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1. Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network

   https://doi.org/10.3847/1538-4365/ac5f56  

   Abduallah, Yasser; Jordanova, Vania K.; Liu, Hao; Li, Qin; Wang, Jason T. L.; Wang, Haimin (May 2022, The Astrophysical Journal Supplement Series)

   Abstract Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation. 

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2. DeepSun: machine-learning-as-a-service for solar flare prediction

   https://doi.org/10.1088/1674-4527/21/7/160  

   Abduallah, Yasser; L. Wang, Jason T.; Nie, Yang; Liu, Chang; Wang, Haimin (August 2021, Research in Astronomy and Astrophysics)

   Abstract Solar flare prediction plays an important role in understanding and forecasting space weather. The main goal of the Helioseismic and Magnetic Imager (HMI), one of the instruments on NASA’s Solar Dynamics Observatory, is to study the origin of solar variability and characterize the Sun’s magnetic activity. HMI provides continuous full-disk observations of the solar vector magnetic field with high cadence data that lead to reliable predictive capability; yet, solar flare prediction effort utilizing these data is still limited. In this paper, we present a machine-learning-as-a-service (MLaaS) framework, called DeepSun, for predicting solar flares on the web based on HMI’s data products. Specifically, we construct training data by utilizing the physical parameters provided by the Space-weather HMI Active Region Patch (SHARP) and categorize solar flares into four classes, namely B, C, M and X, according to the X-ray flare catalogs available at the National Centers for Environmental Information (NCEI). Thus, the solar flare prediction problem at hand is essentially a multi-class (i.e., four-class) classification problem. The DeepSun system employs several machine learning algorithms to tackle this multi-class prediction problem and provides an application programming interface (API) for remote programming users. To our knowledge, DeepSun is the first MLaaS tool capable of predicting solar flares through the internet. 

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3. Multiple Electron Acceleration Instances during a Series of Solar Microflares Observed Simultaneously at X-Rays and Microwaves

   https://doi.org/10.3847/1538-4357/ac2aa6  

   Battaglia, Marina; Sharma, Rohit; Luo, Yingjie; Chen, Bin; Yu, Sijie; Krucker, Säm (November 2021, The Astrophysical Journal)

   Abstract Even small solar flares can display a surprising level of complexity regarding their morphology and temporal evolution. Many of their properties, such as energy release and electron acceleration can be studied using highly complementary observations at X-ray and radio wavelengths. We present X-ray observations from the Reuven Ramaty High Energy Solar Spectroscopic Imager and radio observations from the Karl G. Jansky Very Large Array (VLA) of a series of GOES A3.4–B1.6 class flares observed on 2013 April 23. The flares, as seen in X-ray and extreme ultraviolet, originated from multiple locations within active region NOAA 11726. A veritable zoo of different radio emissions between 1 GHz and 2 GHz was observed cotemporally with the X-ray flares. In addition to broadband continuum emission, broadband short-lived bursts and narrowband spikes, indicative of accelerated electrons, were observed. However, these sources were located up to 150″ away from the flaring X-ray sources but only some of these emissions could be explained as signatures of electrons that were accelerated near the main flare site. For other sources, no obvious magnetic connection to the main flare site could be found. These emissions likely originate from secondary acceleration sites triggered by the flare, but may be due to reconnection and acceleration completely unrelated to the cotemporally observed flare. Thanks to the extremely high sensitivity of the VLA, not achieved with current X-ray instrumentation, it is shown that particle acceleration happens frequently and at multiple locations within a flaring active region. 

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4. A Multimodal Dataset of SDO/HMI Magnetic Polarity Inversions for Solar Flare Forecasting

   https://doi.org/10.7910/DVN/4A7ORF  

   Mansouri, Reza; Khani, Ziba; Aydin, Berkay; Mansouri, Reza (January 2024, Harvard Dataverse)

   Magnetic polarity inversion lines (PILs) in solar active regions are key to triggering flares and eruptions. Recently, engineered PIL features have been used for predicting solar eruptions. Derived from the original PIL dataset, using line-of-sight (LoS) magnetograms provided by the Solar Dynamics Observatory's (SDO) Helioseismic and Magnetic Imager (HMI) Active Region Patches (HARPs), we provide a publicly available comprehensive dataset in a supervised format, where each instance includes a raster of Polarity Inversion Lines (PILs), one of the polarity convex hull, and a multivariate time-series of properties related to PILs. Using SDO-GOES integrated flares historical data covering May 2010 to January 2019, we have assigned each of the instances their corresponding class of flare, FQ, C, M or X. By integrating these diverse data modalities, our approach aims to improve the accuracy of solar flare predictions. Initial findings suggest that the multimodal approach can uncover new patterns and relationships, potentially leading to breakthroughs in predictive accuracy and more effective mitigation strategies against the impacts of solar activities. 

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5. Solar flare catalog based on SDO/AIA EUV images: Composition and correlation with GOES/XRS X-ray flare magnitudes

   https://doi.org/10.3389/fspas.2022.1031211  

   van der Sande, Kiera; Flyer, Natasha; Berger, Thomas E.; Gagnon, Riana (November 2022, Frontiers in Astronomy and Space Sciences)

   Supervised Machine Learning (ML) models for solar flare prediction rely on accurate labels for a given input data set, commonly obtained from the GOES/XRS X-ray flare catalog. With increasing interest in utilizing ultraviolet (UV) and extreme ultraviolet (EUV) image data as input to these models, we seek to understand if flaring activity can be defined and quantified using EUV data alone. This would allow us to move away from the GOES single pixel measurement definition of flares and use the same data we use for flare prediction for label creation. In this work, we present a Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA)-based flare catalog covering flare of GOES X-ray magnitudes C, M and X from 2010 to 2017. We use active region (AR) cutouts of full disk AIA images to match the corresponding SDO/Helioseismic and Magnetic Imager (HMI) SHARPS (Space weather HMI Active Region Patches) that have been extensively used in ML flare prediction studies, thus allowing for labeling of AR number as well as flare magnitude and timing. Flare start, peak, and end times are defined using a peak-finding algorithm on AIA time series data obtained by summing the intensity across the AIA cutouts. An extremely randomized trees (ERT) regression model is used to map SDO/AIA flare magnitudes to GOES X-ray magnitude, achieving a low-variance regression. We find an accurate overlap on 85% of M/X flares between our resulting AIA catalog and the GOES flare catalog. However, we also discover a number of large flares unrecorded or mislabeled in the GOES catalog. 

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