Forecasting Flare Activity Using Deep Convolutional Neural Networks
Abstract
Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFMSM23A2581H
- Keywords:
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- 1942 Machine learning;
- INFORMATICS;
- 1986 Statistical methods: Inferential;
- INFORMATICS;
- 2799 General or miscellaneous;
- MAGNETOSPHERIC PHYSICS;
- 7924 Forecasting;
- SPACE WEATHER