Keywords

Learning the mapping of magnetograms and EUV images is important for understanding the solar eruption mechanism and space weather forecasting. Previous works are mainly based on the pix2pix model for full-disk magnetograms generation and obtain good performance. However, in general, we are more concerned with the magnetic field distribution in the active regions where various solar storms such as the solar flare and coronal mass ejection happen. In this paper, we fuse the self-attention mechanism with the pix2pix model which allows more computation resource and greater weight for strong magnetic regions. In addition, the attention features are concatenated by the Residual Hadamard Production (RHP) with the abstracted features after the encoder. We named our model as RHP-attention pix2pix. From the experiments, we can find that the proposed model can generate magnetograms with finer strong magnetic structures, such as sunspots. In addition, the polarity distribution of generated magnetograms at strong magnetic regions is more consistent with observed ones.

For the public having a better understanding of solar activities, the Educational Adaptive-optics Solar Telescope (EAST) was built in July 2021 and is located at the Shanghai Astronomy Museum. The EAST consists of a 65 cm aperture solar telescope with a 177-element adaptive optics system and two-channel high resolution imaging system at the Hα and TiO bands, in addition to three full disk solar telescopes at Ca K, Hα and TiO bands equipped on the tube of the main telescope. In this paper, the configuration of the EAST is described. Its performance and on-sky observational results are presented. The EAST, to our knowledge, is the most advanced solar telescope for the popularization of science in the world. Due to its excellent performance, the data acquired by the EAST can also be used for research on solar physics and space weather prediction.

One of the greatest challenges in solar physics is understanding the heating of the Sun's corona. Most theories for coronal heating postulate that free energy in the form of magnetic twist/stress is injected by the photosphere into the corona where the free energy is converted into heat either through reconnection or wave dissipation. The magnetic helicity associated with the twist/stress, however, is expected to be conserved and appear in the corona. In previous works, we showed that the helicity associated with the small-scale twists undergoes an inverse cascade via stochastic reconnection in the corona and ends up as the observed large-scale shear of filament channels. Our "helicity condensation" model accounts for both the formation of filament channels and the observed smooth, laminar structure of coronal loops. In this paper, we demonstrate, using helicity- and energy-conserving numerical simulations of a coronal system driven by photospheric motions, that the model also provides a natural mechanism for heating the corona. We show that the heat generated by the reconnection responsible for the helicity condensation process is sufficient to account for the observed coronal heating. We study the role that helicity injection plays in determining coronal heating and find that, crucially, the heating rate is only weakly dependent on the net helicity preference of the photospheric driving. Our calculations demonstrate that motions with 100% helicity preference are least efficient at heating the corona; those with 0% preference are most efficient. We discuss the physical origins of this result and its implications for the observed corona.

Texture is one of the most obvious characteristics in solar images and it is normally described by texture features. Because textures from solar images of the same wavelength are similar, we assume that texture features of solar images are multi-fractals. Based on this assumption, we propose a pure data-based image restoration method: with several high-resolution solar images as references, we use the Cycle-Consistent Adversarial Network to restore blurred images of the same steady physical process, in the same wavelength obtained by the same telescope. We test our method with simulated and real observation data and find that our method can improve the spatial resolution of solar images, without loss of any frames. Because our method does not need a paired training set or additional instruments, it can be used as a post-processing method for solar images obtained by either seeing-limited telescopes or telescopes with ground-layer adaptive optic systems.

We provide a large image parameter data set extracted from the Solar Dynamics Observatory (SDO) mission's Atmospheric Imaging Assembly (AIA) instrument, for the period of 2011 January through the current date, with the cadence of 6 minutes, for nine wavelength channels. The volume of the data set for each year is just short of 1 TiB. Toward achieving better results in the region classification of active regions and coronal holes, we improve on the performance of a set of 10 image parameters, through an in-depth evaluation of various assumptions that are necessary for calculation of these image parameters. Then, where possible, a method for finding an appropriate setting for the parameter calculations was devised, as well as a validation task to show our improved results. In addition, we include comparisons of JP2 and FITS image formats using supervised classification models, by tuning the parameters specific to the format of the images from which they are extracted and specific to each wavelength. The results of these comparisons show that utilizing JP2 images, which are significantly smaller files, is not detrimental to the region classification task that these parameters were originally intended for. Finally, we compute the tuned parameters on the AIA images and provide a public API (see http://dmlab.cs.gsu.edu/dmlabapi/) to access the data set. This data set can be used in a range of studies on AIA images, such as content-based image retrieval or tracking of solar events, where dimensionality reduction on the images is necessary for feasibility of the tasks.

Using observations from the Solar Dynamics Observatory, we studied an interesting example of a sigmoid formation and eruption from small-scale flux-canceling regions of active region (AR) 11942. Through an analysis of Helioseismic and Magnetic Imager and Atmospheric Imaging Assembly observations we infer that initially the AR is compact and bipolar in nature, evolved to a sheared configuration consisting of inverse J-shaped loops hosting a filament channel over a couple of days. By tracking the photospheric magnetic features, shearing and converging motions are observed to play a prime role in the development of S-shaped loops and further flux cancellation leads to tether-cutting reconnection of J loops. This phase is cotemporal with the filament rise motion, followed by sigmoid eruption at 21:32 UT on January 6. The flux rope rises in phases of slow (v_avg = 26 km s⁻¹) and fast (a_avg = 55 m s⁻²) rise motion categorizing the coronal mass ejection (CME) as slow with an associated weak C1.0 class X-ray flare. The flare ribbon separation velocity peaks at around the peak time of the flare at which the maximum reconnection rate (2.14 V cm⁻¹) occurs. Furthermore, the extreme ultraviolet light curves of 131, 171 Å have delayed peaks of 130 minutes compared to 94 Å and are explained by differential emission measure. Our analysis suggests that the energy release is proceeded by a much longer time duration, manifesting the onset of the filament rise and an eventual eruption driven by converging and canceling flux in the photosphere. Unlike strong eruption events, the observed slow CME and weak flare are indications of slow runway tether-cutting reconnection in which most of the sheared arcade is relaxed during the extended phase after the eruption.

During a solar flare, it is believed that reconnection takes place in the corona followed by fast energy transport to the chromosphere. The resulting intense heating strongly disturbs the chromospheric structure and induces complex radiation hydrodynamic effects. Interpreting the physics of the flaring solar atmosphere is one of the most challenging tasks in solar physics. Here we present a novel deep-learning approach, an invertible neural network, to understanding the chromospheric physics of a flaring solar atmosphere via the inversion of observed solar line profiles in Hα and Ca ii λ8542. Our network is trained using flare simulations from the 1D radiation hydrodynamic code RADYN as the expected atmosphere and line profile. This model is then applied to single pixels from an observation of an M1.1 solar flare taken with the Swedish 1 m Solar Telescope/CRisp Imaging SpectroPolarimeter instrument just after the flare onset. The inverted atmospheres obtained from observations provide physical information on the electron number density, temperature, and bulk velocity flow of the plasma throughout the solar atmosphere ranging from 0 to 10 Mm in height. The density and temperature profiles appear consistent with the expected atmospheric response, and the bulk plasma velocity provides the gradients needed to produce the broad spectral lines while also predicting the expected chromospheric evaporation from flare heating. We conclude that we have taught our novel algorithm the physics of a solar flare according to RADYN and that this can be confidently used for the analysis of flare data taken in these two wavelengths. This algorithm can also be adapted for a menagerie of inverse problems providing extremely fast (∼10 μs) inversion samples.

The N–S asymmetry (the north–south hemispheric asymmetry) of sunspot areas for each of the cycles 7–24 have been investigated, and a trend of a long-term characteristic timescale of about eight cycles is inferred, which is confirmed again by studying the fitted lines of the yearly values of the N–S asymmetry of sunspot numbers and sunspot group numbers at solar cycle 24. Then, a periodic behavior of about 12 solar cycles is found in the cumulative counts of yearly sunspot areas for each of the cycles 7–24. Nevertheless, the cumulative counts of sunspot numbers and sunspot group numbers for cycle 24 have different behaviors. Moreover, the dominant hemispheres for cycles 7–23 show a trend of a long-term characteristic timescale of about 12 cycles. However, we cannot determine the dominant hemisphere of cycle 24, as these three parameters give different results for the dominant hemisphere. Cycle 24 is a particular solar activity cycle, as sunspot areas suggest a long characteristic timescale of about 12-cycle length, while sunspot numbers and sunspot group numbers support an eight-cycle period of the N–S asymmetry.

To characterize the particle radiation environment at the Lagrangian point L1 and in the near-Earth space we performed a systematic analysis of the particle flux data recorded by different instruments on board different spacecraft (ACE EPAM/LEMS120, IMP-8 CPME, and Geotail EPIC-ICS). We focused on protons in the poorly explored energy range ∼0.05–5 MeV, including energies of the so-called soft protons, which are critical for the Advanced Telescope for High Energy Astrophysics (ATHENA) mission, as well as the 145–440 MeV one, because high-energy particles affect all interplanetary missions. We estimated the energetic proton environment by computing the cumulative distribution functions for the different energy channels of each instrument and studied its variations with respect to solar activity. We obtained energetic proton spectra at cumulative probabilities (CPs) of 50% and 90% and worst-case scenarios, which can be used by the ATHENA mission for operational purposes and more generally for space weather hazards. We found an increase in the ∼0.05–5 MeV proton spectrum at 90% CP during the maximum phase of solar cycle (SC) No. 23 of about a factor from 3 to 5, depending on the energy, with respect to the overall period (1997–2014). Moreover, the 300–500 keV proton flux at 90% CP is higher during SC No. 21 by about a factor 1.5 and 3 compared to SC No. 22 and SC No. 23, respectively. Finally, variations with solar activity of the 145–440 MeV proton flux are within a factor of 2 at both 90% and 50% CPs, thus representing the low-energy galactic cosmic rays.

The hadronic interaction of cosmic rays with solar atmosphere can produce high energy gamma-rays. The gamma-ray luminosity is correlated both with the flux of primary cosmic rays and the intensity of the solar magnetic field. The gamma-rays below 200 GeV have been observed by Fermi without any evident energy cutoff. The bright gamma-ray flux above 100 GeV has been detected only during solar minimum. The only available data in the TeV range come from the HAWC observations, however, outside the solar minimum. The ARGO-YBJ data set has been used to search for sub-TeV/TeV gamma-rays from the Sun during the solar minimum from 2008 to 2010, the same time period covered by the Fermi data. A suitable model containing the Sun shadow, solar disk emission, and inverse-Compton emission has been developed, and the chi-square minimization method was used to quantitatively estimate the disk gamma-ray signal. The result shows that no significant gamma-ray signal is detected and upper limits to the gamma-ray flux at 0.3–7 TeV are set at the 95% confidence level. In the low energy range these limits are consistent with the extrapolation of the Fermi-LAT measurements taken during solar minimum and are compatible with a softening of the gamma-ray spectrum below 1 TeV. They also provide an experimental upper bound to any solar disk emission at TeV energies. Models of dark matter annihilation via long-lived mediators predicting gamma-ray fluxes >10⁻⁷ GeV cm⁻² s⁻¹ below 1 TeV are ruled out by the ARGO-YBJ limits.