Solar Physics最新文献

The ratio of radially to tangentially polarized Thomson-scattered white light provides a powerful tool for locating the 3D position of compact structures in the solar corona and inner heliosphere, and the Polarimeter to Unify the Corona and Heliosphere (PUNCH) has been designed to take full advantage of this diagnostic capability. Interestingly, this same observable that establishes the position of transient blob-like structures becomes a local measure of the slope of the global falloff of density in the background solar wind. It is thus important to characterize the extent along the line of sight of structures being studied, in order to determine whether they are sufficiently compact for 3D positioning. In this paper, we build from analyses of individual lines of sight to three-dimensional models of coronal mass ejections (CMEs), allowing us to consider how accurately polarization properties of the transient and quiescent solar wind are diagnosed. In this way, we demonstrate the challenges and opportunities presented by PUNCH polarization data for various quantitative diagnostics.

The slopes of the linear relations between sunspot and white light (WL) facular areas at the onset of sunspot Cycles 12 – 21 correlate well with the amplitudes of those cycles between 1878 – 1980 (Brown and Evans in Sol. Phys. 66:233, 1980). We use continuum images from the Michelson Doppler Imager on board the Solar and Heliospheric Observatory and the Heliospheric Magnetic Imager on board the Solar Dynamics Observatory to show that the relation holds also for Cycles 24 and 25. The amplitudes of Cycles 12 – 21 and 24 calculated using this relation agree with the observed amplitudes to within ± 4% rms. It also enables us in 2022 to correctly predict a larger Cycle 25 than estimated by the International Prediction Panel, 3 years before maximum. The technique offers an objective, physically based predictor of cycle amplitudes 3 – 4 years ahead of their maxima, given a stable source of continuum full disk photospheric images.

The quasi-periodic density structures (PDSs) are quasiperiodic variations of solar wind density ranging from a few minutes to a few hours. They are trains of advected density structures with radial length scales (L_{R}approx )100 – 10,000 Mm, thus belonging to the class of solar wind “mesoscale structures”. Even though PDS at L1 have been extensively studied both through statistical and event analysis, their investigation at distances closer to the Sun is limited. This study performs a statistical investigation of PDS at various distances from the Sun between 0.3 and 1 AU by exploiting Solar Orbiter data. We compiled and made publicly available an extensive list of PDSs following a well-established methodology that combines the Multitaper method as well as wavelet analysis to reveal the distribution of PDS radial length scales and how they vary with respect to the radial distance. Our results indicate that PDS advected with the ambient slow solar wind are expanded, while PDS detected during fast solar wind segments show compression indicative of their interaction with stream interaction regions.

The solar X-EUV irradiance is a dominant energy source inputting to the Earth’s upper atmosphere from the outer space. Variability of the solar X-EUV irradiance drives disturbance of the ionosphere, thermosphere and density of the upper atmosphere. Accurate measurement of the solar X-EUV spectra is essential to learn how the solar activities change the Earth’s upper atmosphere vertically and globally, and further impacts to the global space weather or ever the global weather changes. Since the solar X-EUV spectra coming from the super-hot coronal plasma is composited with rich emission lines and continuum, higher-order diffractions introduced by a traditional grating make spectral data severely contaminated in the X-EUV region. It greatly challenges precise measurement of the solar X-EUV irradiance. In this paper we propose an innovative 2-dimension grating designed in a pattern of zigzag or photon sieve, to be used for future solar X-EUV spectrometers that could have capability to deeply suppress higher-order diffractions, whose magnitude is deeply suppressed to be four orders lower comparing to that measured by a traditional instrument based on black-white grating.

The continuous flux of charged particles from the Sun, i.e., the solar wind, influences both planetary and circumplanetary environments. Although the precise origin of each type is still debated, the solar wind originates primarily from the expansion of the solar corona and is driven by the solar magnetic field. The cyclic 11-year variations observable in several solar activity proxies can also be traced in the average properties of the solar wind, though the relationship in terms of amplitude and phase synchronization with solar activity is not uniform. Focusing on the period 1965 – 2024, we investigate how the relationship between a chromospheric proxy, the Ca ii K index, and 1AU solar wind properties, such as speed, temperature, and dynamic pressure, has evolved over the last five solar cycles. On the one hand, variations in their relationship are found in terms of time lag, correlation coefficient, and amplitude (i.e., fit slope) in a cycle-based analysis. In particular, we find evidence consistent with a linear relationship between the time lag (in years) and the slope of the fit characterizing the dependence of solar wind properties on the intensity of the solar magnetic cycle. We also examine these variations in light of the contribution of the different solar wind flow types along individual solar cycles. On the other hand, continuous cross-correlation reveals distinct dynamical regimes in solar wind–Ca ii K lag, with stable behavior at 2 – 4 years and instability emerging at both shorter and longer lag intervals, suggesting a nonlinear bifurcation mechanism. Finally, the cycle-to-cycle variations reported can help in understanding the space climate connection between solar activity and near-Earth solar wind properties, additionally providing insight into the contribution of each solar wind flow type.

Forecasting the Interplanetary Magnetic Field (IMF) (B_{z}) component is a critical and persistent challenge in space weather, often termed the “(B_{z}) problem”. As Coronal Mass Ejections (CMEs) are the primary drivers of strong and sustained southward (B_{z}) events, this study investigates a multi-modal deep-learning framework for CME event-driven IMF (B_{z}) forecasts at 12-hour intervals up to a 96-hour lead time. We propose a novel attention-based architecture, (B_{z})4SWx, to fuse CME kinematic parameters with their associated solar magnetograms. This model employs a dual-branch network enhanced by a Convolutional Block Attention Module (CBAM). To evaluate its effectiveness, we compared its performance against several baseline models, including a uni-modal MLP (numerics), a uni-modal CNN (images), and a naive concatenation-based fusion model. The (B_{z})4SWx model achieved the best overall performance, yielding an MAE of 3.270 nT, an RMSE of 4.124 nT, and a bias of −2.61 nT, with timing precision competitive with other multi-modal approaches. Interpretability analysis confirmed that while magnetograms provide the dominant predictive signal, CBAM was critical for dynamically focusing the model on relevant solar active regions. We conclude that attention mechanisms provide a powerful and interpretable framework for CME event-based IMF (B_{z}) forecasting, representing a significant step toward resolving the persistent (B_{z}) problem.

The soft X-ray (SXR) measurements made by NOAA’s GOES weather satellites are an important resource for solar physics and space weather. In particular, they are extensively used to study the energetics of solar flares via temperatures and emission measures derived from the SXR data. However, the SXR instruments measure just two channels, 0.5 – 4 Å and 1 – 8 Å: with just two data points, it is only possible to represent the flare plasma with a single temperature component, whereas it is well known that a flare can display a wide range of temperatures at any given time. In order to assess how representative the GOES SXR temperatures and emission measures are, we compare GOES measurements with EUV data for six spectral lines of Fe that cover the typical temperature range of flares, 10 – 20 MK. From a sample of 23 large flares, we find that the GOES temperatures match the emission-measure-weighted EUV temperatures surprisingly well, but (assuming photospheric abundances) the GOES emission measures are smaller than the EUV emission measures by up to 50%, with the discrepancy larger at higher temperatures.

Coronal mass ejections (CMEs) are a major driver of space weather. To assess CME geoeffectiveness, among other scientific goals, it is necessary to reliably identify and characterize their morphology and kinematics in coronagraph images. Current methods of CME identification are either subjected to human biases or perform a poor identification due to deficiencies in the automatic detection. In this approach, we have trained the deep convolutional neural model Mask R-CNN to automatically segment the outer envelope of one or multiple CMEs present in a single difference coronagraph image. The empirical training dataset is composed of (1.13times 10^{5}) synthetic coronagraph images with known pixel-level CME segmentation masks. It is obtained by combining quiet (no CME) coronagraph observations, with synthetic white-light CMEs produced using the Graduated Cylindrical Shell geometric model and ray-tracing technique. To filter the different instances found by Mask R-CNN, we use the temporal consistency of mask properties such as the intersection over union ((IoU)). We found that our model-based trained Mask R-CNN infers segmentation masks that are smooth and topologically connected (without holes or isolated patches). While the inferred masks are not representative of the detailed outer envelope of complex CMEs, the neural model can better differentiate a CME from other radially moving background/foreground features, segment multiple simultaneous CMEs that are close to each other, and work with images from different instruments. This is accomplished without relying on kinematic information, i.e. only the included in the single input difference image. We obtain a median (IoU=0.98) for (1.6times 10^{4}) synthetic validation images, and (IoU=0.77) when compared with two independent manual segmentations of 115 observations acquired by the COR2-A, COR2-B, and LASCO C2 coronagraphs. The methodology presented in this work can be used with other CME models to produce more realistic synthetic brightness images while preserving desired morphological features, and obtain more robust and/or tailored segmentations.

Understanding the initiation mechanisms of major solar flares remains a central problem in solar physics, particularly when multiple complex magnetic topologies such as null points and flux ropes are involved. This study aims to investigate the magnetic configuration that led to the X1.3-class flare on 2022 March 30 in NOAA Active Region (AR) 12975 and to understand the role of the magnetic topology in triggering the flare. For this purpose, we employ a non-force-free-field (NFFF) extrapolation technique on vector magnetogram data from Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamic Observatory (SDO) to reconstruct a temporal sequence of the three-dimensional (3D) coronal magnetic-field configurations during the flare. In the flaring region, we identify key magnetic features such as a 3D magnetic null, sheared arcades, a magnetic flux rope (MFR), and quasi-separatrix layers (QSLs). The sequence of the extrapolation shows the development of a magnetic-flux rope from an initially sheared arcade structure. The development is suggested to be facilitated by the magnetic reconnections in the magnetic field lines of the sheared arcade. In addition, reconnection at the 3D null is also found to occur. The footpoints of the null point coincide with observed pre-flare brightenings and the some part of the flare ribbons, indicating null-point reconnection as a key flare trigger. Furthermore, there is also indication of the onset of the slipping reconnection at the footpoints of the null which may further contribute to the ribbon brightenings. Overall, based on the extrapolation sequence, a plausible scenario can be proposed in which the reconnection between sheared arcades may lead to the formation and subsequent rise of the magnetic flux rope, which becomes unstable by the removal of the overlying flux through 3D null-point reconnection leading to the flaring event.

We consider effects of the harmonic magnetic field boundary conditions at the top of the dynamo domain on the dynamo stability inside the solar convection zone. These boundary conditions allow us to quantify the helical properties of the coronal magnetic field that stems from the dynamo region. In connecting the tangential component of the mean electric field we are able to take into account the effect the diffusive properties of the stellar corona on the dynamo instability. The model shows that effect of the vacuum boundary conditions can be restored if we introduce a few orders of magnitude jump of the coronal magnetic field turbulent diffusion over its typical value at the top of the dynamo domain. The parameters of this jump define the critical instability threshold of the (alpha ) effect in the (alpha ^{2}Omega ) dynamo.