Astrophysics and Space Science最新文献

Coronal mass ejections (CMEs) are major drivers of space weather disturbances, and their deflection from the radial direction critically affects their potential impact on Earth. While the influence of the surrounding magnetic field in guiding CME trajectories is well established, accurately predicting non-radial propagation remains a challenge. In this work, we introduce and compare recently developed techniques for analyzing the early deflection of eruptive events. We revisit a largely deflected prominence-CME event of 2010 December 16 using an improved tracking framework and a new application of the topological path method. Our results suggest the deflection of the eruption is dominated by the channeling of the magnetic field lines. This study offers new physical insight into CME guidance mechanisms and validates the predictive capability of the topological path, highlighting its potential as a diagnostic tool for estimating the propagation direction of strongly deflected events.

We review the observational evidence for the empirical upper luminosity limit in the Hertzsprung-Russell Diagram. We discuss its impact on our understanding of the evolution of the most massive stars, the importance of the high mass loss events that shape the upper limit, and the instabilities that may tigger the eruptions in stars close to their Eddington Limit.

Solar flares, intense solar eruptions, discharge electromagnetic radiation and energetic particles that may have major consequences for both space weather and Earth’s atmospheric conditions. Therefore, developing high-precision forecasting models is crucial. In this paper, we propose a solar flare prediction model, which integrates the Swin Transformer with a TCN augmented by a global attention mechanism, named SwinTCN-Att, for predicting whether ≥C- and ≥M-class flare events will erupt in the solar active regions (ARs) in the next 24 hours. We collected magnetogram data from solar ARs obtained from the Space Weather Helioseismic and Magnetic Imager Active Region Patch (SHARP) dataset, spanning from May 2010 to December 2019, and selected 16 magnetic field feature parameters from the SHARP data. The construction of the model is carried out in two stages: first, the spatial characteristics of the magnetogram are captured using the Swin Transformer; next, these spatial features are integrated with 16 magnetic field parameters. Temporal features are then derived using TCN with a global attention mechanism to predict solar flares. Then, following model training and testing, we evaluated performance using five different assessment metrics, with the True Skill Statistic (TSS) serving as the primary evaluation metric. The results show that the TSS scores achieved were 0.825 ± 0.042 for ≥C-class flares and 0.879 ± 0.025 for ≥M-class flares, marking a significant improvement over previous models. These results demonstrate that the proposed SwinTCN-Att model effectively integrates relevant solar flare information, combines the strengths of both individual models, and captures solar flare evolution features, achieving superior predictive performance.

In this paper, we present a comprehensive validation of bottomside electron density profile (EDP) thickness (B0) and shape (B1) parameters derived through least-square fitting of FORMOSAT-7/COSMIC-2 radio occultations with the coincident-colocated Digisonde EDPs at 24 locations spanning equatorial, low-, and mid-latitude regions and the default bottomside modeling option in the latest edition of the International Reference Ionosphere model (IRI-2020). These parameters are essential descriptors of the ionospheric bottomside morphology, which are critical for characterizing the vertical structure of the ionosphere and are influenced by solar flux, geomagnetic activity, and space weather dynamics. Leveraging a large dataset from COSMIC-2, we employed rigorous quality constraints through visual inspections and defined exclusion criteria to identify the most representative profiles for investigating the diurnal, seasonal, and longitudinal variations of the parameters as a function of local time during the period from 2020 to 2022, corresponding to the ascending phase of solar cycle 25. The results demonstrate that COSMIC-2 derived B0 and B1 parameters have better agreement with Digisonde observations than those predicted by IRI-2020, highlighting the significance of COSMIC-2 profile parameters towards improvement in empirical ionosphere models.

In this work, we studied the helium-induced collisional excitation of the radical ion HCl⁺. Our work focuses on calculating two-dimensional potential energy surfaces (PES) to study the interaction due to the collision between HCl⁺ and He, and on analyzing the influence of the isotopic effect on cross sections and collision rates. For Ab initio calculations of PES (^{2}A^{prime }) and (^{2}A^{prime prime }) of HCl⁺(X(^{2}Pi ))-He complex, we used the RCCSD(T)-F12 method with cc-pVQZ-F12 basis sets. These surfaces have been fitted using the Reproducing Kernel Hilbert Space (RKHS) method and were submitted to the close-coupling approach in order to work out the inelastic integral cross sections. Collision cross sections taking into account the fine structures of HCl⁺ have been performed for kinetic energies up to 3500 cm⁻¹ and the thermal excitation rates for kinetic temperatures varying from (4K) up to 400 K. It appears that the difference in the cross section and collisional rate cofficients for the H³⁵Cl⁺ and H³⁷Cl⁺ colliding with He was found to be negligeable. In contrast, a significant difference in effective cross-sections and collision rates between HCl⁺-He and DCl⁺-He was observed to the extent that it is impossible to make estimation of collision rates of deuterated species from those of the hydrogenated species.

We consider the dependence of diffusion spectra on acceleration and deceleration of the shell, which can be caused by the interaction of the shell with the environment, on the duration of the action of the gamma-ray burst (GRB) source and on the period of its action. With periodic action of the GRB source, a second maximum appears in the diffusion spectra at high frequencies. The closest to typical value of the low-energy spectral index is obtained for a decelerating shell with a duration of action of the GRB source not less than the diffusion time; the values of the high-energy spectral indices for the decelerating shell correspond to typical ones.

We review participatory science programs that have contributed to the understanding of star formation. The Milky Way Project (MWP), one of the earliest participatory science projects launched on the Zooniverse platform, produced the largest catalog of “bubbles” associated with feedback from hot young stars to date, and enabled the identification of a new class of compact star-forming regions (SFRs) known as “yellowballs” (YBs). The analysis of YBs through their infrared colors and catalog cross-matching led to discovering that YBs are compact photodissociation regions generated by intermediate- and high-mass young stellar objects embedded in clumps that range in mass from 10 - 10⁴ M_⊙ and luminosity from 10 - 10⁶ L_⊙. The MIRION catalog, assembled from 6176 YBs identified by citizen scientists, increases the number of candidate intermediate-mass SFRs by nearly two orders of magnitude. Ongoing work utilizing data from the Spitzer, Herschel and WISE missions involves analyzing infrared color trends to predict physical properties and ages of YB environments. Methods include applying summary statistics to histograms and color-color plots as well as SED fitting. Students in introductory astronomy classes contribute toward continued efforts refining photometric measurements of YBs while learning fundamental concepts in astronomy through a classroom-based participatory science experience, the PERYSCOPE project. We also describe an initiative that engaged seminaries, family groups, and interfaith communities in a wide variety of science projects on the Zooniverse platform. This initiative produced important guidance on attracting audiences that are underserved, underrepresented, or apprehensive about science.

Our analysis focuses on the Generalized Chaplygin Gas (GCG) model within the (f(Q, L_{m})) gravity framework, assuming (f(Q,L_{m})=beta Q+delta L_{m}) with (L_{m}=-rho ). Using the GCG equation of state (p=-frac{A}{rho ^{alpha }}), we derive expressions for energy density (rho (z)) and the Hubble parameter (H(z)). Constraining parameters through MCMC analysis with 31 cosmic chronometers, 15 BAO points, recent DESI DR2 BAO points and 1701 Pantheon+, we find best-fit values (H_{0}=74.026^{+3.332}_{-3.317}) km/s/Mpc, (A_{s}=0.880^{+0.019}_{-0.020}) and (alpha =-0.001^{+0.053}_{-0.052}) which are consistent with local measurements. The deceleration parameter transitions at (z_{tr} approx 0.79), with present value (q_{0}=-0.61), while the equation of state evolves toward (omega =-1) with (omega _{0} approx -0.79). Energy conditions are satisfied except for the SEC, which is violated during acceleration. The model predicts a cosmic age of 13.42 Gyr and shows freezing quintessence behavior in the (omega -omega ') plane, confirming its potential as a viable dark energy candidate.

Coronal mass ejections (CMEs) are significant drivers of space weather, and accurately predicting their propagation speed is crucial for mitigating their impact on Earth’s environment. In this study, we leverage machine learning techniques to model and predict CME speed at 20(R_{odot }) utilizing data from the Coordinated Data Analysis Workshop catalog. We considered data from Solar Cycles 23 and 24, divided into their rising, maxima, decline, and minima phases, to train multivariate linear regression, Random Forest, and XGBoost machine learning models aimed at predicting CME speeds at 20(R_{odot }). The machine learning models use linear speed, acceleration, width, and kinetic energy as input features to estimate CME speeds at 20(R_{odot }). Our results indicate that Random Forest and XGBoost models significantly outperform linear regression model across all datasets, achieving high R² values (≈0.97) and low relative errors (6%) for most phases, especially during high solar activity. Feature importance analysis identifies CME linear speed and acceleration as the dominant predictors of CME speed at 20(R_{odot }). This result is consistent with physical models, which describe CME propagation as being influenced primarily by initial speed and the drag force acting through acceleration or deceleration in the interplanetary medium. The trained models were applied to available events from Solar Cycle 25, to predict CME speeds at 20(R_{odot }). The predicted values showed very good agreement with the actual speeds reported in the CDAW catalog. This successful application demonstrates the models’ generalizability and potential for forecasting future CME dynamics. Furthermore, such data-driven predictions can complement physics-based models—such as the Drag-Based Model—by providing reliable speed estimates at specific heliocentric distances, thereby enhancing the accuracy of space weather forecasts.