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Spicules, the smallest observable jetlike dynamic features ubiquitous in the chromosphere, are supposedly an important potential source for small-scale solar wind transients, with supporting evidence yet needed. We studied the high-resolution Hαimages (0.″10) and magnetograms (0.″29) from the Big Bear Solar Observatory to find that spicules are an ideal candidate for the solar wind magnetic switchbacks detected by the Parker Solar Probe (PSP). It is not that spicules are a miniature of coronal jets, but that they have unique properties not found in other solar candidates in explaining solar origin of switchbacks. (1) The spicules under this study originate from filigrees, all in a single magnetic polarity. Since filigrees are known as footpoints of open fields, the spicule guiding field lines can form a unipolar funnel, which is needed to create an SB patch, a group of field lines that switch from one common base polarity to the other polarity. (2) The spicules come in a cluster lined up along a supergranulation boundary, and the simulated waiting times from their spatial intervals exhibit a number distribution continuously decreasing from a few seconds to ∼30 minutes, similar to that of switchbacks. (3) From a time–distance map for spicules, we estimate their occurrence rate as 0.55 spicules Mm⁻²s⁻¹, which is sufficiently high for detection by PSP. In addition, the dissimilarity of spicules with coronal jets, including the absence of base brightening and low correlation with EUV emission, is briefly discussed.

Small-scale magnetic flux ropes (SMFRs) fill much of the solar wind, but their origin and evolution are debated. We apply our recently developed, improved Grad–Shafranov algorithm for the detection and reconstruction of SMFRs to data from Parker Solar Probe, Solar Orbiter, Wind, and Voyager 1 and 2 to detect events from 0.06 to 10 au. We observe that the axial flux density is the same for SMFRs of all sizes at a fixed heliocentric distance but decreases with distance owing to solar wind expansion. Additionally, using the difference in speed between SMFRs, we find that the vast majority of SMFRs will make contact with others at least once during the 100 hr transit to 1 au. Such contact would allow SMFRs to undergo magnetic reconnection, allowing for processes such as merging via the coalescence instability. Furthermore, we observe that the number of SMFRs with higher axial flux increases significantly with distance from the Sun. Axial flux is conserved under solar wind expansion, but the observation can be explained by a model in which SMFRs undergo turbulent evolution by stochastically merging to produce larger SMFRs. This is supported by the observed log-normal axial flux distribution. Lastly, we derive the global number of SMFRs above 10¹⁵Mx near the Sun to investigate whether SMFRs begin their journey as small-scale solar ejections or are continuously generated within the outer corona and solar wind.

When and where the magnetic field energy is released and converted in eruptive solar flares remains an outstanding topic in solar physics. To shed light on this question, here we report multiwavelength observations of a C9.4-class eruptive limb flare that occurred on 2017 August 20. The flare, accompanied by a magnetic flux rope eruption and a white light coronal mass ejection, features three post-impulsive X-ray and microwave bursts immediately following its main impulsive phase. For each burst, both microwave and X-ray imaging suggest that the nonthermal electrons are located in the above-the-loop-top region. Interestingly, contrary to many other flares, the peak flux of the three post-impulsive microwave and X-ray bursts shows an increase for later bursts. Spectral analysis reveals that the sources have a hardening spectral index, suggesting a more efficient electron acceleration into the later post-impulsive bursts. We observe a positive correlation between the acceleration of the magnetic flux rope and the nonthermal energy release during the post-impulsive bursts in the same event. Intriguingly, different from some other eruptive events, this correlation does not hold for the main impulse phase of this event, which we interpret as energy release due to the tether-cutting reconnection before the primary flux rope acceleration occurs. In addition, using footpoint brightenings at conjugate flare ribbons, a weakening reconnection guide field is inferred, which may also contribute to the hardening of the nonthermal electrons during the post-impulsive phase.

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short‐term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short‐term forecasts of the SYM‐H index based on 1‐ and 5‐min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM‐H index value at time pointt + whours for a given time pointtwherewis 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1‐ and 5‐min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. When predicting the SYM‐H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot.

Small-scale interplanetary magnetic flux ropes (SMFRs) are similar to ICMEs in magnetic structure, but are smaller and do not exhibit coronal mass ejection plasma signatures. We present a computationally efficient and GPU-powered version of the single-spacecraft automated SMFR detection algorithm based on the Grad–Shafranov (GS) technique. Our algorithm can process higher resolution data, eliminates selection bias caused by a fixed 〈B〉 threshold, has improved detection criteria demonstrated to have better results on an MHD simulation, and recovers full 2.5D cross sections using GS reconstruction. We used it to detect 512,152 SMFRs from 27 yr (1996–2022) of 3 s cadence Wind measurements. Our novel findings are the following: (1) the SMFR filling factor (∼ 35%) is independent of solar activity, distance to the heliospheric current sheet, and solar wind plasma type, although the minority of SMFRs with diameters greater than ∼0.01 au have a strong solar activity dependence; (2) SMFR diameters follow a log-normal distribution that peaks below the resolved range (≳10⁴km), although the filling factor is dominated by SMFRs between 10⁵and 10⁶km; (3) most SMFRs at 1 au have strong field-aligned flows like those from Parker Solar Probe measurements; (4) the radial density (generally ∼1 detected per 10⁶km) and axial magnetic flux density of SMFRs are higher in faster solar wind types, suggesting that they are more compressed. Implications for the origin of SMFRs and switchbacks are briefly discussed. The new algorithm and SMFR dataset are made freely available.

Interplanetary magnetic flux ropes (MFRs) are commonly observed structures in the solar wind, categorized as magnetic clouds (MCs) and small-scale MFRs (SMFRs) depending on whether they are associated with coronal mass ejections. We apply machine learning to systematically compare SMFRs, MCs, and ambient solar wind plasma properties. We construct a data set of 3-minute averaged sequential data points of the solar wind’s instantaneous bulk fluid plasma properties using about 20 years of measurements from Wind. We label samples by the presence and type of MFRs containing them using a catalog based on Grad–Shafranov (GS) automated detection for SMFRs and NASA's catalog for MCs (with samples in neither labeled non-MFRs). We apply the random forest machine learning algorithm to find which categories can be more easily distinguished and by what features. MCs were distinguished from non-MFRs with an area under the receiver-operator curve (AUC) of 94% and SMFRs with an AUC of 89%, and had distinctive plasma properties. In contrast, while SMFRs were distinguished from non-MFRs with an AUC of 86%, this appears to rely solely on the 〈B〉 > 5 nT threshold applied by the GS catalog. The results indicate that SMFRs have virtually the same plasma properties as the ambient solar wind, unlike the distinct plasma regimes of MCs. We interpret our findings as additional evidence that most SMFRs at 1 au are generated within the solar wind. We also suggest that they should be considered a salient feature of the solar wind’s magnetic structure rather than transient events.

Magnetic field plays an important role in various solar eruption phenomena. The formation and evolution of the characteristic magnetic field topology in solar eruptions are critical problems that will ultimately help us understand the origin of these eruptions in the solar source regions. With the development of advanced techniques and instruments, observations with higher resolutions in different wavelengths and fields of view have provided more quantitative information for finer structures. It is therefore essential to improve the method with which we study the magnetic field topology in the solar source regions by taking advantage of high-resolution observations. In this study, we employ a nonlinear force-free field extrapolation method based on a nonuniform grid setting for an M-class flare eruption event (SOL2015-06-22T17:39) with embedded vector magnetograms from the Solar Dynamics Observatory (SDO) and the Goode Solar Telescope (GST). The extrapolation results for which the nonuniform embedded magnetogram for the bottom boundary was employed are obtained by maintaining the native resolutions of the corresponding GST and SDO magnetograms. We compare the field line connectivity with the simultaneous GST/Hαand SDO/Atmospheric Imaging Assembly observations for these fine-scale structures, which are associated with precursor brightenings. Then we perform a topological analysis of the field line connectivity corresponding to fine-scale magnetic field structures based on the extrapolation results. The analysis results indicate that when we combine the high-resolution GST magnetogram with a larger magnetogram from the SDO, the derived magnetic field topology is consistent with a scenario of magnetic reconnection among sheared field lines across the main polarity inversion line during solar flare precursors.

Here, we present the study of a compact emission source during an X1.3 flare on 2022 March 30. Within a ∼41 s period (17:34:48 UT to 17:35:29 UT), Interface Region Imaging Spectrograph observations show spectral lines of Mgii, Cii, and Siivwith extremely broadened, asymmetric red wings. This source of interest (SOI) is compact, ∼1.″6, and is located in the wake of a passing ribbon. Two methods were applied to measure the Doppler velocities associated with these red wings: spectral moments and multi-Gaussian fits. The spectral-moments method considers the averaged shift of the lines, which are 85, 125, and 115 km s⁻¹for the Mgii, Cii, and Siivlines respectively. The red-most Gaussian fit suggests a Doppler velocity up to ∼160 km s⁻¹in all of the three lines. Downward mass motions with such high speeds are very atypical, with most chromospheric downflows in flares on the order 10–100 km s⁻¹. Furthermore, extreme-UV (EUV) emission is strong within flaring loops connecting two flare ribbons located mainly to the east of the central flare region. The EUV loops that connect the SOI and its counterpart source in the opposite field are much less brightened, indicating that the density and/or temperature is comparatively low. These observations suggest a very fast downflowing plasma in the transition region and upper chromosphere, which decelerates rapidly since there is no equivalently strong shift of the O I chromospheric lines. This unusual observation presents a challenge that models of the solar atmosphere’s response to flares must be able to explain.

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.

Coronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy. Being able to estimate these properties helps better understand CME dynamics. Our study is based on the CME catalog maintained at the Coordinated Data Analysis Workshops Data Center, which contains all CMEs manually identified since 1996 using the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory. We use LASCO C2 data in the period between 1996 January and 2020 December to train, validate, and test DeepCME through 10-fold cross validation. The DeepCME method is a fusion of three deep-learning models, namely ResNet, InceptionNet, and InceptionResNet. Our fusion model extracts features from LASCO C2 images, effectively combining the learning capabilities of the three component models to jointly estimate the mass and kinetic energy of CMEs. Experimental results show that the fusion model yields a mean relative error (MRE) of 0.013 (0.009, respectively) compared to the MRE of 0.019 (0.017, respectively) of the best component model InceptionResNet (InceptionNet, respectively) in estimating the CME mass (kinetic energy, respectively). To our knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations.