Solar Physics最新文献

Unraveling the intricate interplay between the solar photosphere’s magnetic field and the dynamics of the upper solar atmosphere is paramount to understanding the organization of solar magnetic fields and their influence on space weather events. This study delves into the organization of photospheric magnetic fields particularly in the context of coronal holes (CHs), as they are believed to harbor the sources of fast solar wind. We employed the signed measure technique on synthetic images that depict various arrangements of magnetic fields, encompassing imbalances in the sign of the magnetic field (inward and outward) and spatial organization.

This study provides compelling evidence that the cancellation functions of simulated regions with imbalanced magnetic fields along the boundaries of supergranular cells align with cancellation function trends of observed photospheric magnetic regions associated with CHs. Thus the analysis serves as a significant proof that CHs arise from the formation of imbalanced magnetic patterns on the edges of supergranular cells.

Using the magnetic power spectrum approach, we explore the magnetic energy changes at different spatial scales in four moderate-size decaying active regions (ARs). We find the dominant energy variations to take place at large spatial scales while the energy at low scales changes insignificantly. The analysis of the energy transfer function allows us to conclude that the direct turbulent cascade might occur occasionally and does not significantly contribute to the flux budget. Instead, we confirm the turbulent erosion, along with turbulent diffusion, to be the dominant mechanisms of the AR decay. We also reveal a gradual monotonous convergence of two coherent sunspots of opposite magnetic polarities as the decay proceeds. The sunspots exhibit magnetic connection seen as plasma loops in UV images. We suppose that the convergence is a result of an AR-size (Omega )-loop submergence beneath the photosphere.

The iodine-cell technique, which is known to be efficient in precisely establishing Doppler velocity shifts, was once applied by the author to measuring the solar differential rotation based on full-disk spectroscopic observations (Takeda and Ueno 2011). However, the data reduction procedure (in simple analogy with the stellar case) adopted therein was not necessarily adequate, because a specific characteristic involved with the disk-resolved Sun (i.e., center–limb variation of line strengths) was not properly taken into consideration. Therefore this problem is revisited based on the same data but with an application to theoretical spectrum fitting, which can yield absolute heliocentric radial velocities ((v_{mathrm{obs}})) in a consistent manner as shown in the study of solar gravitational redshift (Takeda and Ueno 2012). Likewise, instead of converting (v_{mathrm{obs}}) into (omega ) (angular velocity) at each disk point, which suffers considerable errors especially near the central meridian, (omega ) is derived this time by applying the least squares analysis to a dataset comprising (v_{mathrm{obs}}) values at many points. This new analysis resulted in (omega ) (deg day⁻¹) = (13.92 (pm 0.03) -1.69(pm 0.34)sin ^{2}psi -2.37(pm 0.62) sin ^{4}psi ) ((psi ): the heliographic latitude) along with the gravitational redshift of 675 m s⁻¹, which are favorably compared with previous publications. In addition, how the distribution of observing points on the disk affects the result is also examined, which reveals that rotation parameters may suffer appreciable errors depending on cases.

Coronal jets are believed to be the miniature version of large-scale solar eruptions. In particular, the eruption of a minifilament inside the base arch is suggested to be the trigger and even driver of blowout jets. Here, we propose an alternative triggering mechanism, based on high-resolution H(alpha ) observations of a blowout jet associated with a minifilament and an M1.2-class flare. The minifilament remains largely stationary during the blowout jet, except that it is straddled by flare loops connecting two flare ribbons, indicating that the magnetic arcade embedding the minifilament has been torn into two parts, with the upper part escaping with the blowout jet. In the wake of the flare, the southern end of the minifilament fans out like neighboring fibrils, indicative of mass and field exchanges between the minifilament and the fibrils. The blowout jet is preceded by a standard jet. With H(alpha ) fibrils moving toward the single-strand spire in a sweeping fashion, the standard jet transitions to the blowout jet. A similar pattern of standard-to-blowout jet transition occurs in an earlier C-class flare before the minifilament forms. The spiraling morphology and sweeping direction of these fibrils are suggestive of their footpoints being dragged by the leading sunspot that undergoes clockwise rotation for over two days. Soon after the sunspot rotation reaches a peak angular speed as fast as 10 deg h⁻¹, the dormant active region becomes flare productive, and the minifilament forms through the interaction of moving magnetic features from the rotating sunspot with satellite spots/pores. Hence, we suggest that the sunspot rotation plays a key role in building up free energy for flares and jets and in triggering blowout jets by inducing sweeping motions of fibrils.

The Disturbance Storm Time (Dst) Index stands as a crucial geomagnetic metric, serving to quantify the intensity of geomagnetic disturbances. The accurate prediction of the Dst index plays a pivotal role in mitigating the detrimental effects caused by severe space-weather events. Therefore, Dst prediction has been a long-standing focal point within the realms of space physics and space-weather forecasting. In this study, a Temporal Convolutional Network (TCN) is deployed in tandem with the Integrated Gradient (IG) algorithm to predict the Dst index and scrutinize its associated physical processes. With these two components, our model can give the contribution of each input parameter to the outcome along with the forecast. The TCN component of our model utilizes interplanetary observational data, encompassing the vector magnetic field, solar-wind velocity, proton temperature, proton density, interplanetary electric field, and other relevant parameters for forecasting Dst indices. Despite the disparity in test sets, our model’s forecast accuracy approximates the error levels of the prior models. Remarkably, the prediction error of these machine-learning models has become comparable to the inherent error between the Dst index itself and the actual ring-current strength.

To understand the physical process behind the forecasting model, the IG algorithm was applied in our prediction model, in an attempt to analyze the underlying physical process of the machine-learning black box. In the temporal dimension, it is evident that the more recent the time, the more substantial the influence on the final prediction. Regarding the physical parameters, besides the historical Dst index itself, the flow pressure, the (z)-component of the magnetic field, and the proton density all significantly contribute to the final prediction. Additionally, IG attributions were analyzed for subsets of data, including different Dst-index ranges, different observation times, and different interplanetary structures. Most of the subsets exhibit an IG matrix with deviations from the mean distribution, which indicates a complex nonlinear system and sensitivity of the prediction to input values. These analyses align with physical reasoning and are in good agreement with previous research. The results affirm that the TCN+IG technique not only enhances space-weather forecast accuracy but also advances our comprehension of the underlying physical processes in space weather.

The variability of galactic cosmic rays near Earth is nearly isotropic and driven by large-scale heliospheric modulation but rarely can very local anisotropic events be observed in low-energy cosmic rays. These anisotropic cosmic-ray enhancement (ACRE) events are related to interplanetary transients. Until now, two such events have been known. Here, we report the discovery of the third ACRE event observed as an increase of up to 6.4% in count rates of high- and midlatitude neutron monitors between ca. 09 – 14 UT on 5 November 2023 followed by a moderate Forbush decrease and a strong geomagnetic storm. This is the first known observation of ACRE in the midrigidity range of up to 8 GV. The anisotropy axis of ACRE was in the nearly anti-Sun direction. Modeling of the geomagnetic conditions implies that the observed increase was not caused by a storm-induced weakening of the geomagnetic shielding. As suggested by a detailed analysis and qualitative modeling using the EUHFORIA model, the ACRE event was likely produced by the scattering of cosmic rays on an intense interplanetary flux rope propagating north of the Earth and causing a glancing encounter. The forthcoming Forbush decrease was caused by an interplanetary coronal mass ejection that hit Earth centrally. A comprehensive analysis of the ACRE and complex heliospheric conditions is presented. However, a full quantitative modeling of such a complex event is not possible even with the most advanced models and calls for further developments.

Geomagnetic storms resulting from solar disturbances impact telecommunication and satellite systems. Satellites are positioned at Lagrange point L1 to monitor these disturbances and give warning 30 min to 1 h ahead. As propagation delay from L1 to Earth depends on various factors, estimating the delay using the assumption of ballistic propagation can result in greater uncertainty. In this study, we aim to reduce the uncertainty in the propagation delay by using machine-learning (ML) models. Solar-wind velocity components ((V_{ mathrm{x}}), (V_{mathrm{y}}), (V_{mathrm{z}})), the position of Advanced Composition Explorer (ACE) at all three coordinates ((r_{mathrm{x}}), (r_{mathrm{y}}), (r_{mathrm{z}})), and the Earth’s dipole tilt angle at the time of the disturbances are taken as input parameters. The target is the time taken by the disturbances to reach from L1 to the magnetosphere. The study involves a comparison of eight ML models that are trained across three different speed ranges of solar-wind disturbances. For low and very high-speed solar wind, the vector-delay method fares better than the flat-plane propagation method and ML models. Ridge regression performs consistently better at all three speed ranges in ML models. For high-speed solar wind, boosting models perform well with an error of around 3.8 min better than the vector-delay model. Studying the best-performing models through variable-importance measures, the velocity component (V_{mathrm{x}}) is identified as the most important feature for the estimation and aligns well with the flat-plane propagation method. Additionally, for slow solar-wind disturbances, the position of ACE is seen as the second most important feature in ridge regression, while high-speed disturbances emphasize the importance of other vector components of solar-wind speed over the ACE position. This work improves our understanding of the propagation delay of different solar-wind speed and showcases the potential of ML in space weather prediction.

The AIA 304 Å channel on board the Solar Dynamics Observatory (SDO) offers a unique view of (approx 10^{5}text{ K}) plasma emitting in the He ii 304 Å line. However, when observing off-limb, the emission of the (small) cool structures in the solar atmosphere (such as spicules, coronal rain and prominence material) can be of the same order as the surrounding hot coronal emission from other spectral lines included in the 304 Å passband, particularly over active regions. In this paper, we investigate three methods based on temperature and morphology that are able to distinguish the cool and hot emission within the 304 Å passband. The methods are based on the Differential Emission Measure (DEM), a linear decomposition of the AIA response functions (RFit) and the Blind Source Separation (BSS) technique. All three methods are found to produce satisfactory results in both quiescent and flaring conditions, largely removing the diffuse corona and leading to images with cool material off-limb in sharp contrast with the background. We compare our results with co-aligned data from the Interface Region Imaging Spectrograph (IRIS) in the SJI 1400 Å and 2796 Å channels, and find the RFit method to best match the quantity and evolution of the cool material detected with IRIS. Some differences can appear due to plasma emitting in the (log T=5.1,text{--},5.5) temperature range, particularly during the catastrophic cooling stage prior to rain appearance during flares. These methods are, in principle, applicable to any passband from any instrument suffering from similar cool and hot emission ambiguity, as long as there is good coverage of the high-temperature range.

The solar eruption that occurred on 28 November 2023 (SOL2023-11-28) triggered an intense geomagnetic storm on 1 December 2023. The associated terrestrial auroras manifested at the most southern latitudes in the northern hemisphere observed in the past two decades. In order to explore the profound geoeffectiveness of this event, we conducted a comprehensive analysis of its solar origin to offer potential factors contributing to its impact. Magnetic flux ropes (MFRs) are twisted magnetic structures recognized as significant contributors to coronal mass ejections (CMEs), thereby impacting space weather greatly. In this event, we identified multiple MFRs in the solar active region and observed distinct slipping processes of the three MFRs: MFR1, MFR2, and MFR3. All three MFRs exhibit slipping motions at a speed of 40 – 137 km s⁻¹, extending beyond their original locations. Notably, the slipping of MFR2 extends to (sim 30text{ Mm}) and initiates the eruption of MFR3. Ultimately, MFR1’s eruption results in an M3.4-class flare and a CME, while MFR2 and MFR3 collectively produce an M9.8-class flare and another halo CME. This study shows the slipping process in a multi-MFR system, showing how one MFR’s slipping can trigger the eruption of another MFR. We propose that the CME–CME interactions caused by multiple MFR eruptions may contribute to the significant geoeffectiveness.