Global Oscillation Network Group Research Articles

Seismic differences between solar magnetic cycles 23 and 24 for low-degree modes

Solar magnetic activity follows regular cycles of about 11 years with an inversion of polarity in the poles every sim 22 years. This changing surface magnetism impacts the properties of the acoustic modes. The acoustic mode frequency shifts are a good proxy of the magnetic cycle. In this Letter we investigate solar magnetic activity cycles 23 and 24 through the evolution of the frequency shifts of low-degree modes (ell = 0, 1, and 2) in three frequency bands. These bands probe properties between 74 and 1575 km beneath the surface. The analysis was carried out using observations from the space instrument Global Oscillations at Low Frequency and the ground-based Birmingham Solar Oscillations Network and Global Oscillation Network Group. The frequency shifts of radial modes suggest that changes in the magnetic field amplitude and configuration likely occur near the Sun's surface rather than near its core. The maximum shifts of solar cycle 24 occurred earlier at mid and high latitudes (relative to the equator) and about 1550 km beneath the photosphere. At this depth but near the equator, this maximum aligns with the surface activity but has a stronger magnitude. At around 74 km deep, the behaviour near the equator mirrors the behaviour at the surface, while at higher latitudes, it matches the strength of cycle 23.

Sensitivity of Global Oscillation Frequencies to Solar Activity on Short Timescales

Sun’s oscillations frequencies vary with the changing magnetic activity and hold a strong positive correlation. While most of the analysis for global modes have been carried out using time series longer than a solar rotation, only a few studies are available showing their variation on shorter timescales. Using Doppler observations from Global Oscillation Network Group during the period 1995–2024, we analyze oscillation frequencies computed on a timescale of nine days and compare them with those obtained from 108 days. We also study their sensitivity in cycles 23 and 24 to the change in solar activity as represented by the 10.7 cm radio flux.

A dataset of manually annotated filaments from H-alpha observations.

We present the Manually Annotated GONG Filaments in H-alpha Observations (MAGFiLO v1.0) dataset. This dataset contains 10,244 annotated filaments from 1,593 observations captured by the Global Oscillation Network Group (GONG), spanning the years 2011 through 2022. Each annotation details one filament's segmentation, minimum bounding box, spine, and magnetic field chirality. With a total of over one thousand person-hours of annotation, and a double-blind review process, we ensured high-quality ground-truth data. Our inter-annotator agreement reaches a Kappa score of 0.66. We also verified that the hemispheric preference of filaments as annotated in MAGFiLO aligns with the findings from similar datasets of much smaller sample sizes. MAGFiLO is the first dataset of its size, enabling advanced deep learning models to identify filaments and their features with unprecedented precision. It also provides a testbed for solar physicists interested in large-scale analysis of filaments. In this report, we document the details of the annotation and the post-processing phases that were applied.

Helioseismic Properties of Dynamo Waves in the Variation of Solar Differential Rotation

Solar differential rotation exhibits a prominent feature: its cyclic variations over the solar cycle, referred to as zonal flows or torsional oscillations, are observed throughout the convection zone. Given the challenge of measuring magnetic fields in subsurface layers, understanding deep torsional oscillations becomes pivotal in deciphering the underlying solar dynamo mechanism. In this study, we address the critical question of identifying specific signatures within helioseismic frequency-splitting data associated with the torsional oscillations. To achieve this, a comprehensive forward modeling approach is employed to simulate the helioseismic data for a dynamo model that, to some extent, reproduces solar-cycle variations of magnetic fields and flows. We provide a comprehensive derivation of the forward modeling process utilizing generalized spherical harmonics, as it involves intricate algebraic computations. All estimated frequency-splitting coefficients from the model display an 11 yr periodicity. Using the simulated splitting coefficients and realistic noise, we show that it is possible to identify the dynamo wave signal present in the solar zonal flow from the tachocline to the solar surface. By analyzing observed data, we find similar dynamo wave patterns in the observational data from the Michelson Doppler Imager, Helioseismic Magnetic Imager, and Global Oscillation Network Group. This validates the earlier detection of dynamo waves and holds potential implications for the solar dynamo theory models.

Solar Wind With Field Lines and Energetic Particles (SOFIE) Model: Application to Historical Solar Energetic Particle Events

AbstractIn this paper, we demonstrate the applicability of the data‐driven solar energetic particle (SEP) model, SOlar‐wind with FIeld‐lines and Energetic‐particles (SOFIE), to simulate the acceleration and transport processes of SEPs and make forecast of the energetic proton flux at energies ≥10 MeV that will be observed near 1 AU. The SOFIE model is built upon the Space Weather Modeling Framework developed at the University of Michigan. In SOFIE, the background solar wind plasma in the solar corona and interplanetary space is calculated by the Stream‐Aligned Aflvén Wave Solar‐atmosphere Model(‐Realtime) driven by the near‐real‐time hourly updated Global Oscillation Network Group solar magnetograms. In the background solar wind, coronal mass ejections (CMEs) are launched by placing an force‐imbalanced magnetic flux rope on top of the parent active region, using the Eruptive Event Generator using Gibson‐Low model. The acceleration and transport processes are modeled by the Multiple‐Field‐Line Advection Model for Particle Acceleration. In this work, nine SEP events (Solar Heliospheric and INterplanetary Environment challenge/campaign events) are modeled. The three modules in SOFIE are validated and evaluated by comparing with observations, including the steady‐state background solar wind properties, the white‐light image of the CMEs, and the flux of solar energetic protons, at energies of ≥10 MeV.

Refinement of Global Coronal and Interplanetary Magnetic Field Extrapolations Constrained by Remote-sensing and In Situ Observations at the Solar Minimum

Solar magnetic fields are closely related to various physical phenomena on the Sun, which can be extrapolated with different models from photospheric magnetograms. However, the open flux problem (OFP), the underestimation of the magnetic field derived from the extrapolated model, is still unsolved. To minimize the impact of the OFP, we propose three evaluation parameters to quantitatively evaluate magnetic field models and determine the optimal free parameters in the models by constraining the coronal magnetic fields and the interplanetary magnetic fields (IMFs) with real observations. Although the OFP still exists, we find that magnetic field lines traced from the coronal models effectively capture the intricate topological configurations observed in the corona, including streamers and plumes. The OFP is lessened by using the Helioseismic and Magnetic Imager synoptic map instead of the Global Oscillation Network Group daily synoptic maps, and the potential field source surface + potential field current sheet (PFSS+PFCS) model instead of the current sheet source surface (CSSS) model. For Carrington Rotation 2231 at the solar minimum, we suggest that the optimal parameters for the PFSS+PFCS model are R ss = 2.2–2.5 R ☉ and R scs = 10.5–14.0 R ☉, as well as for the CSSS model, are R cs = 2.0–2.4 R ☉, R ss = 11.0–14.7 R ☉, and a = 1.0 R ☉. Despite the IMFs at 1 au being consistent with the measurements by artificially increasing the polar magnetic fields, the IMFs near the Sun are still underestimated. The OFP might be advanced by improving the accuracy of both the weak magnetic fields and polar magnetic fields, especially considering magnetic activities arising from interplanetary physical processes.

Multipoint study of the rapid filament evolution during a confined C2 flare on 28 March 2022, leading to eruption

This study focuses on the rapid evolution of the solar filament in active region 12975 during a confined C2 flare on 28 March 2022, which finally led to an eruptive M4 flare 1.5 h later. The event is characterized by the apparent breakup of the filament, the disappearance of its southern half, and the flow of the remaining filament plasma into a new, longer channel with a topology very similar to an extreme ultraviolet (EUV) hot channel observed during the flare. In addition, we outline the emergence of the original filament from a sheared arcade and discuss possible drivers for its rise and eruption. We took advantage of Solar Orbiter's favorable position, 0.33 AU from the Sun, and $83. 5^ west of the Sun-Earth line, to perform a multi-point study using the Spectrometer Telescope for Imaging X-rays (STIX) and the Extreme Ultraviolet Imager (EUI) in combination with the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO) and Halpha images from the Earth-based Kanzelhöhe Observatory for Solar and Environmental Research (KSO) and the Global Oscillation Network Group (GONG). While STIX and EUI observed the flare and the filament's rise from close up and at the limb, AIA and HMI observations provided highly complementary on-disk observations from which we derived differential emission measure (DEM) maps and nonlinear force-free (NLFF) magnetic field extrapolations. According to our pre-flare NLFF extrapolation, field lines corresponding to both filament channels existed in close proximity before the flare. We propose a loop-loop reconnection scenario based on field structures associated with the AIA 1600 Å flare ribbons and kernels. It involves field lines surrounding and passing beneath the shorter filament channel, and field lines closely following the southern part of the longer channel. Reconnection occurs in an essentially vertical current sheet at a polarity inversion line (PIL) below the breakup region, which enables the formation of the flare loop arcade and EUV hot channel. This scenario is supported by concentrated currents and free magnetic energy built up by antiparallel flows along the PIL before the flare, and by non-thermal X-ray emission observed from the reconnection region. The reconnection probably propagated to involve the original filament itself, leading to its breakup and new geometry. This reconnection geometry also provides a general mechanism for the formation of the long filament channel and realizes the concept of tether cutting. It was probably active throughout the filament's continuous rise phase, which lasted from at least 30 min before the C2 flare until the filament eruption. The C2 flare represents a period of fast reconnection during this otherwise more steady period, during which most of the original filament was reconnected and joined the longer channel. These results demonstrate how rapid changes in solar filament topology can be driven by loop-loop reconnection with nearby field structures, and how this can be part of a long-lasting tether-cutting reconnection process. They also illustrate how a confined precursor flare due to loop-loop reconnection (Type I) can contribute to the evolution towards a full eruption, and that they can produce a flare loop arcade when the contact region between interacting loop systems has a sheet-like geometry similar to a flare current sheet.

Filament eruption by multiple reconnections

Context. Filament eruption is a common phenomenon in solar activity, but the triggering mechanism is not well understood. Aims. We focus our study on a filament eruption located in a complex nest of three active regions close to a coronal hole. Methods. The filament eruption is observed at multiple wavelengths: by the Global Oscillation Network Group (GONG), the Solar Terrestrial Relations Observatory (STEREO), the Solar Upper Transition Region Imager (SUTRI), and the Atmospheric Imaging Assembly (AIA) and Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamic Observatory (SDO). Thanks to high-temporal-resolution observations, we were able to analyze the evolution of the fine structure of the filament in detail. The filament changes direction during the eruption, which is followed by a halo coronal mass ejection detected by the Large Angle Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory (SOHO). A Type III radio burst was also registered at the time of the eruption. To investigate the process of the eruption, we analyzed the magnetic topology of the filament region adopting a nonlinear force-free-field (NLFFF) extrapolation method and the polytropic global magnetohydrodynamic (MHD) modeling. We modeled the filament by embedding a twisted flux rope with the regularized Biot-Savart Laws (RBSL) method in the ambient magnetic field. Results. The extrapolation results show that magnetic reconnection occurs in a fan-spine configuration resulting in a circular flare ribbon. The global modeling of the corona demonstrates that there was an interaction between the filament and open field lines, causing a deflection of the filament in the direction of the observed CME eruption and dimming area. Conclusions. The modeling supports the following scenario: magnetic reconnection not only occurs with the filament itself (the flux rope) but also with the background magnetic field lines and open field lines of the coronal hole located to the east of the flux rope. This multiwavelength analysis indicates that the filament undergoes multiple magnetic reconnections on small and large scales with a drifting of the flux rope.

A universal method for solar filament detection from Hαobservations using semi-supervised deep learning

Filaments are omnipresent features in the solar atmosphere. Their location, properties, and time evolution can provide important information about changes in solar activity and assist in the operational space weather forecast. Therefore, filaments have to be identified in full-disk images and their properties extracted from these images, but manual extraction is tedious and too time-consuming, and extraction with morphological image processing tools produces a large number of false positive detections. Automatic object detection, segmentation, and extraction in a reliable manner would allow for the processing of more data in a shorter time frame. The Chromospheric Telescope (ChroTel; Tenerife, Spain), the Global Oscillation Network Group (GONG), and the Kanzelhöhe Observatory for Solar and Environmental Research (KSO; Austria) provide regular full-disk observations of the Sun in the core of the chromospheric Hαabsorption line. In this paper, we present a deep learning method that provides reliable extractions of solar filaments from Hαfiltergrams. First, we trained the object detection algorithm YOLOv5 with labeled filament data of ChroTel Hαfiltergrams. We used the trained model to obtain bounding boxes from the full GONG archive. In a second step, we applied a semi-supervised training approach where we used the bounding boxes of filaments to train the algorithm on a pixel-wise classification of solar filaments with u-net. We made use of the increased data set size, which avoids overfitting of spurious artifacts from the generated training masks. Filaments were predicted with an accuracy of 92%. With the resulting filament segmentations, physical parameters such as the area or tilt angle could be easily determined and studied. We demonstrated this in an example where we determined the rush-to-the pole for Solar Cycle 24 from the segmented GONG images. In a last step, we applied the filament detection to Hαobservations from KSO and demonstrated the general applicability of our method to Hαfiltergrams.

Prediction of Solar Wind Speed Through Machine Learning From Extrapolated Solar Coronal Magnetic Field

AbstractAn accurate solar wind (SW) speed model is important for space weather predictions, catastrophic event warnings, and other issues concerning SW—magnetosphere interaction. In this work, we construct a model based on convolutional neural network (CNN) and Potential Field Source Surface (PFSS) magnetic field maps, considering a SW source surface of RSS = 2.5R⊙, aiming to predict the SW speed at the Lagrange‐1 (L1) point of the Sun‐Earth system. The input of our model consists of four PFSS magnetic field maps at RSS, which are three, four, five, and six days before the target epoch. Reduced maps are used to promote the model's efficiency. We use the Global Oscillation Network Group (GONG) photospheric magnetograms and the potential field extrapolation model to generate PFSS magnetic field maps at the source surface. The model provides predictions of the quasi‐continuous test data set, which is generated by randomly assigning 120 data segments that are individually continuous in time, with an averaged correlation coefficient (CC) of 0.53 ± 0.07 and a root mean square error (RMSE) of 80.8 ± 4.8 km/s in an eight‐fold validation training scheme with the time resolution of the data as small as one hour. The model also has the potential to forecast high speed streams of the SW, which can be quantified with a general threat score of 0.39.

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Superresolution (SR) aims to increase the resolution of images by recovering detail. Compared to standard interpolation, deep learning-based approaches learn features and their relationships to leverage prior knowledge of what low-resolution patterns look like in higher resolution. Deep neural networks can also perform image cross-calibration by learning the systematic properties of the target images. While SR for natural images aims to create perceptually convincing results, SR of scientific data requires careful quantitative evaluation. In this work, we demonstrate that deep learning can increase the resolution and calibrate solar imagers belonging to different instrumental generations. We convert solar magnetic field images taken by the Michelson Doppler Imager (resolution ∼2″ pixel−1; space based) and the Global Oscillation Network Group (resolution ∼2.″5 pixel−1; ground based) to the characteristics of the Helioseismic and Magnetic Imager (resolution ∼0.″5 pixel−1; space based). We also establish a set of performance measurements to benchmark deep-learning-based SR and calibration for scientific applications.

Connection between Subsurface Layers and Surface Magnetic Activity over Multiple Solar Cycles Using GONG Observations

We investigate the spatiotemporal evolution of high-degree acoustic-mode frequencies of the Sun and surface magnetic activity over the course of multiple solar cycles, to improve our understanding of the connection between the solar interior and atmosphere. We focus on high-degree p-modes due to their ability to characterize conditions in the shear layer just below the solar surface, and analyze 22 yr of oscillation frequencies obtained from the Global Oscillation Network Group. Considering 10.7 cm radio flux measurements, the sunspot number, and the local magnetic activity index as solar-activity proxies, we find strong correlation between the mode frequencies and each activity index. We further investigate the hemispheric asymmetry associated with oscillation frequencies and magnetic activity proxies, and find that both were dominant in the southern hemisphere during the descending phase of cycle 23, while in cycle 24 these quantities fluctuated between northern and southern hemispheres. Analyzing the frequencies at different latitudes with the progression of solar cycles, we observe that the variations at midlatitudes were dominant in the southern hemisphere during the maximum-activity period of cycle 24, but the values overlap as the cycle advances toward the minimum phase. The mode frequencies at the beginning of cycle 25 are found to be dominant in the southern hemisphere following the pattern of magnetic activity. The analysis provides added evidence that the variability in oscillation frequencies is caused by both strong and weak magnetic fields.

A Transfer Learning Method to Generate Synthetic Synoptic Magnetograms

AbstractCurrent magnetohydrodynamics (MHD) models largely rely on synoptic magnetograms, such as the ones produced by the Global Oscillation Network Group (GONG). Magnetograms are currently available mostly from the front side of the Sun, which significantly reduces the accuracy of MHD modeling. Extreme Ultraviolet (EUV) images can instead be obtained from other vantage points. To investigate the potential, we explore the possibility of using EUV information from the Atmospheric Imaging Assembly (AIA) to directly generate the input for the state‐of‐the‐art 3D MHD model European Heliospheric FORecasting Information Asset (EUHFORIA). Toward this goal, we develop a method called Transfer‐Solar‐GAN which combines a conditional generative adversarial network with a transfer learning approach to overcome training data set limitations. The source domain data set is constructed from multiple pairs of the central portion of co‐registered AIA and Helioseismic and Magnetic Imager (HMI) line of sight (LOS) full‐disk images, while the target domain is constructed from pairs of portions of AIA and GONG sine‐latitude synoptic maps that we call segments. We evaluate Transfer‐Solar‐GAN by comparing modeled and measured solar wind velocity and magnetic field density parameters at the L1 Lagrange point and along the Parker Solar Probe (PSP) trajectory which were determined with EUHFORIA using both empirical GONG and artificial‐intelligence (AI)‐synthetic synoptic magnetograms as inputs. Our results demonstrate that the Transfer‐Solar‐GAN model can provide the necessary information to run solar physics models by EUV information. Our proposed model is trained with only 528 paired image segments and enforces a reliable data division strategy.

Global solar photospheric and coronal magnetic field over activity cycles 21–25

The evolution of the global solar magnetic field from the beginning of cycle 21 (mid-1970s) until the currently-ascending cycle 25 is described using photospheric full-disk and synoptic magnetograms from NSO Kitt Peak Vacuum Telescope (KPVT) 512-channel and Spectromagnetograph (SPMG) and the Synoptic Optical Long-term Investigation of the Sun (SOLIS) Vector Spectro-Magnetograph (VSM) and Global Oscillations Network Group (GONG), and Stanford University’s Wilcox Solar Observatory (WSO). The evolving strength and symmetry of the global coronal field are described by potential-field source-surface models decomposed into axisymmetric and non-axisymmetric, and even- and odd-ordered magnetic multipoles. The overall weakness of the global solar magnetic field since cycle 23 splits the 50-year observing window into the stronger, simpler, more hemispherically symmetric cycles 21 and 22 and the weaker, more complex cycles 23 and 24. An anomalously large decrease in the global solar field strength occurred during cycles 23, and an anomalously weak axial/polar field resulted from that cycle, accompanied by an anomalously weak radial interplanetary magnetic field (IMF) during cycle 23 activity minimum and a weakened radial IMF overall since cycle 23. The general long-term decline in solar field strength and the development during cycle 24 of strong swings of hemispheric and polar asymmetry are analyzed in detail, including their transfer through global coronal structural changes to dominate mean in situ interplanetary field measurements for several years. Although more symmetric than cycle 24, the rise phase of cycle 25 began with the southern leading the northern hemisphere, but the north has recovered to lead this cycle’s polar field reversal. The mean polar flux (poleward of ±60°) has reversed at each pole, so far more symmetrically than the cycle 23 and 24 polar reversals.

Helioseismic Investigation of Quasi-biennial Oscillation Source Regions

We studied the temporal evolution of quasi-biennial oscillations (QBOs) using acoustic mode oscillation frequencies from the Global Oscillation Network Group. The data used here span more than 25 yr, covering solar cycles 23 and 24 and the ascending phase of cycle 25. The analysis reveals that QBO-like signals are present in both the cycles, but with different periods. The dominant QBO period in cycle 23 is found to be about 2 yr, while it is about 3 yr in cycle 24. Furthermore, the quasi-biennial oscillatory signals are present only during the ascending and high-activity phases of cycle 23 and quickly weaken around 2005, during the declining phase. In comparison, the QBO signals are present throughout cycle 24, starting from 2009 to 2017. We also explored the depth dependence in QBO signals and obtained a close agreement at all depths, except in the near-surface shear layer. A detailed analysis of the near-surface shear layer suggests that the source region of QBOs is probably within a few thousand kilometers just below the surface.

Near-Sun In Situ and Remote-sensing Observations of a Coronal Mass Ejection and its Effect on the Heliospheric Current Sheet

During the thirteenth encounter of the Parker Solar Probe (PSP) mission, the spacecraft traveled through a topologically complex interplanetary coronal mass ejection (ICME) beginning on 2022 September 5. PSP traversed through the flank and wake of the ICME while observing the event for nearly two days. The Solar Probe ANalyzer and FIELDS instruments collected in situ measurements of the plasma particles and magnetic field at ∼13.3 R S from the Sun. We observe classical ICME signatures, such as a fast-forward shock, bidirectional electrons, low proton temperatures, low plasma β, and high alpha particle to proton number density ratios. In addition, PSP traveled through two magnetic inversion lines, a magnetic reconnection exhaust, and multiple sub-Alfvénic regions. We compare these in situ measurements to remote-sensing observations from the Wide-field Imager for Solar PRobe Plus instrument on board PSP and the Sun Earth Connection Coronal and Heliospheric Investigation on the Solar Terrestrial Relations Observatory. Based on white-light coronagraphs, two CMEs are forward modeled to best fit the extent of the event. Furthermore, Air Force Data Assimilative Flux Transport magnetograms modeled from Global Oscillation Network Group magnetograms and Potential Field Source Surface modeling portray a global reconfiguration of the heliospheric current sheet (HCS) after the CME event, suggesting that these eruptions play a significant role in the evolution of the HCS.

Three-dimensional inversion of corona structure and simulation of solar wind parameters based on the photospheric magnetic field deduced from the Global Oscillation Network Group

In this research, the Potential Field Source Surface–Wang–Sheeley–Arge (PFSS–WSA) solar wind model is used. This model consists of the Potential Field Source Surface (PFSS) coronal magnetic field extrapolation module and the Wang–Sheeley–Arge (WSA) solar wind velocity module. PFSS is implemented by the POT3D package deployed on Tianhe 1A supercomputer system. In order to obtain the three–dimensional (3D) distribution of the coronal magnetic field at different source surface radii (Rss), the model utilizes the Global Oscillation Network Group (GONG) photospheric magnetic field profiles for two Carrington rotations (CRs), CR2069 (in 2008) and CR2217 (in 2019), as the input data, with the source surface at Rss = 2Rs, Rss = 2.5Rs and Rss = 3Rs, respectively. Then the solar wind velocity, the coronal magnetic field expansion factor, and the minimum angular distance of the open magnetic field lines from the coronal hole boundary are estimated within the WSA module. The simulated solar wind speed is compared with the value for the corona extrapolated from the data observed near 1 AU, through the calculations of the mean square error (MSE), root mean square error (RMSE) and correlation coefficient (CC). Here we extrapolate the solar wind velocity at 1 AU back to the source surface via the Parker spiral. By comparing the evaluation metrics of the three source surface heights, we concluded that the solar source surface should be properly decreased with respect to Rss = 2.5Rs during the low solar activity phase of solar cycle 23.

Examining the Source Regions of Solar Energetic Particles Using an AI-generated Synchronic Potential Field Source Surface Model

We study the source regions of six solar energetic particle (SEP) events accelerated near or behind the limbs of the Sun. We use AI-generated farside magnetograms at a near real-time basis developed by Jeong et al. and AIHMI-PFSS extrapolations up to 2.5R ⊙ computed using the input of the synchronic data combining AI-generated farside and HMI magnetograms. By comparing the AIHMI, HMI, Global Oscillations Network Group (GONG) synoptic magnetograms, and Air force Data Assimilative Photospheric flux Transport synchronic magnetograms, as well as the PFSS extrapolations, we find interesting differences between them in view of SEP source regions and magnetic field configurations. First, the structures and sizes of the source active regions (ARs) are changed. The total unsigned magnetic field fluxes of the ARs are mostly stronger in the AIHMI than in the HMI and GONG magnetograms. Second, newly emerging ARs are observed in the SEP source regions in the AIHMI magnetograms for two events. Third, the alterations in the magnetic flux, the emergence, and the dissipation of ARs lead to modifications in the locations of the global polarity inversion lines (PILs). The EUV wave propagation is typically observed to be oriented nearly perpendicular with respect to the local PIL, suggesting that the AIHMI-PFSS extrapolations around the source region are more realistic. This study shows that the continuous farside evolution of AR magnetic fields, which is accomplished by our AI synchronic magnetograms, can lead to an improved understanding of SEP source ARs.

Generating Space-based SDO/HMI-like Solar Magnetograms from Ground-based Hα Images by Deep Learning

Recently, the method of estimating magnetic field through monochromatic images by deep learning has been proposed, demonstrating good morphological similarity but somewhat poor magnetic polarity consistency relative to real observation. In this paper, we propose to estimate magnetic field from Hα images by using a conditional generative adversarial network (cGAN) as the basic framework. The Hα images from the Global Oscillation Network Group are used as the inputs and the line-of-sight magnetograms of the Helioseismic Magnetic Imager (HMI) are used as the targets. First, we train a cGAN model (Model A) with shuffling training data. However, the estimated magnetic polarities are not very consistent with real observations. Second, to improve the accuracy of estimated magnetic polarities, we train a cGAN model (Model B) with the chronological Hα and HMI images, which can implicitly exploit the magnetic polarity constraint of time-series observation to generate more accurate magnetic polarities. We compare the generated magnetograms with the target HMI magnetograms to evaluate the two models. It can be observed that Model B has better magnetic polarity consistency than Model A. To quantitatively measure this consistency, we propose a new metric called pixel-to-pixel polarity accuracy (PPA). With respect to PPA, Model B is superior to Model A. This work gives us an insight that the time-series constraint can be implicitly exploited through organizing training data chronologically, and this conclusion also can be applied to other similar tasks related to time-series data.

Why “Solar Tsunamis” Rarely Leave Their Imprints in the Chromosphere

Solar coronal waves frequently appear as bright disturbances that propagate globally from the eruption center in the solar atmosphere, just like the tsunamis in the ocean on Earth. Theoretically, coronal waves can sweep over the underlying chromosphere and leave an imprint in the form of Moreton wave, due to the enhanced pressure beneath their coronal wave front. Despite the frequent observations of coronal waves, their counterparts in the chromosphere are rarely detected. Why the chromosphere rarely bears the imprints of solar tsunamis remained a mystery since their discovery three decades ago. To resolve this question, all coronal waves and associated Moreton waves in the last decade have been initially surveyed, though the detection of Moreton waves could be hampered by utilizing the low-quality Hα data from the Global Oscillations Network Group. Here, we present eight cases (including five in the Appendix) of the coexistence of coronal and Moreton waves in inclined eruptions where it is argued that the extreme inclination is key to providing an answer to address the question. For all these events, the lowest part of the coronal wave front near the solar surface appears very bright, and the simultaneous disturbances in the solar transition region and the chromosphere predominantly occur beneath the bright segment. Therefore, evidenced by observations, we propose a scenario for the excitation mechanism of the coronal-Moreton waves in highly inclined eruptions, in which the lowest part of a coronal wave can effectively disturb the chromosphere even for a weak (e.g., B-class) solar flare.