Search for: All records

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 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 Recent observations of the solar wind ions by the SPAN-I instruments on board the Parker Solar Probe (PSP) spacecraft at solar perihelia (Encounters) 4 and closer find ample evidence of complex anisotropic non-Maxwellian velocity distributions that consist of core, beam, and “hammerhead” (i.e., anisotropic beam) populations. The proton core populations are anisotropic, withT_⊥/T_∥ > 1, and the beams have super-Alfvénic speed relative to the core (we provide an example from Encounter 17). Theα-particle population shows similar features to the protons. These unstable velocity distribution functions (VDFs) are associated with enhanced, right-hand (RH) and left-hand (LH) polarized ion-scale kinetic wave activity, detected by the FIELDS instrument. Motivated by PSP observations, we employ nonlinear hybrid models to investigate the evolution of the anisotropic hot-beam VDFs and model the growth and the nonlinear stage of ion kinetic instabilities in several linearly unstable cases. The models are initialized with ion VDFs motivated by the observational parameters. We find rapidly growing (in terms of proton gyroperiods) combined ion-cyclotron and magnetosonic instabilities, which produce LH and RH ion-scale wave spectra, respectively. The modeled ion VDFs in the nonlinear stage of the evolution are qualitatively in agreement with PSP observations of the anisotropic core and “hammerhead” velocity distributions, quantifying the effect of the ion kinetic instabilities on wind plasma heating close to the Sun. We conclude that the wave–particle interactions play an important role in the energy transfer between the magnetic energy (waves) and random particle motion, leading to anisotropic solar wind plasma heating. 

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 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 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 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 Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI) observations reveal a class of solar flares with substantial energy and momentum impacts in the photosphere, concurrent with white-light emission and helioseismic responses, known as sunquakes. Previous radiative hydrodynamic modeling has demonstrated the challenges of explaining sunquakes in the framework of the standard flare model of “electron beam” heating. One of the possibilities to explain the sunquakes and other signatures of the photospheric impact is to consider additional heating mechanisms involved in solar flares, for example via flare-accelerated protons. In this work, we analyze a set of single-loop Fokker–Planck and radiative hydrodynamics RADYN+FP simulations where the atmosphere is heated by nonthermal power-law-distributed proton beams which can penetrate deeper than the electron beams into the low atmospheric layers. Using the output of the RADYN models, we calculate synthetic Fei6173 Å line Stokes profiles and from those the line-of-sight observables of the SDO/HMI instrument, as well as the 3D helioseismic response, and compare them with the corresponding observational characteristics. These initial results show that the models with proton beam heating can produce the enhancement of the HMI continuum observable and explain qualitatively the generation of sunquakes. The continuum observable enhancement is evident in all models but is more prominent in ones withE_c≥ 500 keV. In contrast, the models withE_c≤ 100 keV provide a stronger sunquake-like helioseismic impact according to the 3D acoustic modeling, suggesting that low-energy (deka- and hecto-keV) protons have an important role in the generation of sunquakes.