Solar Energy Particles (SEPs) can be associated with solar flares and coronal mass ejections (CMEs) and offer energy spectra ranging from few KeVs to many GeVs. These events can occur without any notable indication and alter the radiation environment of the inner solar systems, which can potentially lead to precarious conditions for humans in space, affect the interior of spacecraft’s sensitive electronics, and trigger radio blackouts. Identifying the most critical physical parameters of the Solar Dynamic Observatory (SDO) to detect SEPs can allow for a swift response against its adverse effects. With the profusion of high-quality time series data from the SDO, which accounts for the modulating background of magnetic activity and the inherently dynamic phenomenon of pre-flares and post-flare phases; antithetical to non-representative data with the point-in-time measurements employed earlier, selection of vital parameters for solar flare classification using machine learning algorithms appears to be a well-fitted problem in this realm. The primary issue of dealing with multivariate time series data (mvts) is the large number of physical parameters operating at a rapid frequency, making the data dimensionality very high and thus causing the learning process to curb. Moreover, manually selecting vital parameters is a tedious and costly task on which experts may not always agree on the results. In response, we examined feature subset selection using multiple algorithms on both mvts data and the statistical features derived from mvts segments (vectorized data). We used the SWAN-SF (Space Weather Analytics for Solar Flares) benchmark dataset collected from May 2010 - September 2018 to conduct our experiments. The comprehensive study gives a stable scheme to recognize the critical physical parameters, which boosts the learning process and can be used as a blueprint to foretell future solar flare episodes.

A halo Coronal Mass Ejection can have a devastating impact on Earth by causing damage to satellites and electrical transmission line facilities and disrupting radio transmissions. To predict the orientation of the magnetic field (and therefore the occurrence of a geomagnetic storm) associated with an occurring CME, filaments’ sign of magnetic helicity can be used. This would allow us to predict a geomagnetic storm. With the deluge of image data produced by ground-based and space-borne observatories and the unprecedented success of computer vision algorithms in detecting and classifying objects (events) on images, identification of filaments’ chirality appears to be a well-fitted problem in this domain. To be more specific, Deep Learning algorithms with a Convolutional Neural Network (CNN) backbone are made to attack this very type of problem. The only challenge is that these supervised algorithms are data-hungry; their large number of model parameters demand millions of labeled instances to learn. Datasets of filaments with manually identified chirality, however, are costly to be built. This scarcity exists primarily because of the tedious task of data annotation, especially that identification of filaments’ chirality requires domain expertise. In response, we created a pipeline for the augmentation of filaments based on the existing and labeled instances. This Python toolkit provides a resource of unlimited augmented (new) filaments with labeled magnetic helicity signs. Using an existing dataset of H-alpha based manually-labeled filaments as input seeds, collected from August 2000 to 2016 from the big bear solar observatory (BBSO) full-disk solar images, we augment new filament instances by passing labeled filaments through a pipeline of chirality-preserving transformation functions. This augmentation engine is fully compatible with PyTorch, a popular library for deep learning and generates the data based on users requirement.