Solar Physics最新文献

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 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.

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.

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.

The combination of the H i Ly(alpha ) (121.6 nm) line formation mechanism with ultraviolet (UV) Ly(alpha ) and white-light (WL) observations provides an effective method for determining the electron temperature of coronal mass ejections (CMEs). A key to ensuring the accuracy of this diagnostic technique is the precise calculation of theoretical Ly(alpha ) intensities. This study performs a modeled CME and its driven shock via the three-dimensional numerical magneto-hydrodynamic simulation. Then, we generate synthetic UV and WL images of the CME and shock within a few solar radii to quantify the impact of different assumptions on the theoretical Ly(alpha ) intensities, such as the incident intensity of the solar chromospheric Ly(alpha ) line ((I_{disk})), the geometric scattering function ((p(theta ))), and the kinetic temperature ((T_{ boldsymbol{n}})) assumed to be equal to either the proton ((T_{p})) or electron ((T_{e})) temperature. By comparing differences of the Ly(alpha ) intensities of the CME and shock under these assumptions, we find that: (1) Using the uniform or Carrington maps of the disk Ly(alpha ) emission underestimates the corona Ly(alpha ) intensity (with relative uncertainties below 10%) compared to the synchronic map, except for a slight overestimate (<4%) observed in the partial CME core. The Carrington map yields lower uncertainties than the uniform disk. (2) Neglecting the geometric scattering process has a relatively minor impact on the Ly(alpha ) intensity, with a maximum relative uncertainty of no more than 5%. The Ly(alpha ) intensity is underestimated for the most part but overestimated in the CME core. (3) Compared to the assumption (T_{boldsymbol{n}}=T_{p}), using (T_{boldsymbol{n}}=T_{e}) leads to more complex relative uncertainties in CME Ly(alpha ) intensity. The CME core and void are both overestimated, with the maximum relative uncertainty in the core exceeding 50% and in the void remaining below 35%. An appropriate increasing proton-to-electron temperature ratio can reduce the uncertainty in the CME core and void. In the CME front, both overestimates and underestimates exist with relative uncertainties of less than 35%. The electron temperature assumption has a smaller impact on the shock, with an underestimated relative uncertainty of less than 20%.

In this study, we investigate the decay of sunspots in the active region NOAA 13229 using data from the ASO-S/FMG and SDO/HMI. We closely examine the decay patterns of sunspots S1 and S2, which reveal different decay rates and features due to the mechanisms of magnetic cancellation, dispersion, and the role of horizontal flows. Our analysis highlights the significant impact of magnetic flux changes, including the decrease of both the sunspot area and magnetic flux over time, which adheres to distinct decay laws. This study elucidates the complex interplay between magnetic submergence, cancellation, and dispersion in the sunspot decay process, contributing to our understanding of the underlying mechanisms driving these phenomena. Our results emphasize the importance of horizontal flow dynamics in shaping the decay characteristics of sunspots, providing insights for the role played by the magnetic and plasma processes in solar active regions.

The long-term study of the temporal variation of the rotation period of the solar photosphere, chromosphere, and corona has been widely undertaken. To date it is unclear whether the temporal variation of the rotation period of the solar transition region has a systematic periodicity. In this article we perform a study on the temporal variation of the rotation period of the solar transition region. For this purpose, we use the Lyman (alpha ) line emission at a wavelength of 121.56 nm corresponding to the solar transition region from the year 1965 to 2019, covering four complete solar cycles (i.e., Cycles 21, 22, 23, 24) as well as descending and ascending phases of Cycles 20 and 25, respectively. An autocorrelation analysis depicts that the average sidereal rotation period of the transition region (from 1965 to 2019) is 24.8 days. Furthermore, we find that a significant periodicity of about 12 years exists in the temporal variation of the sidereal rotation period of the solar transition region. The results indicate that this periodicity is closely linked to the 11-year Schwabe cycle. A cross-correlation analysis between the time series of the sidereal rotation period and sunspot numbers (as a function of lag in years) exhibits a positive correlation between these aforementioned parameters. From this result, we can state that the sidereal rotation period of the solar transition region leads the solar activity by about six months. This correlation again proves the periodicity of about 11 years in the rotation period of the transition region which is closely linked to the 11-year Schwabe cycle. Furthermore, long-term variation of rotation periods also demonstrates a decreasing trend from 1965 to 2019, which is similar to that in the sunspot numbers. From this long-term study, it seems that solar activity is largely driven by solar rotation.