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Abstract Between 2017 and 2024, the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory has observed numerous white-light solar flares (WLFs). HMI spectropolarimetric observations of certain WLFs, in particular the X9.3 flare of 2017 September 6, reveal one or more locations within the umbra or along the umbra/penumbra boundary of the flaring active region where the FeI6173 Å line briefly goes into full emission, indicating significant heating of the photosphere and lower chromosphere. For five flares featuring FeI6173 Å line-core emission, we perform spectropolarimetric analysis using HMI 90 s cadence Stokes data. For all investigated flares, line-core emission is observed to last for a single 90 s frame and is either concurrent with or followed by an increase in the line continuum intensity lasting one to two frames (90–180 s). Additionally, permanent changes to the StokesQ,U, and/orVprofiles were observed, indicating long-lasting nontransient changes to the photospheric magnetic field. These emissions coincided with local maxima in hard X-ray emission observed by Konus-Wind, as well as local maxima in the time derivative of soft X-ray emission observed by GOES 16-18. Comparison of the FeI6173 Å line profile synthesis for the ad hoc heating of the initial empirical VAL-S umbra model and quiescent-Sun (VAL-C-like) model indicates that the FeI6173 Å line emission in the white-light flare kernels could be explained by the strong heating of initially cool photospheric regions. 

Abstract Analysis of ion-kinetic instabilities in solar wind plasmas is crucial for understanding energetics and dynamics throughout the heliosphere, as evident from spacecraft observations of complex ion velocity distribution functions (VDFs) and ubiquitous ion-scale kinetic waves. In this work, we explore machine learning (ML) and deep learning (DL) classification models to identify unstable cases of ion VDFs driving kinetic waves. Using 34 hybrid particle-in-cell simulations of kinetic protons andα-particles initialized using plasma parameters derived from solar wind (SW) observations, we prepare a data set of nearly 1600 VDFs representing stable/unstable cases and associated plasma and wave properties. We compare feature-based classifiers applied to VDF moments, such as support vector machine and random forest (RF), with DL convolutional neural networks (CNNs) applied directly to VDFs as images in the gyrotropic velocity plane. The best-performing classifier, RF, has an accuracy of 0.96 ± 0.01, and a true skill score of 0.89 ± 0.03, with the majority of missed predictions made near stability thresholds. We study how the variations of the temporal derivative thresholds of anisotropies and magnetic energies, and sampling strategies for simulation runs, affect classification. CNN-based models have the highest accuracy of 0.88 ± 0.18 among all considered if evaluated on the runs entirely not used during the model training. The addition of theE_⊥power spectrum as an input for the ML models leads to the improvement of instability analysis for some cases. The results demonstrate the potential of ML and DL for the detection of ion-scale kinetic instabilities using spacecraft observations of SW and magnetospheric plasmas. 

Abstract Recent in situ observations from Parker Solar Probe (PSP) near perihelia reveal ion beams, temperature anisotropies, and kinetic wave activity. These features are likely linked to solar wind heating and acceleration. During PSP Encounter 17 (at 11.4R_s) on 2023 September 26, the PSP/FIELDS instrument detected enhanced ion-scale wave activity associated with deviations from local thermodynamic equilibrium in ion velocity distribution functions (VDFs) observed by the PSP/Solar Probe Analyzers-Ion. Dense beams (secondary populations) were present in the proton VDFs during this wave activity. Using bi-Maxwellian fits to the proton VDFs, we found that the density of the proton beam population increased during the wave activity and, unexpectedly, surpassed the core population at certain intervals. Interestingly, the wave power was reduced during the intervals when the beam population density exceeded the core density. The drift velocity of the beams decreases from 0.9 to 0.7 of the Alfvén speed, and the proton core shows a higher temperature anisotropy (T_⊥/T_∥ > 2.5) during these intervals. We conclude that the observations during these intervals are consistent with a reconnection event during a heliospheric current sheet crossing. During this event,α-particle parameters (density, velocity, and temperature anisotropy) remained nearly constant. Using linear analysis, we examined how the proton beam drives instability or wave dissipation. Furthermore, we investigated the nonlinear evolution of ion kinetic instabilities using hybrid kinetic simulations. This study provides direct clues about energy transfer between particles and waves in the young solar wind. 

Abstract This work explores the impacts of magnetogram projection effects on machine-learning-based solar flare forecasting models. Utilizing a methodology proposed by D. A. Falconer et al., we correct for projection effects present in Georgia State University’s Space Weather Analytics for Solar Flares benchmark data set. We then train and test a support vector machine classifier on the corrected and uncorrected data, comparing differences in performance. Additionally, we provide insight into several other methodologies that mitigate projection effects, such as stacking ensemble classifiers and active region location-informed models. Our analysis shows that data corrections slightly increase both the true-positive (correctly predicted flaring samples) and false-positive (nonflaring samples predicted as flaring) prediction rates, averaging a few percent. Similarly, changes in performance metrics are minimal for the stacking ensemble and location-based model. This suggests that a more complicated correction methodology may be needed to see improvements. It may also indicate inherent limitations when using magnetogram data for flare forecasting. 

Abstract Timely and accurate prediction of solar flares is a crucial task due to the danger they pose to human life and infrastructure beyond Earth’s atmosphere. Although various machine learning algorithms have been employed to improve solar flare prediction, there has been limited focus on improving performance using outlier detection. In this study, we propose the use of a tree-based outlier detection algorithm, Isolation Forest (iForest), to identify multivariate time-series instances within the flare-forecasting benchmark data set, Space Weather Analytics for Solar Flares (SWAN-SF). By removing anomalous samples from the nonflaring class (N-class) data, we observe a significant improvement in both the true skill score and the updated Heidke skill score in two separate experiments. We focus on analyzing outliers detected by iForest at a 2.4% contamination rate, considered the most effective overall. Our analysis reveals a co-occurrence between the outliers we discovered and strong flares. Additionally, we investigated the similarity between the outliers and the strong-flare data and quantified it using Kullback–Leibler divergence. This analysis demonstrates a higher similarity between our outliers and strong-flare data when compared to the similarity between the outliers and the rest of the N-class data, supporting our rationale for using outlier detection to enhance SWAN-SF data for flare prediction. Furthermore, we explore a novel approach by treating our outliers as if they belong to flaring-class data in the training phase of our machine learning, resulting in further enhancements to our models’ performance. 

Abstract The prediction of solar energetic particle (SEP) events garners increasing interest as space missions extend beyond Earth’s protective magnetosphere. These events, which are, in most cases, products of magnetic-reconnection-driven processes during solar flares or fast coronal-mass-ejection-driven shock waves, pose significant radiation hazards to aviation, space-based electronics, and particularly space exploration. In this work, we utilize the recently developed data set that combines the Solar Dynamics Observatory/Space-weather Helioseismic and Magnetic Imager Active Region Patches and the Solar and Heliospheric Observatory/Space-weather Michelson Doppler Imager Active Region Patches. We employ a suite of machine learning strategies, including support vector machines (SVMs) and regression models, to evaluate the predictive potential of this new data product for a forecast of post-solar flare SEP events. Our study indicates that despite the augmented volume of data, the prediction accuracy reaches 0.7 ± 0.1 (experimental setting), which aligns with but does not exceed these published benchmarks. A linear SVM model with training and testing configurations that mimic an operational setting (positive–negative imbalance) reveals a slight increase (+0.04 ± 0.05) in the accuracy of a 14 hr SEP forecast compared to previous studies. This outcome emphasizes the imperative for more sophisticated, physics-informed models to better understand the underlying processes leading to SEP events. 

Abstract Solar flare prediction studies have been recently conducted with the use of Space-Weather MDI (Michelson Doppler Imager on board Solar and Heliospheric Observatory) Active Region Patches (SMARPs) and Space-Weather HMI (Helioseismic and Magnetic Imager on board Solar Dynamics Observatory) Active Region Patches (SHARPs), which are two currently available data products containing magnetic field characteristics of solar active regions (ARs). The present work is an effort to combine them into one data product, and perform some initial statistical analyses in order to further expand their application in space-weather forecasting. The combined data are derived by filtering, rescaling, and merging the SMARP and SHARP parameters, which can then be spatially reduced to create uniform multivariate time series. The resulting combined MDI–HMI data set currently spans the period between 1996 April 4 and 2022 December 13, and may be extended to a more recent date. This provides an opportunity to correlate and compare it with other space-weather time series, such as the daily solar flare index or the statistical properties of the soft X-ray flux measured by the Geostationary Operational Environmental Satellites. Time-lagged cross correlation indicates that a relationship may exist, where some magnetic field properties of ARs lead the flare index in time. Applying the rolling-window technique makes it possible to see how this leader–follower dynamic varies with time. Preliminary results indicate that areas of high correlation generally correspond to increased flare activity during the peak solar cycle. 

Abstract 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. 

Abstract The first significant sunquake event of Solar Cycle 25 was observed during the X1.5 flare of 2022 May 10, by the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory. We perform a detailed spectro-polarimetric analysis of the sunquake photospheric sources, using the Stokes profiles of the Fei6173 Å line, reconstructed from the HMI linear and circular polarized filtergrams. The results show fast variations of the continuum emission with rapid growth and slower decay lasting 3–4 minutes, coinciding in time with the hard X-ray impulses observed by the Konus instrument on board the Wind spacecraft. The variations in the line core appeared slightly ahead of the variations in the line wings, showing that the heating started in the higher atmospheric layers and propagated downward. The most significant feature of the line profile variations is the transient emission in the line core in three of the four sources, indicating intense, impulsive heating in the lower chromosphere and photosphere. In addition, the observed variations of the Stokes profiles reflect transient and permanent changes in the magnetic field strength and geometry in the sunquake sources. Comparison with the radiative hydrodynamics models shows that the physical processes in the impulsive flare phase are substantially more complex than those predicted by proton and electron beam flare models currently presented in the literature. 

Abstract Solar energetic particle (SEP) events and their major subclass, solar proton events (SPEs), can have unfavorable consequences on numerous aspects of life and technology, making them one of the most harmful effects of solar activity. Garnering knowledge preceding such events by studying operational data flows is essential for their forecasting. Considering only solar cycle (SC) 24 in our previous study, we found that it may be sufficient to only utilize proton and soft X-ray (SXR) parameters for SPE forecasts. Here, we report a catalog recording ≥10 MeV ≥10 particle flux unit SPEs with their properties, spanning SCs 22–24, using NOAA’s Geostationary Operational Environmental Satellite flux data. We report an additional catalog of daily proton and SXR flux statistics for this period, employing it to test the application of machine learning (ML) on the prediction of SPEs using a support vector machine (SVM) and extreme gradient boosting (XGBoost). We explore the effects of training models with data from oneandtwo SCs, evaluating how transferable a model might be across different time periods. XGBoost proved to be more accurate than SVMs for almost every test considered, while also outperforming operational SWPC NOAA predictions and a persistence forecast. Interestingly, training done with SC 24 produces weaker true skill statistic and Heidke skill scores₂, even when paired with SC 22 or SC 23, indicating transferability issues. This work contributes toward validating forecasts using long-spanning data—an understudied area in SEP research that should be considered to verify the cross cycle robustness of ML-driven forecasts.