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1. Operational prediction of solar flares using a transformer-based framework

   https://doi.org/10.1038/s41598-023-40884-1  

   Abduallah, Yasser ; Wang, Jason T. L. ; Wang, Haimin ; Xu, Yan ( August 2023 , Scientific Reports)

   Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a$$\gamma$$$\gamma$-class flare within the next 24 to 72 h. We consider three$$\gamma$$$\gamma$classes, namely the$$\ge$$$\ge$M5.0 class, the$$\ge$$$\ge$M class and the$$\ge$$$\ge$C class, and build three transformers separately, each corresponding to a$$\gamma$$$\gamma$class. Each transformer is used to make predictions of its corresponding$$\gamma$$$\gamma$-class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web.

    

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2. Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods

   https://doi.org/10.3847/1538-4365/ac9b17  

   Zhang, Hewei ; Li, Qin ; Yang, Yanxing ; Jing, Ju ; Wang, Jason T. L. ; Wang, Haimin ; Shang, Zuofeng ( December 2022 , The Astrophysical Journal Supplement Series)

   Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections. They are the most important sources of space weather effects, which can severely impact the near-Earth environment. Thus it is essential to forecast flares (especially the M- and X-class ones) to mitigate their destructive and hazardous consequences. Here, we introduce several statistical and machine-learning approaches to the prediction of an active region’s (AR) flare index (FI) that quantifies the flare productivity of an AR by taking into account the number of different class flares within a certain time interval. Specifically, our sample includes 563 ARs that appeared on the solar disk from 2010 May to 2017 December. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, characterize coronal magnetic energy stored in ARs by proxy and are used as the predictors. We investigate the relationship between these SHARP parameters and the FI of ARs with a machine-learning algorithm (spline regression) and the resampling method (Synthetic Minority Oversampling Technique for Regression with Gaussian Noise). Based on the established relationship, we are able to predict the value of FIs for a given AR within the next 1 day period. Compared with other four popular machine-learning algorithms, our methods improve the accuracy of FI prediction, especially for a large FI. In addition, we sort the importance of SHARP parameters by the Borda count method calculated from the ranks that are rendered by nine different machine-learning methods.

    

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3. 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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4. Deep Neural Networks Based Solar Flare Prediction Using Compressed Full-disk Line-of-sight Magnetograms

   Pandey C. ; Angryk R. ; Aydin B. ( April 2022 , Communications in computer and information science)

   Lossio-Ventura J.A. ; Valverde-Rebaza J. ; Diaz E. ; Muñante D. ; Gavidia-Calderon C. ; Baria Valejo A.D. ; Alatrista-Salas H. (Ed.)

   The efforts in solar flare prediction have been engendered by the advancements in machine learning and deep learning methods. We present a new approach to flare prediction using full-disk compressed magnetogram images with Convolutional Neural Networks. We selected three prediction modes, among which two are binary for predicting the occurrence of ≥M1.0 and ≥C4.0 class flares and one is a multi-class mode for predicting the occurrence of more » « less
5. Identifying Flare-indicative Photospheric Magnetic Field Parameters from Multivariate Time-series Data of Solar Active Regions

   https://doi.org/10.3847/1538-4365/ad21e4  

   Alshammari, Khaznah ; Hamdi, Shah Muhammad ; Filali Boubrahimi, Soukaina ( March 2024 , The Astrophysical Journal Supplement Series)

   Photospheric magnetic field parameters are frequently used to analyze and predict solar events. Observation of these parameters over time, i.e., representing solar events by multivariate time-series (MVTS) data, can determine relationships between magnetic field states in active regions and extreme solar events, e.g., solar flares. We can improve our understanding of these events by selecting the most relevant parameters that give the highest predictive performance. In this study, we propose a two-step incremental feature selection method for MVTS data using a deep-learning model based on long short-term memory (LSTM) networks. First, each MVTS feature (magnetic field parameter) is evaluated individually by a univariate sequence classifier utilizing an LSTM network. Then, the top performing features are combined to produce input for an LSTM-based multivariate sequence classifier. Finally, we tested the discrimination ability of the selected features by training downstream classifiers, e.g., Minimally Random Convolutional Kernel Transform and support vector machine. We performed our experiments using a benchmark data set for flare prediction known as Space Weather Analytics for Solar Flares. We compared our proposed method with three other baseline feature selection methods and demonstrated that our method selects more discriminatory features compared to other methods. Due to the imbalanced nature of the data, primarily caused by the rarity of minority flare classes (e.g., the X and M classes), we used the true skill statistic as the evaluation metric. Finally, we reported the set of photospheric magnetic field parameters that give the highest discrimination performance in predicting flare classes.

    

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