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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-minute and 5-minute 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 point t + w hours for a given time point t where w is 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-minute and 5-minute 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 hour in advance) in a large storm (SYM-H = -393 nT) using 5-minute resolution data. When predicting the SYM-H indices (2 hours 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., Comment: 28 pages, 8 figures

Here we present the study of a compact emission source during an X1.3 flare on 2022-March-30. Within a $\sim41$~s period (17:34:48 UT to 17:35:29 UT), IRIS observations show spectral lines of Mg II, C II and Si IV with extremely broadened, asymmetric red-wings. This source of interest (SOI) is compact, $\sim$ 1\arcsec.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 km s$^{-1}$, 125 km s$^{-1}$ and 115 km s$^{-1}$ for the Mg II, C II and Si IV lines respectively. The red-most Gaussian fit suggests a Doppler velocity up to $\sim$160 km s$^{-1}$ in 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, 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 transition region and upper chromosphere, that 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., Comment: accepted, ApJ

Magnetic field plays an important role in various solar eruptions like flares, coronal mass ejections, etc. The formation and evolution of characteristic magnetic field topology in solar eruptions are critical problems that will ultimately help us understand the origination 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. So it is essential to improve our method to 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 (NLFFF) extrapolation method based on a nonuniform grid setting for an M-class flare eruption event (SOL2015-06-22T17:39) with embedded magnetograms from the Solar Dynamics Observatory (SDO) and the Goode Solar Telescope (GST). The extrapolation results employing the embedded magnetogram for the bottom boundary are obtained by maintaining the native resolutions of the corresponding GST and SDO magnetograms. We compare the field line connectivity with the simultaneous GST/H$\alpha$ and SDO/AIA observations for fine-scale structures 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 results indicate that by combining the high-resolution GST magnetogram with a larger HMI magnetogram, 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., Comment: 17 pages, 9 figures, 2 tables, submitted to ApJ

We present a multi-instrument study of the two precursor brightenings prior to the M6.5 flare (SOL2015-06-22T18:23) in the NOAA Active Region 12371, with a focus on the temperature (T), electron number density (n), and emission measure (EM). The data used in this study were obtained from four instruments with a variety of wavelengths, i.e., the Solar Dynamics Observatory's Atmospheric Imaging Assembly (AIA), in six extreme ultraviolet (EUV) passbands; the Expanded Owens Valley Solar Array (EOVSA) in microwave (MW); the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) in hard X-rays (HXR); and the Geostationary Operational Environmental Satellite (GOES) in soft X-rays (SXR). We compare the temporal variations of T, n, and EM derived from the different data sets. Here are the key results. (1) GOES SXR and AIA EUV have almost identical EM variations (1.5-3x10^48 per cm^3) and very similar T variations, from 8 to 15 million Kelvin (MK). (2) Listed from highest to lowest, EOVSA MW provides the highest temperature variations (15-60 MK), followed by RHESSI HXR (10-24 MK), then GOES SXR and AIA EUV (8-15 MK). (3) The EM variation from the RHESSI HXR measurements is always less than the values from AIA EUV and GOES SXR by at most 20 times. The number density variation from EOVSA MW is greater than the value from AIA EUV by at most 100 times. The results quantitatively describe the differences in the thermal parameters at the precursor phase, as measured by different instruments operating at different wavelength regimes and for different emission mechanisms., Comment: 10 pages, 7 figures

We performed two data-based magnetohydrodynamic (MHD) simulations for solar active region 12371 which produced an M6.5 flare. The first simulation is a full data-driven simulation where the initial condition is given by a nonlinear force-free field (NLFFF). This NLFFF was extrapolated from photospheric magnetograms approximately 1 hour prior to the flare, and then a time-varying photospheric magnetic field is imposed at the bottom surface. The second simulation is also a data-driven simulation, but it stops driving at the bottom before the time of flare onset and then switches to the data-constrained simulation, where the horizontal component of the magnetic field varies according to an induction equation while the normal component is fixed with time. Both simulations lead to an eruption, with both simulations producing highly twisted field lines before the eruption which were not found in the NLFFF alone. After the eruption, the first simulation based on the time-varying photospheric magneitic field, continues to produce sheared field lines after the flare without reproducing phenomena such as post-flare loops. The second simulation reproduces the phenomena associated with flares well. However in this case the evolution of the bottom magnetic field is inconsistent with the evolution of the observed magnetic field. In this letter, we report potential advantages and disadvantages in data-constrained and data-driven MHD simulations that need to be taken into consideration by future studies., Comment: Accepted for Publication in The Astrophysical Journal Letters

Solar activity is usually caused by the evolution of solar magnetic fields. Magnetic field parameters derived from photospheric vector magnetograms of solar active regions have been used to analyze and forecast eruptive events such as solar flares and coronal mass ejections. Unfortunately, the most recent solar cycle 24 was relatively weak with few large flares, though it is the only solar cycle in which consistent time-sequence vector magnetograms have been available through the Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) since its launch in 2010. In this paper, we look into another major instrument, namely the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO) from 1996 to 2010. The data archive of SOHO/MDI covers more active solar cycle 23 with many large flares. However, SOHO/MDI data only has line-of-sight (LOS) magnetograms. We propose a new deep learning method, named MagNet, to learn from combined LOS magnetograms, Bx and By taken by SDO/HMI along with H-alpha observations collected by the Big Bear Solar Observatory (BBSO), and to generate vector components Bx' and By', which would form vector magnetograms with observed LOS data. In this way, we can expand the availability of vector magnetograms to the period from 1996 to present. Experimental results demonstrate the good performance of the proposed method. To our knowledge, this is the first time that deep learning has been used to generate photospheric vector magnetograms of solar active regions for SOHO/MDI using SDO/HMI and H-alpha data., Comment: 15 pages, 6 figures

Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections (CMEs). They are the most important sources of space weather effects, that 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 the AR's Flare Index (FI) that quantifies the flare productivity of an AR by taking into account the numbers of different class flares within a certain time interval. Specifically, our sample includes 563 ARs appeared on solar disk from May 2010 to Dec 2017. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO), 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 Over-Sampling Technique for Regression with Gaussian Noise, short by SMOGN). 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 4 popular machine learning algorithms, our methods improve the accuracy of FI prediction, especially for large FI. In addition, we sort the importance of SHARP parameters by Borda Count method calculated from the ranks that are rendered by 9 different machine learning methods.

This study presents a C3.0 flare observed by the BBSO/GST and IRIS, on 2018-May-28 around 17:10 UT. The Near Infrared Imaging Spectropolarimeter (NIRIS) of GST was set to spectral imaging mode to scan five spectral positions at $\pm$ 0.8 \AA, $\pm$ 0.4 \AA and line center of He I 10830. At the flare ribbon's leading edge the line is observed to undergo enhanced absorption, while the rest of the ribbon is observed to be in emission. When in emission, the contrast compared to the pre-flare ranges from about $30~\%$ to nearly $100~\%$ at different spectral positions. Two types of spectra, "convex" shape with higher intensity at line core and "concave" shape with higher emission in the line wings, are found at the trailing and peak flaring areas, respectively. On the ribbon front, negative contrasts, or enhanced absorption, of about $\sim 10\% - 20\%$ appear in all five wavelengths. This observation strongly suggests that the negative flares observed in He I 10830 with mono-filtergram previously were not caused by pure Doppler shifts of this spectral line. Instead, the enhanced absorption appears to be a consequence of flare energy injection, namely non-thermal collisional ionization of helium caused by the precipitation of high energy electrons, as found in our recent numerical modeling results. In addition, though not strictly simultaneous, observations of Mg II from the IRIS spacecraft, show an obvious central reversal pattern at the locations where enhanced absorption of He I 10830 is seen, which is in consistent with previous observations., Comment: 10 pages, 4 figures Accepted by ApJ Letters

Magnetic flux ropes are the centerpiece of solar eruptions. Direct measurements for the magnetic field of flux ropes are crucial for understanding the triggering and energy release processes, yet they remain heretofore elusive. Here we report microwave imaging spectroscopy observations of an M1.4-class solar flare that occurred on 2017 September 6, using data obtained by the Expanded Owens Valley Solar Array. This flare event is associated with a partial eruption of a twisted filament observed in H{\alpha} by the Goode Solar Telescope at the Big Bear Solar Observatory. The extreme ultraviolet (EUV) and X-ray signatures of the event are generally consistent with the standard scenario of eruptive flares, with the presence of double flare ribbons connected by a bright flare arcade. Intriguingly, this partial eruption event features a microwave counterpart, whose spatial and temporal evolution closely follow the filament seen in H{\alpha} and EUV. The spectral properties of the microwave source are consistent with nonthermal gyrosynchrotron radiation. Using spatially resolved microwave spectral analysis, we derive the magnetic field strength along the filament spine, which ranges from 600-1400 Gauss from its apex to the legs. The results agree well with the non-linear force-free magnetic model extrapolated from the pre-flare photospheric magnetogram. We conclude that the microwave counterpart of the erupting filament is likely due to flare-accelerated electrons injected into the filament-hosting magnetic flux rope cavity following the newly reconnected magnetic field lines., Comment: 15 pages, 11 figures

We present a new deep learning method, dubbed FibrilNet, for tracing chromospheric fibrils in Halpha images of solar observations. Our method consists of a data pre-processing component that prepares training data from a threshold-based tool, a deep learning model implemented as a Bayesian convolutional neural network for probabilistic image segmentation with uncertainty quantification to predict fibrils, and a post-processing component containing a fibril-fitting algorithm to determine fibril orientations. The FibrilNet tool is applied to high-resolution Halpha images from an active region (AR 12665) collected by the 1.6 m Goode Solar Telescope (GST) equipped with high-order adaptive optics at the Big Bear Solar Observatory (BBSO). We quantitatively assess the FibrilNet tool, comparing its image segmentation algorithm and fibril-fitting algorithm with those employed by the threshold-based tool. Our experimental results and major findings are summarized as follows. First, the image segmentation results (i.e., detected fibrils) of the two tools are quite similar, demonstrating the good learning capability of FibrilNet. Second, FibrilNet finds more accurate and smoother fibril orientation angles than the threshold-based tool. Third, FibrilNet is faster than the threshold-based tool and the uncertainty maps produced by FibrilNet not only provide a quantitative way to measure the confidence on each detected fibril, but also help identify fibril structures that are not detected by the threshold-based tool but are inferred through machine learning. Finally, we apply FibrilNet to full-disk Halpha images from other solar observatories and additional high-resolution Halpha images collected by BBSO/GST, demonstrating the tool's usability in diverse datasets., Comment: 20 pages, 12 figures

Deep learning has drawn a lot of interest in recent years due to its effectiveness in processing big and complex observational data gathered from diverse instruments. Here we propose a new deep learning method, called SolarUnet, to identify and track solar magnetic flux elements or features in observed vector magnetograms based on the Southwest Automatic Magnetic Identification Suite (SWAMIS). Our method consists of a data pre-processing component that prepares training data from the SWAMIS tool, a deep learning model implemented as a U-shaped convolutional neural network for fast and accurate image segmentation, and a post-processing component that prepares tracking results. SolarUnet is applied to data from the 1.6 meter Goode Solar Telescope at the Big Bear Solar Observatory. When compared to the widely used SWAMIS tool, SolarUnet is faster while agreeing mostly with SWAMIS on feature size and flux distributions, and complementing SWAMIS in tracking long-lifetime features. Thus, the proposed physics-guided deep learning-based tool can be considered as an alternative method for solar magnetic tracking., Comment: 17 pages, 12 figures

The He I 10830 \AA\ triplet is a very informative indicator of chromospheric activities as the helium is the second most abundant element in the solar atmosphere. Taking advantage of the high resolution of the 1.6 m Goode Solar Telescope (GST) at Big Bear Solar Observatory (BBSO), previous observations have shown clear evidence of the enhanced absorption, instead of typically-observed emission, for two M-class flares. In this study, we analyze the evolution of the He I 10830 10830 \AA\ emission in numerical models and compare it with observations. The models represent the RADYN simulation results obtained from the F-CHROMA database. We consider the models with the injected electron spectra parameters close to observational estimates for the 2013-August-17 flare event ($\delta=8$, $E_c = \{15,20\}$ keV, $F=\{1\times 10^{11}, 3\times{}10^{11}\}$ erg cm$^{-2}$) in detail, as well as other available models. The modeling results agree well with observations, in the sense of both the maximum intensity decrease (-17.1%, compared to the observed value of -13.7%) and the trend of temporal variation (initial absorption phase followed by the emission). All models demonstrate the increased number densities and decreased ratio of the upper and lower level populations of He I 10830 10830 \AA\ transition in the initial phase, which enhances the opacity and forms an absorption feature. Models suggest that the temperatures and free electron densities at heights of 1.3-1.5 Mm should be larger than $\sim 10^4$ K and $6\times 10^{11}$ cm$^{-3}$ thresholds for the line to start being in emission.

We propose a new machine learning approach to Stokes inversion based on a convolutional neural network (CNN) and the Milne-Eddington (ME) method. The Stokes measurements used in this study were taken by the Near InfraRed Imaging Spectropolarimeter (NIRIS) on the 1.6 m Goode Solar Telescope (GST) at the Big Bear Solar Observatory. By learning the latent patterns in the training data prepared by the physics-based ME tool, the proposed CNN method is able to infer vector magnetic fields from the Stokes profiles of GST/NIRIS. Experimental results show that our CNN method produces smoother and cleaner magnetic maps than the widely used ME method. Furthermore, the CNN method is 4~6 times faster than the ME method, and is able to produce vector magnetic fields in near real-time, which is essential to space weather forecasting. Specifically, it takes ~50 seconds for the CNN method to process an image of 720 x 720 pixels comprising Stokes profiles of GST/NIRIS. Finally, the CNN-inferred results are highly correlated to the ME-calculated results and are closer to the ME's results with the Pearson product-moment correlation coefficient (PPMCC) being closer to 1 on average than those from other machine learning algorithms such as multiple support vector regression and multilayer perceptrons (MLP). In particular, the CNN method outperforms the current best machine learning method (MLP) by 2.6% on average in PPMCC according to our experimental study. Thus, the proposed physics-assisted deep learning-based CNN tool can be considered as an alternative, efficient method for Stokes inversion for high resolution polarimetric observations obtained by GST/NIRIS., Comment: 24 pages, 9 figures

A recent laboratory experiment of ideal magnetohydrodynamic (MHD) instabilities reveals four distinct eruption regimes readily distinguished by the torus instability (TI) and helical kink instability (KI) parameters \citep{Myers2015}. To establish its observational counterpart, we collect 38 solar flares (stronger than GOES class M5 in general) that took place within 45$^{\circ}$ of disk center during 2011$-$2017, 26 of which are associated with a halo or partial halo coronal mass ejection (CME) (i.e., ejective events), while the others are CMEless (i.e., confined events). This is a complete sample of solar events satisfying our selection criteria detailed in the paper. For each event, we calculate decay index $n$ of the potential strapping field above the magnetic flux rope (MFR) in and around the flaring magnetic polarity inversion line (a TI parameter), and the unsigned twist number $T_w$ of the non-linear force-free (NLFF) field lines forming the same MFR (a KI parameter). We then construct a $n-T_w$ diagram to investigate how the eruptiveness depends on these parameters. We find: (1) $T_w$ appears to play little role in discriminating between confined and ejective events; (2) the events with $n\gtrsim0.8$ are all ejective and all confined events have $n\lesssim0.8$. However, $n\gtrsim0.8$ is not a necessary condition for eruption, because some events with $n\lesssim0.8$ also erupted. In addition, we investigate the MFR's geometrical parameters, apex height and distance between footpoints, as a possible factor for the eruptiveness. We briefly discuss the difference of the present result for solar eruptions with that of the laboratory result in terms of the role played by magnetic reconnection., Comment: 20 pages, 5 figures

Polar Crown Filaments (PCFs) form above the magnetic polarity inversion line, which separates the unipolar polar fields and the nearest dispersed fields from trailing part of active regions with opposite polarity. The statistical properties of PCFs are correlated with the solar cycle. Therefore, study of PCFs plays an important role in understanding the variation of solar cycle, especially the prolonged cycle 23 and the current "abnormal" solar cycle 24. In this study, we investigate PCFs using full disk H{\alpha} data from 1973 to early 2018, recorded by Kanzelhohe Solar Observatory (KSO) and Big Bear Solar Observatory (BBSO), in digital form from 1997 to 2018 and in 35 mm film (digitized) from 1973 to 1996. PCFs are identified manually because their segmented shape and close-to-limb location were not handled well by automatical detections in several previous studies. Our results show that the PCFs start to move poleward at the beginning of each solar cycle. When the PCFs approach to the maximum latitude, the polar field strength reduces to zero followed by a reversal. The migration rates are about 0.4 to 0.7 degree per Carrington rotation, with clear N-S asymmetric pattern. In cycles 21 and 23, the PCFs in the northern hemisphere migrate faster than those in the southern hemisphere. However, in the "abnormal" cycle 24, the southern PCFs migrate faster, which is consistent with other observations of magnetic fields and radio emission. In addition, there are more days in cycle 23 and 24 without PCFs than in the previous cycles.

As one of the most violent eruptions on the Sun, flares are believed to be powered by magnetic reconnection. The fundamental physics involving the release, transfer and deposition of energy have been studied extensively. Taking advantage of the unprecedented resolution provided by the 1.6-m Goode Solar Telescope, here we show a sudden rotation of vector magnetic fields, about 12$^{\circ}$-20$^{\circ}$ counterclockwise, associated with a flare. Unlike the permanent changes reported previously, the azimuth-angle change is transient and co-spatial/temporal with H$\alpha$ emission. The measured azimuth angle becomes closer to that in potential fields suggesting untwist of flare loops. The magnetograms were obtained in the near infrared at 1.56~$\mu$m, which is minimally affected by flare emission and no intensity profile change was detected. We believe that these transient changes are real and discuss the possible explanations in which the high energy electron beams or $Alfv\acute{e}n$ waves play a crucial role., Comment: http://rdcu.be/Epb8

The well-established notion of a "common population" of the accelerated electrons simultaneously producing the hard X-ray (HXR) and the microwave (MW) emission during the flare impulsive phase has been challenged by some studies reporting the discrepancies between the HXR-inferred and the MW-inferred electron energy spectra. The traditional methods of their spectral inversion have some problems that can be mainly attributed to the unrealistic and the oversimplified treatment of the flare emission. To properly address this problem, we use a Non-linear Force Free Field (NLFFF) model extrapolated from an observed photospheric magnetogram as input to the three-dimensional, multi-wavelength modeling platform GX Simulator, and create a unified electron population model that can simultaneously reproduce the observed HXR and MW observations. We model the end of the impulsive phase of the 2015-06-22 M6.5 flare, and constrain the modeled electron spatial and energy parameters using observations made by the highest-resolving instruments currently available in two wavelengths, the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI) for HXR and the Expanded Owens Valley Solar Array (EOVSA) for MW. Our results suggest that the HXR-emitting electron population model fits the standard flare model with a broken power-law spectrum (E_break ~ 200 keV) that simultaneously produces the HXR footpoint emission and the MW high frequency emission. The model also includes an "HXR invisible" population of nonthermal electrons that are trapped in a large volume of magnetic field above the HXR-emitting loops, which is observable by its gyrosynchrotron (GS) radiation emitting mainly in MW low frequency range.

We analyze the high-resolution observations from the 1.6m telescope at Big Bear Solar Observatory that cover an active region filament. Counter-streaming motions are clearly observed in the filament. The northern end of the counter-streaming motions extends to a light bridge, forming a spectacular circulation pattern around a sunspot, with clockwise motion in the blue wing and counterclockwise motion in the red wing as observed in Halpha off-bands. The apparent speed of the flow is around 10 to 60 km/s in the filament, decreasing to 5 to 20 km/s in the light bridge. The most intriguing results are the magnetic structure and the counter-streaming motions in the light bridge. Similar to those in the filament, magnetic fields show a dominant transverse component in the light bridge. However, the filament is located between opposite magnetic polarities, while the light bridge is between strong fields of the same polarity. We analyze the power of oscillations with the image sequences of constructed Dopplergrams, and find that the filament's counter-streaming motion is due to physical mass motion along fibrils, while the light bridge's counter-streaming motion is due to oscillation in the direction along the line-of-sight. The oscillation power peaks around 4 minutes. However, the section of the light bridge next to the filament also contains a component of the extension of the filament in combination with the oscillation, indicating that some strands of the filament are extended to and rooted in that part of the light bridge., Comment: 5 Figures, ApJ Letter, Accepted

How eruption can recur from a confined magnetic structure is discussed based on the {\it Solar Dynamics Observatory} (SDO) observations of the NOAA active region 11444, which produced three eruptions within 1.5 hours on March 27, 2012. The active region had the positive polarity magnetic fields in the center surrounded by the negative polarity fields around. Since such a distribution of magnetic polarity tends to form a dome-like magnetic fan structure confined over the active region, the multiple eruptions was puzzling. Our investigation reveals that this event exhibits several properties distinct from other eruptions associated with magnetic fan structures: (i) a long filament encircling the active region was present before the eruptions; (ii) expansion of the open-closed boundary of the field lines after each eruption suggestive of the growing fan-dome structure, and (iii) the ribbons inside the closed magnetic polarity inversion line evolving in response to the expanding open-closed boundary. It thus appears that in spite of multiple eruptions the fan-dome structure remained undamaged, and the closing back field lines after each eruption rather reinforced the fan-dome structure. We argue that the multiple eruptions could occur in this active region in spite of its confined magnetic structure because the filament encircling the active region was adequate for slipping through the magnetic separatrix to minimize the damage to its overlying fan-dome structure. The result of this study provides a new insight into the productivity of eruptions from a confined magnetic structure., Comment: 12 pages, 4 figures, published in ApJL

It is now a common practice to use local magnetic parameters such as magnetic decay index for explaining solar eruptions from active regions, but there can be an alternative view that the global properties of the source region should be counted as a more important factor. We discuss this issue based on {\it Solar Dynamics Observatory} (SDO) observations of the three successive eruptions within 1.5 hours from the NOAA active region 11444 and the magnetic parameters calculated using the nonlinear force-free field (NLFFF) model. Two violent eruptions occurred in the regions with relatively high magnetic twist number (0.5--1.5) and high decay index (0.9--1.1) at the nominal height of the filament (12$''$) and otherwise a mild eruption occurred, which supports the local parameter paradigm. Our main point is that the time sequence of the eruptions did not go with these parameters. It is argued that an additional factor, in the form of stabilizing force, should operate to determine the onset of the first eruption and temporal behaviors of subsequent eruptions. As supporting evidence, we report that the heating and fast plasma flow continuing for a timescale of an hour was the direct cause for the first eruption, and that the unidirectional propagation of the disturbance determined the timing of subsequent eruptions. Both of these factors are associated with the overall magnetic structure rather than local magnetic properties of the active region., Comment: 13 pages, 5 figures, published in ApJL

We report the intriguing large-scale dynamic phenomena associated with the M6.5 flare~(SOL2015-06-22T18:23) in NOAA active region 12371, observed by RHESSI, Fermi, and the Atmospheric Image Assembly (AIA) and Magnetic Imager (HMI) on the Solar Dynamic Observatory (SDO). The most interesting feature of this event is a third ribbon (R3) arising in the decay phase, propagating along a dimming channel (seen in EUV passbands) towards a neighboring sunspot. The propagation of R3 occurs in the presence of hard X-ray footpoint emission, and is broadly visible at temperatures from 0.6~MK to over 10~MK through the Differential Emission Measure (DEM) analysis. The coronal loops then undergo an apparent slipping motion following the same path of R3, after a $\sim$ 80~min delay. To understand the underlying physics, we investigate the magnetic configuration and the thermal structure of the flaring region. Our results are in favor of a slipping-type reconnection followed by the thermodynamic evolution of coronal loops. In comparison with those previously reported slipping reconnection events, this one proceeds across a particularly long distance ($\sim$60 Mm) over a long period of time ($\sim$50 min), and shows two clearly distinguished phases: the propagation of the footpoint brightening driven by nonthermal particle injection and the apparent slippage of loops governed by plasma heating and subsequent cooling.

Studying the dynamics of filaments at pre-eruption phase can shed light on the precursor of eruptive events. Such studies in high-resolution (in the order of 0.1") are highly desirable yet very rare so far. In this work, we present a detailed observation of a pre-eruption evolution of a filament obtained by the 1.6 m New Solar Telescope (NST) at Big Bear Solar Observatory (BBSO). One end of the filament is anchored at the sunspot in NOAA active region (AR) 11515, which is well observed by NST H$\alpha$ off-bands four hours before till one hour after the filament eruption. A M1.6 flare is associated with the eruption. We observed persistent downflowing materials along the H$\alpha$ multi-threaded component of the loop towards the AR end during the pre-eruption phase. We traced the trajectories of plasma blobs along the H$\alpha$ threads and obtained the plane-of-sky velocity of 45 km s$^{-1}$ on average. We further estimated the real velocities of the downflows and the altitude of the filament by matching the observed H$\alpha$ threads with magnetic field lines extrapolated from a nonlinear force-free field (NLFFF) model. Observation of chromospheric brightenings (BZs) at the footpoints of the falling plasma blobs is also presented in the paper. The lower limit of the kinetic energy per second of the downflows through the BZs is found to be $\sim$ 10$^{21}$ erg. Larger FOV observations from BBSO full disk H$\alpha$ images show that the AR end of the filament started ascending four hours before the flare. We attribute the observed downflows at the AR end of the filament to the draining effect of the filament rising prior to its eruption. During the slow-rise phase, the downflows continuously drained away $\sim$ 10$^{15}$g mass from the filament over a few hours, which is believed to be essential for the instability at last, and could be an important precursor of eruptive events.

Solar flares are generally believed to be powered by free magnetic energy stored in the corona, but the build up of coronal energy alone may be insufficient for the imminent flare occurrence. The flare onset mechanism is a critical but less understood problem, insights into which could be gained from small-scale energy releases known as precursors, which are observed as small pre-flare brightenings in various wavelengths, and also from certain small-scale magnetic configurations such as the opposite polarity fluxes, where magnetic orientation of small bipoles is opposite to that of the ambient main polarities. However, high-resolution observations of flare precursors together with the associated photospheric magnetic field dynamics are lacking. Here we study precursors of a flare using unprecedented spatiotemporal resolution of the 1.6 m New Solar Telescope, complemented by novel microwave data. Two episodes of precursor brightenings are initiated at a small-scale magnetic channel (a form of opposite polarity fluxes) with multiple polarity inversions and enhanced magnetic fluxes and currents, lying near the footpoints of sheared magnetic loops. The low-atmospheric origin of these precursor emissions is corroborated by microwave spectra. We propose that the emerging magnetic channel field interacts with the sheared arcades to cause precursor brightenings at the main flare core region. These high-resolution results provide evidence of low-atmospheric small-scale energy release and possible relationship to the onset of the main flare., Comment: 6 pages, 4 figures, plus 4 supplementary figures and 2 supplementary videos, published in Nature Astronomy

In this study we synthesize the results of four previous studies on the global energetics of solar flares and associated coronal mass ejections (CMEs), which include magnetic, thermal, nonthermal, and CME energies in 399 solar M and X-class flare events observed during the first 3.5 years of the Solar Dynamics Observatory (SDO) mission. Our findings are: (1) The sum of the mean nonthermal energy of flare-accelerated particles ($E_{\mathrm{nt}}$), the energy of direct heating ($E_{\mathrm{dir}}$), and the energy in coronal mass ejections ($E_{\mathrm{CME}}$), which are the primary energy dissipation processes in a flare, is found to have a ratio of $(E_{\mathrm{nt}}+E_{\mathrm{dir}}+ E_{\mathrm{CME}})/E_{\mathrm{mag}} = 0.87 \pm 0.18$, compared with the dissipated magnetic free energy $E_{\mathrm{mag}}$, which confirms energy closure within the measurement uncertainties and corroborates the magnetic origin of flares and CMEs; (2) The energy partition of the dissipated magnetic free energy is: $0.51\pm0.17$ in nonthermal energy of $\ge 6$ keV electrons, $0.17\pm0.17$ in nonthermal $\ge 1$ MeV ions, $0.07\pm0.14$ in CMEs, and $0.07\pm0.17$ in direct heating; (3) The thermal energy is almost always less than the nonthermal energy, which is consistent with the thick-target model; (4) The bolometric luminosity in white-light flares is comparable with the thermal energy in soft X-rays (SXR); (5) Solar Energetic Particle (SEP) events carry a fraction $\approx 0.03$ of the CME energy, which is consistent with CME-driven shock acceleration; and (6) The warm-target model predicts a lower limit of the low-energy cutoff at $e_c \approx 6$ keV, based on the mean differential emission measure (DEM) peak temperature of $T_e=8.6$ MK during flares. This work represents the first statistical study that establishes energy closure in solar flare/CME events., Comment: 35 pages, 10 Figures, accepted for publication in The Astrophysical Journal