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Activity Number: 11 - Statistical Inference for Solar and Geophysical Data
Type: Invited
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract #309392
Title: Solar Flare Prediction with Machine Learning
Author(s): Yang Chen*
Companies: University of Michigan
Keywords: time series; prediction; machine learning

We present our machine learning efforts, which show great promise towards early predictions of solar flare events. First, we present a data pre-processing pipeline that is built to extract useful data from multiple sources -- Geostationary Operational Environmental Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) and SDO/Atmospheric Imaging Assembly (AIA) -- to prepare inputs for machine learning algorithms. Second, we adopt deep learning algorithms to extract/select features from raw HMI and AIA data. Third, we train machine learning models that capture both the spatial and temporal information from HMI magnetogram data for strong/weak flare classification and for predictions of flare intensities. Fourth, we show that using the ML-derived features gives almost as good performance as using active region parameters provided in HMI data files, i.e. features manually constructed based on physical principles. Last, case studies show a significant increase in the prediction score around 20 hours before strong solar flare events, which implies that early precursors appear at least 20 hours prior to the peak of a flare event.

Authors who are presenting talks have a * after their name.

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