The Analysis of Solar Flares Using Machine Learning

Diplôme Thèse The Analysis of Solar Flares Using Machine Learning eng 2021-05-10 author Panos Brandon-Leigh 876540 director Kleint Lucia 940444 director Voloshynovskyy Svyatoslav 514089 director Krucker Samuel 10.13097/archive-ouverte/unige:153812 urn:nbn:ch:unige-1538126 Sc. 5560 The Sun is capable of generating massive explosions called solar flares. The energy powering these events is derived from the Sun's complex magnetic field and is released spontaneously in a process known as "magnetic reconnection". During such an event, the entire electromagnetic spectrum experiences an enhancement, and a variety of instruments analyze different aspects of the spectrum to uncover the underlying physics of flares. Because solar flares can negatively impact the earth's geomagnetic envelope and compromise many of the electronics that our society depends on, it has become paramount to better understand and possibly predict these events. In service of these ambitions, we use modern machine learning techniques to analyze large volumes of spectral data collected by NASA's IRIS satellite. We find techniques that allow us to automatically analyze millions of spectra efficiently, calculate information-theoretic quantities, as well as provide the first flare prediction results using a novel spectral dataset. Dewey 025.063 Swiss National Science Foundation Machine Learning based Analytics for Big Data in Astronomy 167158 CH Programmes 32 Département d'informatique 18 false 46a7027e-10fa-4ac8-b58b-b7ff5a582173 Thesis Public PhD_Dissertation.pdf 61946536 application/pdf openAccess Faculté des sciences / Département d'informatique

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The Analysis of Solar Flares Using Machine Learning				2021	907	164

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Panos, Brandon-Leigh