-012-0165-8
Bibliographic Code:
2013SoPh..286..185B

Abstract

The Sun Watcher with Active Pixels and Image Processing (SWAP) EUV imager onboard PROBA2 provides a non-stop stream of coronal extreme-ultraviolet (EUV) images at a cadence of typically 130 seconds. These images show the solar drivers of space-weather, such as flares and erupting filaments. We have developed a software tool that automatically processes the images and localises and identifies flares. On one hand, the output of this software tool is intended as a service to the Space Weather Segment of ESA's Space Situational Awareness (SSA) program. On the other hand, we consider the PROBA2/SWAP images as a model for the data from the Extreme Ultraviolet Imager (EUI) instrument prepared for the future Solar Orbiter mission, where onboard intelligence is required for prioritising data within the challenging telemetry quota. In this article we present the concept of the software, the first statistics on its effectiveness and the online display in real time of its results. Our results indicate that it is not only possible to detect EUV flares automatically in an acquired dataset, but that quantifying a range of EUV dynamics is also possible. The method is based on thresholding of macropixelled image sequences. The robustness and simplicity of the algorithm is a clear advantage for future onboard use.
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