Solar Physics最新文献

We carried out tomographic reconstructions of the three-dimensional distribution of the electron density of the solar corona based on white light polarized brightness (pB) images taken by the Metis coronagraph on board the Solar Orbiter (SolO) mission. We selected three different time intervals during 2022, and further implemented independent synchronous reconstructions based on LASCO-C2 pB images for comparison purposes. The range of elongations covered by the field-of-view (FoV) of Metis considerably varies as SolO describes its highly eccentric orbit, whereas that of LASCO-C2 remains almost constant. During the selected time intervals, their FoVs partially overlap, allowing a comparison of the reconstructions within the regions in common. The shape and size of the reconstructed coronal structures, streamers and coronal holes, are consistent, demonstrating the suitability of the images of the synoptic program of Metis for tomographic reconstruction of the coronal electron density over its varying FoV. A comparison between the two tomographic reconstructions for each analyzed time interval, shows that the Metis-to-C2 ratio of reconstructed electron density has a median value of (approx 1.7). This is consistent with the observed ratio of the pB measurements of the two instruments. Our analysis thus also illustrates the value of tomography as a tool for intercalibrating solar coronagraphs irrespective of their spatial location, as long as their FoV partially overlap.

Analysis of more than 300 M-class solar flares observed with the Atmospheric Imaging Assembly onboard the Solar Dynamics Observatory in the 131 Å channel, revealed 16 events of sloshing oscillations in hot solar coronal loops. Time–distance maps made along the loops demonstrated EUV emission intensity blobs bouncing between the footpoints, i.e., showing characteristic zigzagging patterns, of the size shorter than 25% of the loop length. The oscillation periods are found to range from about 150 s to 1325 s. The average phase speed, estimated as the ratio of the oscillation period and the loop length, is about 500 km s⁻¹. Parameters of the oscillations are consistent with the interpretation in terms of multi-harmonic slow magnetoacoustic oscillations.

The article focuses on identifying and studying several large-scale solar-wind disturbances and associated Forbush effects in the first months of 2023. Variations of the cosmic-ray flux (with 10 GV rigidity) are obtained using the Global Survey Method with data from the global network of neutron monitors. The beginning of 2023 is characterized by a relatively large number of Forbush effects; the largest ones were recorded on 26 – 28 February, 15 – 16 March, 23 – 25 March, and 23 – 24 April. These events and their relationship with solar-wind parameters, geomagnetic activity, and associated solar sources are discussed in detail. In terms of the number and magnitude of interplanetary disturbances and corresponding cosmic-ray variations, February–April 2023 proves to be the first active period since the beginning of Solar Cycle 25.

Maps of the magnetic field at the Sun’s surface are commonly used as boundary conditions in space-weather modeling. However, continuous observations are only available from the Earth-facing part of the Sun’s surface. One commonly used approach to mitigate the lack of far-side information is to apply a surface flux transport (SFT) model to model the evolution of the magnetic field as the Sun rotates. Helioseismology can image active regions on the far side using acoustic oscillations and hence has the potential to improve the modeled surface magnetic field. In this study, we propose a novel approach for estimating magnetic fields of active regions on the Sun’s far side based on seismic measurements and then include them into an SFT model. To calibrate the conversion from helioseismic signal to magnetic field, we apply our SFT model to line-of-sight magnetograms from Helioseismic and Magnetic Imager (HMI) on board the Solar Dynamics Observatory (SDO) to obtain reference maps of global magnetic fields (including the far side). The resulting magnetic maps are compared with helioseismic phase maps on the Sun’s far side computed using helioseismic holography. The spatial structure of the magnetic field within an active region is reflected in the spatial structure of the helioseismic phase shifts. We assign polarities to the unipolar magnetic-field concentrations based upon Hale’s law and require approximate flux balance between the two polarities. From 2010 to 2024, we modeled 859 active regions, with an average total unsigned flux of (7.84 cdot 10^{21}) Mx and an average area of (4.48 cdot 10^{10}) km². Approximately (4.2%) of the active regions were found to have an anti-Hale configuration, which we manually corrected. Including these far-side active regions resulted in an average increase of (1.2%) (up to (25.3%)) in the total unsigned magnetogram flux. Comparisons between modeled open-field areas and EUV observations reveal a substantial improvement in agreement when far-side active regions are included. This proof of concept study demonstrates the potential of the “combined surface flux transport and helioseismic Far-side Active Region Model” (FARM) to improve space-weather modeling.

The global distribution of the solar wind speed (V) is closely related to the configuration of the coronal magnetic-field, and the expansion factor (f) of the flux tube is known as a parameter for determining (V). However, the inverse relation between (f) and (V) does not hold for pseudostreamers, which separate open-field regions with the same polarity. In the present study, we examined the magnetic-field configuration of pseudostreamers using the potential field (PF) model analysis of magnetograph observations for six Carrington rotations (CRs) in Cycle 23 and compared it with (V) data derived from interplanetary scintillation observations. We calculated the parameter (S), which represents the relative angular distance of foot points on the photosphere magnetically connected to adjacent pixels on the source surface and (f) from PF model analysis and discriminated areas of helmet and pseudostreamers on the source surface by selecting large values of (S). Although the overall correlation between (S) and (V) was very poor, helmet and pseudostreamers with large (S) values were exclusively associated with slow (V). Furthermore, helmet and pseudostreamers were associated with large and small values of (f), respectively. This suggests that (S) enables a better discrimination of slow-wind sources associated with pseudostreamers than (f). We calculated the distance from the streamer boundary (DSTB) on the source surface using data of helmet and pseudostreamers to compare with (V) data. Calculated DSTB data exhibited significant correlations with (V) data except for the solar maximum period. The average of correlation coefficients between DSTB and (V) over five CRs excluding one at the solar maximum were 0.69, higher than that between the distance from the coronal hole boundary (DCHB) and (V). This suggests that DSTB acts as a better parameter for determining (V) than DCHB. We demonstrated that (f) for pseudostreamers tended to reach a maximum at a height lower than the source surface (2.5 (R_{odot })). This provides important insight into the formation process of the slow solar wind in pseudostreamers.

Despite the energetic significance of Lyman-alpha (Ly(alpha ); 1216 Å) emission from solar flares, regular observations of flare related Ly(alpha ) have been relatively scarce until recently. Advances in instrumental capabilities and a shift in focus over previous solar cycles mean it is now routinely possible to take regular co-observations of Ly(alpha ) emission in solar flares. Thus, it is valuable to examine how the instruments selected for flare observations may influence the conclusions drawn from the analysis of their unique measurements. Here, we examine three M-class flares each observed in Ly(alpha ) by GOES-14/EUVS-E, GOES-15/EUVS-E, or GOES-16/EXIS-EUVS-B, and at least one other instrument from PROBA2/LYRA, MAVEN/EUVM, ASO-S/LST-SDI, and SDO/EVE-MEGS-P. For each flare, the relative and excess flux, contrast, total energy, and timings of the Ly(alpha ) emission were compared between instruments. It was found that while the discrepancies in measurements of the relative flux between instruments may be considered minimal, the calculated contrasts, excess fluxes, and energetics may differ significantly – in some cases up to a factor of five. This may have a notable impact on multi-instrument investigations of the variable Ly(alpha ) emission in solar flares and estimates of the contribution of Ly(alpha ) to the radiated energy budget of the chromosphere. The findings presented in this study will act as a guide for the interpretation of observations of flare-related Ly(alpha ) from upcoming instruments during future solar cycles and inform conclusions drawn from multi-instrument studies.

The application of machine learning to the study of coronal mass ejections (CMEs) and their impacts on Earth has seen significant growth recently. Understanding and forecasting CME geoeffectiveness are crucial for protecting infrastructure in space and ensuring the resilience of technological systems on Earth. Here we present GeoCME, a deep-learning framework designed to predict, deterministically or probabilistically, whether a CME event that arrives at Earth will cause a geomagnetic storm. A geomagnetic storm is defined as a disturbance of the Earth’s magnetosphere during which the minimum Dst index value is less than −50 nT. GeoCME is trained on observations from the instruments including LASCO C2, EIT, and MDI on board the Solar and Heliospheric Observatory (SOHO), focusing on a dataset that includes 136 halo/partial halo CMEs in Solar Cycle 23. Using ensemble and transfer learning techniques, GeoCME is capable of extracting features hidden in the SOHO observations and making predictions based on the learned features. Our experimental results demonstrate the good performance of GeoCME, achieving a Matthew’s correlation coefficient of 0.807 and a true skill statistics score of 0.714 when the tool is used as a deterministic prediction model. When the tool is used as a probabilistic forecasting model, it achieves a Brier score of 0.094 and a Brier skill score of 0.493. These results are promising, showing that the proposed GeoCME can help enhance our understanding of CME-triggered solar-terrestrial interactions.

Most solar hard X-ray (HXR) imagers in the past and current solar missions obtain X-ray images via Fourier transform imaging technology, which requires proper imaging algorithms to reconstruct images from spatially-modulated or temporally-modulated signals. A variety of algorithms have been developed during the last 50 years for the characteristics of respective instruments. In this work, we present a new imaging algorithm developed based on deep learning for the Hard X-ray Imager (HXI) onboard the Advanced Space-based Solar Observatory (ASO-S) and the preliminary test results of the algorithm with both simulated data and observations. We first created a training dataset by obtaining modulation data from simulated HXR images of single, double and loop-shaped sources, respectively, and the patterns of HXI sub-collimators. Then, we introduced machine-learning algorithm to develop a pattern-based deep learning network model: HXI_DLA, which can directly produce an image from modulation counts. After training the model with simple sources, we tested DLA for simple sources, extended sources, and double sources for imaging dynamic range. Finally, we compared CLEAN and DLA images reconstructed from HXI observations of three flares. Overall, these imaging tests revealed that the current HXI_DLA method produces comparable image result to those from the widely used imaging method CLEAN. In some cases, DLA images are even slightly better. Besides, HXI_DLA is super fast for imaging and parameter-free. Although this is only the first step towards a fully developed and practical DLA method, the tests have shown the potential of deep learning in the field of solar hard X-ray imaging.

Sunspot-area measurements using digital images captured by two telescopes at the Mitaka campus of the National Astronomical Observatory of Japan are conducted using automated sunspot detection. A comparison between sunspot areas derived from Mitaka and those from the reference data by Mandal et al. (Astron. Astrophys. 640, A78, 2020), who compiled a crosscalibrated daily sunspot-area catalog, revealed that the correlation coefficients between them are high (0.96 – 0.97), whereas the areas in the Mitaka data are 70 – 83% of those of Mandal et al. The correlation is limited by the differences in observation times and detection capabilities of spots near the limb, with discrepancies in areas arising from different definitions of spot outlines. Given the high correlation and the ease of calibrating area discrepancies with a correction factor, automated sunspot detection appears promising for future sunspot-area measurements. Furthermore, we addressed the measurements of brightness deficit caused by sunspots.

We present the ground calibration and on-orbit performance of the Full-disk vector MagnetoGraph (FMG) payload on board the Advanced Space-Based Solar Observatory (ASO-S), which is China’s first spaceborne magnetograph. FMG has the ability to acquire the full-disk Stokes I, Q/I, U/I, and V/I maps with a spatial resolution of about 1.5 arcsec. The Lyot filter for the flight model has a full width at half maximum of 0.01 nm. Using two calibration lenses, we measure the non-uniform wavelength drift across the entire field of view, with a maximum value of 0.003 nm. The on-orbit polarization sensitivity is approximately 0.00039 and 0.0009 for the deep integration and routine modes, corresponding to a cadence of 18 and 2 minutes, respectively. The corresponding sensitivity of the longitudinal magnetic field is 8.5 G and 20 G with the current linear calibration coefficient of 21,913. Since 1 April 2023, FMG has released Level 2 filtergram and longitudinal magnetic field data products for active regions. Furthermore, line-of-sight Carrington synoptic magnetograms spanning a 27-day solar rotation can be generated, which have been released to the public since February 2024. The longitudinal magnetic field obtained by FMG is consistent with that of the Helioseismic and Magnetic Imager on board the Solar Dynamic Observatory and the Solar Magnetism and Activity Telescope at Huairou Solar Observing Station for the regions without magnetic saturation.