This study explores the behavior of machine-learning-based flare forecasting models deployed in a simulated operational environment. Using Georgia State University’s Space Weather Analytics for Solar Flares benchmark data set, we examine the impacts of training methodology and the solar cycle on decision tree, support vector machine, and multilayer perceptron performance. We implement our classifiers using three temporal training windows: stationary, rolling, and expanding. The stationary window trains models using a single set of data available before the first forecasting instance, which remains constant throughout the solar cycle. The rolling window trains models using data from a constant time interval before the forecasting instance, which moves with the solar cycle. Finally, the expanding window trains models using all available data before the forecasting instance. For each window, a number of input features (1, 5, 10, 25, 50, and 120) and temporal sizes (5, 8, 11, 14, 17, and 20 months) were tested. To our surprise, we found that, for a window of 20 months, skill scores were comparable regardless of the window type, feature count, and classifier selected. Furthermore, reducing the size of this window only marginally decreased stationary and rolling window performance. This implies that, given enough data, a stationary window can be chosen over other window types, eliminating the need for model retraining. Finally, a moderately strong positive correlation was found to exist between a model’s false-positive rate and the solar X-ray background flux. This suggests that the solar cycle phase has a considerable influence on forecasting.
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Ahmadzadeh, Azim ; Adhyapak, Rohan ; Chaurasiya, Kartik ; Nagubandi, Laxmi Alekhya ; Aparna, V ; Martens, Petrus C ; Pevtsov, Alexei ; Bertello, Luca ; Pevtsov, Alexander ; Douglas, Naomi ; et al ( , Harvard Dataverse)MAGFiLO is a dataset of manually annotated solar filaments from H-Alpha observations captured by the Global Oscillation Network Group (GONG). This dataset includes over ten thousand annotated filaments, spanning the years 2011 through 2022. Each annotation details one filament's segmentation, minimum bounding box, spine, and magnetic field chirality. MAGFiLO is the first dataset of its size, enabling advanced deep learning models to identify filaments and their features with unprecedented precision. It also provides a testbed for solar physicists interested in large-scale analysis of filaments.more » « less
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Ahmadzadeh, Azim ; Kempton, Dustin J. ; Martens, Petrus C. ; Angryk, Rafal A. ( , IEEE Transactions on Pattern Analysis and Machine Intelligence)
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Angryk, Rafal A. ; Martens, Petrus C. ; Aydin, Berkay ; Kempton, Dustin ; Mahajan, Sushant S. ; Basodi, Sunitha ; Ahmadzadeh, Azim ; Cai, Xumin ; Filali Boubrahimi, Soukaina ; Hamdi, Shah Muhammad ; et al ( , Scientific Data)
Abstract We introduce and make openly accessible a comprehensive, multivariate time series (MVTS) dataset extracted from solar photospheric vector magnetograms in Spaceweather HMI Active Region Patch (SHARP) series. Our dataset also includes a cross-checked NOAA solar flare catalog that immediately facilitates solar flare prediction efforts. We discuss methods used for data collection, cleaning and pre-processing of the solar active region and flare data, and we further describe a novel data integration and sampling methodology. Our dataset covers 4,098 MVTS data collections from active regions occurring between May 2010 and December 2018, includes 51 flare-predictive parameters, and integrates over 10,000 flare reports. Potential directions toward expansion of the time series, either “horizontally” – by adding more prediction-specific parameters, or “vertically” – by generalizing flare into integrated solar eruption prediction, are also explained. The immediate tasks enabled by the disseminated dataset include: optimization of solar flare prediction and detailed investigation for elusive flare predictors or precursors, with both operational (research-to-operations), and basic research (operations-to-research) benefits potentially following in the future.