Astrophysics and Space Science最新文献

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.

The Total Electron Content (TEC) in the ionosphere undergoes dynamic variations, primarily driven by solar radiation and influenced by a range of factors including geomagnetic activity. In this study, we utilize a backpropagation (BP) neural network for TEC modeling and systematically investigate how different geomagnetic indices affect model accuracy across various latitudes in East China, using 12 years (2008–2019) of TEC data from four International GNSS Service (IGS) GIM grid points (CA, BF, JN, and HS). The model inputs include solar activity indices (F10.7, Lyman-(alpha )), periodic variations in TEC (annual, semiannual, diurnal, semidiurnal), and geomagnetic indices (Dst, Kp, Ap, AE), integrated into a two-hidden-layer network (20×20 nodes). Incorporating geomagnetic inputs significantly improved TEC modeling accuracy at mid-latitudes and yielded only marginal improvements at low latitudes. Specifically, at mid-latitude grid points (CA, BF), including geomagnetic indices reduced the Root Mean Squared Error (RMSE) by ∼7% (from 1.70 to 1.58 TECU) under geomagnetically quiet conditions. Moreover, the model effectively captured ionospheric responses during geomagnetic storms, accurately reproducing both positive and negative phases of ionospheric storms, as demonstrated by two representative events (October 25, 2011 and March 17, 2015). In contrast, at low-latitude grid points (JN, HS), the improvements were less pronounced, with less than 2% RMSE reduction under geomagnetically quiet conditions and only minor improvements during these ionospheric storm events. The optimized 20×20 BP neural network architecture achieved a favorable balance between accuracy and computational efficiency, providing useful insights for region-specific TEC modeling and reference for TEC forecasting.

This study explores the stability of an (f(R,T)) gravity-based cosmological model using perturbation techniques and numerical methods. Stability conditions are examined by analyzing the growth rate of small perturbations in density and pressure. Furthermore, the Runge-Kutta fourth-order (RK4) method is employed to numerically track the evolution of these perturbations and validate the theoretical predictions. To ensure consistency with observational data, the model is tested against Hubble parameter measurements and the Pantheon Type Ia supernova dataset using the Maximum Likelihood Estimation (MLE) method. The findings provide deeper insights into the stability of modified gravity models and underscore the significance of combining analytical, numerical, and observational approaches in cosmological studies.

This paper presents the class of solutions to the Einstein field equations for the uncharged static spherically symmetric compact object PSR J0952–0607 by using Generalized Tolman - Kuchowicz space-time metric with quadratic equation of state. We have obtained the bound on the model parameter n graphically and achieved the stable stellar structure of the mathematical model of a compact object. The stability of the generated model is examined by the Tolman - Oppenheimer - Volkoff equation and the Harrison-Zeldovich-Novikov criterion. This anisotropic compact star model fulfills all the required stability criteria including the causality condition, adiabatic index, Buchdahl condition, Herrera’s cracking condition, and pertains free from central singularities.

The eight ultra-long period pulsars (ULPPs) in radio bands have been discovered recently, e.g., ASKAP J1935+2148 with a spin period of 53.8 min, which are much longer than those of normal pulsars, spanning from 0.016 s to 23.5 s, however the origins, spin evolutions and emission mechanisms of these sources are still puzzling. We investigate how the ultra-long period of ASKAP J1935+2148 is evolved by the braking of relativistic particle wind, in a time scale of about 0.1 - 1 Myr, from a normal pulsar with local superstrong magnetic fields. In addition, it is noticed that the ULPPs in the period versus period derivative diagram are much below the “death line”, implying their different characteristics from the normal pulsars. Five sources (including ASKAP J1935+2148) in total eight ULPPs share the rotational energy loss rates to be lower than their respective radio emission luminosities, a phenomenon that can be accounted for by the sustainable radio bursts induced through the reconnection of locally concentrated magnetic field lines. The diversity and complexity of ULPP radio emissions should be closely related to the presence of magnetic reconnection rather than rotational powered discharges in the gaps. Furthermore, it is suggested that the coherent radio emissions of pulsars may have two origins, one from the rotation-powered electric voltage that accounts for the normal pulsar phenomena and the other from the magnetic reconnection-induced continual radio bursts that account for the ULPP observations.

We present the relationship between Forbush decreases (FDs) and associated geomagnetic storms, as well as their connections to interplanetary (IP) solar wind parameters, using high resolution minute data. FDs were classified into groups based on main phase decrease steps, and each group was analyzed using superposed epoch analysis. The results reveal that fast, turbulent, high-field sheath structures form before and pass during the onset of coronal mass ejection (CME) driven FDs, whereas corotating interaction region (CIR) driven events exhibit delayed amplification and more perturbed dynamics. Time lags between the onset of FDs and geomagnetic storms were calculated and discussed, providing insights crucial for space weather forecasting. Correlation analyses between FD amplitude and peak values of various IP parameters were performed and discussed. The relationship between FDs and geomagnetic storms was analyzed, revealing that for CME-driven events, FD amplitudes exhibit a stronger correlation with moderate and strong geomagnetic storms compared to extreme storms. The weaker correlation during extreme CME-driven storms may result from complex magnetospheric responses caused by successive events and prolonged southward interplanetary magnetic field Bz, unlike the more direct responses observed in moderate and strong single-event storms. Interplanetary coronal mass ejection (ICME) manifestations were also correlated with FD amplitude, showing that events with fast forward shocks and compression sheath regions exhibit stronger correlations than those without shocks. Furthermore, we analyzed the energy dependence of FD amplitude using data from twelve neutron monitor stations at different latitudes and altitudes across the globe. As a result, the cosmic ray (CR) energy spectrum exhibits a two-step linear dependence with the FD amplitude, in the lower rigidity FD amplitude decreases sharply, while in higher rigidity regimes, the decrease is more gradual. A broader energy spectrum is recommended for more comprehensive conclusions.

Observations by the James Webb Space Telescope (JWST) have led to a series of groundbreaking discoveries that challenge our current understanding of early galaxy formation. A large number of galaxies have been surprisingly identified during the epoch of cosmic dawn, the redshift of (zsim 11-14), 13.4 to 13.5 billion years ago, far exceeding theoretical predictions. Additionally, many faint AGNs hosting supermassive black holes have been discovered at (z>4). What was happening in the early universe? This article provides an overview of these latest findings.