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Abstract The spectra of coronal mass ejections (CMEs) in the low corona play a crucial role in understanding their origins and physical mechanisms and enhancing space weather forecasting. However, capturing these spectra faces significant challenges. This paper introduces a scheme of a multislit spectrometer design with five slits, acquiring the global spectra of the solar corona simultaneously with a focus on the spectra of CMEs in the low corona. The chosen wavelength range of the spectrometer (170–180 Å) includes four extreme ultraviolet emission lines (Fex174.53 Å, Feix171.07 Å, Fex175.26 Å, Fex177.24 Å), which provides information on the plasma velocity, density, and temperature. Utilizing a numerical simulation of the global corona for both the on-disk and the off-limb scenarios, we focus on resolving the ambiguity associated with various Doppler velocity components of CMEs, particularly for a fast CME in the low corona. A new application of our decomposition technique is adopted, enabling the successful identification of multiple discrete CME velocity components. Our findings demonstrate a strong correlation between the synthetic model spectra and the inverted results, indicating the robustness of our decomposition method and its significant potential for global monitoring of the solar corona, including CMEs. 

Abstract Full-disk spectroscopic observations of the solar corona are highly desired to forecast solar eruptions and their impact on planets and to uncover the origin of solar wind. In this paper, we introduce a new multislit design (five slits) to obtain extreme-ultraviolet (EUV) spectra simultaneously. The selected spectrometer wavelength range (184–197 Å) contains several bright EUV lines that can be used for spectral diagnostics. The multislit approach offers an unprecedented way to efficiently obtain the global spectral data but the ambiguity from different slits should be resolved. Using a numerical simulation of the global corona, we primarily concentrate on the optimization of the disambiguation process, with the objective of extracting decomposed spectral information of six primary lines. This subsequently facilitates a comprehensive series of plasma diagnostics, including density (Fexii195.12/186.89 Å), Doppler velocity (Fexii193.51 Å), line width (Fexii193.51 Å), and temperature diagnostics (Feviii185.21 Å, Fex184.54 Å, Fexi188.22 Å, and Fexii193.51 Å). We find a good agreement between the forward modeling parameters and the inverted results at the initial eruption stage of a coronal mass ejection, indicating the robustness of the decomposition method and its immense potential for global monitoring of the solar corona. 

Context. Solar observations of carbon monoxide (CO) indicate the existence of lower-temperature gas in the lower solar chromosphere. We present an observation of pores, and quiet-Sun, and network magnetic field regions with CO 4.66 μm lines by the Cryogenic Infrared Spectrograph (CYRA) at Big Bear Solar Observatory. Aims. We used the strong CO lines at around 4.66 μm to understand the properties of the thermal structures of lower solar atmosphere in different solar features with various magnetic field strengths. Methods. Different observations with different instruments were included: CO 4.66 μm imaging spectroscopy by CYRA, Atmospheric Imaging Assembly (AIA) 1700 Å images, Helioseismic and Magnetic Imager (HMI) continuum images, line-of-sight (LOS) magnetograms, and vector magnetograms. The data from 3D radiation magnetohydrodynamic (MHD) simulation with the Bifrost code are also employed for the first time to be compared with the observation. We used the Rybicki-Hummer (RH) code to synthesize the CO line profiles in the network regions. Results. The CO 3-2 R14 line center intensity changes to be either enhanced or diminished with increasing magnetic field strength, which should be caused by different heating effects in magnetic flux tubes with different sizes. We find several “cold bubbles” in the CO 3-2 R14 line center intensity images, which can be classified into two types. One type is located in the quiet-Sun regions without magnetic fields. The other type, which has rarely been reported in the past, is near or surrounded by magnetic fields. Notably, some are located at the edge of the magnetic network. The two kinds of cold bubbles and the relationship between cold bubble intensities and network magnetic field strength are both reproduced by the 3D MHD simulation with the Bifrost and RH codes. The simulation also shows that there is a cold plasma blob near the network magnetic fields, causing the observed cold bubbles seen in the CO 3-2 R14 line center image. Conclusions. Our observation and simulation illustrate that the magnetic field plays a vital role in the generation of some CO cold bubbles. 

Context. The magnetic field is the underlying cause of solar activities. Spectropolarimetric Stokes inversions have been routinely used to extract the vector magnetic field from observations for about 40 years. In contrast, the photospheric continuum images have an observational history of more than 100 years. Aims. We suggest a new method to quickly estimate the unsigned radial component of the magnetic field, | B r |, and the transverse field, B t , just from photospheric continuum images ( I ) using deep convolutional neural networks (CNN). Methods. Two independent models, that is, I versus | B r | and I versus B t , are trained by the CNN with a residual architecture. A total of 7800 sets of data ( I , B r and B t ) covering 17 active region patches from 2011 to 2015 from the Helioseismic and Magnetic Imager are used to train and validate the models. Results. The CNN models can successfully estimate | B r | as well as B t maps in sunspot umbra, penumbra, pore, and strong network regions based on the evaluation of four active regions (test datasets). From a series of continuum images, we can also detect the emergence of a transverse magnetic field quantitatively with the trained CNN model. The three-day evolution of the averaged value of the estimated | B r | and B t from continuum images follows that from Stokes inversions well. Furthermore, our models can reproduce the nonlinear relationships between I and | B r | as well as B t , explaining why we can estimate these relationships just from continuum images. Conclusions. Our method provides an effective way to quickly estimate | B r | and B t maps from photospheric continuum images. The method can be applied to the reconstruction of the historical magnetic fields and to future observations for providing the quick look data of the magnetic fields. 

Abstract Multiwavelength bright points (BPs) are taken to be cross sections of magnetic flux tubes extending from the surface of the photosphere upward to the higher photosphere. We aim to study the characteristics of isolated multiwavelength BPs using the cotemporal and cospatial TiO band and H α line wings from the Goode Solar Telescope at Big Bear Solar Observatory. A deep-learning method, based on Track Region-based Convolutional Neural Networks, is proposed to detect, segment, and match the BPs across multiple wavelength observations, including the TiO, H α + 1 Å, H α − 1 Å, H α + 0.8 Å, and H α − 0.8 Å line wings. Based on the efficient detection and matching result with a precision of 0.98, 1283 groups of BPs matched in all five wavelengths are selected for statistics analysis. The characteristic values of the BPs observed at the same red and blue line wings are averaged. For the BPs of the TiO, averaged H α ± 1 Å, and averaged H α ± 0.8 Å line wings, the mean equivalent diameters are 162 ± 32, 254 ± 33, and 284 ± 28 km, respectively. The maximum intensity contrasts are 1.11 ± 0.09, 1.05 ± 0.03, and 1.05 ± 0.02 , respectively. The mean eccentricities are 0.65 ± 0.14, 0.63 ± 0.11, and 0.65 ± 0.11, respectively. Moreover, the characteristic ratios of each H α ± 1 Å and H α ± 0.8 Å BP to its corresponding TiO BP are derived. H α ± 1 Å and H α ± 0.8 Å line wings BPs show 60% and 80% increases compared to TiO BPs, respectively. With increasing height, most BPs almost keep their shapes. This work is helpful for modeling the three-dimensional structure of flux tubes.