Astrophysics and Space Science最新文献

The intrinsic nature of the magnetosphere is important in understanding the role of different drivers in its dynamics. In this study, an attempt was made to characterize and quantify the complexity in the magnetosphere during Solar Cycle 24 using the Dst index as a measure. Two approaches were considered: chaos and multifractal analysis. The chaotic analysis using the Lyapunov exponent, correlation dimension, and entropy measures revealed that the magnetosphere is chaotic for every year of Solar Cycle 24. Furthermore, there was no significant difference between the complexity in Solar Cycle 24 and the previous 4 solar cycles (20-23). Chaotic parameters (sample entropy, Lyapunov exponent, and correlation dimension) showed strong correlations with annual mean Dst values throughout Solar Cycle 24. Multifractal detrended fluctuation analysis parameters showed weak relationships with annual means but revealed underlying structures in Dst values.

Pulsars, the cosmic lighthouses, are strongly self-gravitating objects with core densities significantly exceeding nuclear density. Since the discovery of the Hulse–Taylor pulsar 50 years ago, binary pulsar studies have delivered numerous stringent tests of General Relativity (GR) in the strong-field regime as well as its radiative properties—gravitational waves (GWs). These systems also enable high-precision neutron star mass measurements, placing tight constraints on the behaviour of matter at extreme densities. In addition, pulsars act as natural detectors for nanohertz GWs, primarily from supermassive black hole binaries, culminating in the first reported evidence of a stochastic GW background in 2023. In this article, I review key milestones in pulsar research and highlight some of contributions from my own work. After a brief overview of the gravity experiments in §1, I review the discovery of pulsars—particularly those in binaries—and their critical role in gravity experiments (§2) that laid the foundation for recent advances. In §3, I present the latest efforts on GR tests using the Double Pulsar and a pioneer technique to constrain the dense matter equation of state. §4 demonstrates the potential of binary pulsars on testing alternative theories to GR. Advances in nanohertz GW detection with pulsar timing arrays are discussed in §5. I outline some of the current challenges in §6 and conclude with final remarks in §7.

Solar flares represent a significant element in the broader context of space weather phenomena, exerting a direct influence on the Earth’s ionosphere. The ionosphere is a region of the Earth’s atmosphere that is ionized by solar radiation, which also undergoes seasonal changes. The present study is concerned with elucidating the seasonal fluctuations in differential vertical total electron content (DVTEC) of the ionosphere during solar flare events of solar cycle 24. The present study examines M and C solar flares during the ascending (2013), peak (2014), and descending phases (2015) of solar cycle 24. A total of 207 solar flare events were observed over a three-year period. The IISC is the low-latitude GNSS site in Bangalore, India (geographic latitude 13.02°N, geographic longitude 77.57°E) was utilized for this study. The results indicate the presence of an anomalous winter phenomenon in 2014, as well as a peak in DVTEC during the winter season. The recombination process, which involves the O/N₂ ratio, is responsible for the higher (Delta )DVTEC observed during the winter season. Additionally, modifications to dissociation-recombination during the summer season and vertical advection in the F layer contributed to the 2014 winter anomaly. Among the solar indices examined, a correlation of 0.45, between d(EUV flux)/dt and (Delta )DVTEC, indicating EUV flux as the primary source of ionization in the ionosphere.

The detections (Abbott et al. in Astrophys. J. Suppl. Ser. 267(2):29, 2023; Abbott et al. in SoftwareX 13:100658, 2021) and analysis of gravitational waves (GWs) have introduced us in a new era of our understanding of the cosmos, providing new insights into astrophysical systems involving massive objects as black holes and neutron stars. Normally the precise sky localization of a GW source needs data from three or more observatories (Abbott et al. in Phys. Rev. Lett. 116(22):221101, 2016c; Abbott et al. in Phys. Rev. Lett. 119(14):141101, 2017c). However, the results presented in this article demonstrate that it is in fact possible to obtain the position of a GW source in a small region of the celestial sphere using data from just two GW observatories, in this case LIGO Hanford and LIGO Livingston. Furthermore, we are also able to reconstruct the gravitational-wave polarization (Poisson and Will in Gravity: Newtonian, Post-Newtonian, Relativistic, Cambridge University Press, Cambridge, 2014) modes (PMs) for the GW170104 (Abbott et al. in GW170104: observation of a 50-solar-mass binary black hole coalescence at redshift 0.2. Phys. Rev. Lett. 118(22):221101, 2017b) and GW150914 (Abbott et al. in Phys. Rev. D 93(12):122003, 2016a) events, with data from only these two detectors. The procedure only uses the spin 2 properties of the GW, so that it does not rely on specific assumptions on the nature of the source. Our findings are possible through careful data filtering methods (Moreschi in J. Cosmol. Astropart. Phys. 1904:032, 2019), the use of refined signal processing algorithms (Moreschi in Astrophys. Space Sci. 369(1):12, 2024), and the application of dedicated denoising (Mallat in A Wavelet Tour of Signal Processing: The Sparse Way, Elsevier, Amsterdam, 2009) techniques. This progress in the GW studies represents the first instance of a direct measurement of PMs using such a limited observational data. We provide detailed validation through the reconstruction of PMs for different polarization angles, and calculations of residuals for the GW170104 event. We also test the procedure with synthetic data with ten different source locations and polarization angles.

Large-scale photometric surveys are revolutionizing astronomy by delivering unprecedented amounts of data. The rich data sets from missions such as the NASA Kepler and TESS satellites, and the upcoming ESA PLATO mission, are a treasure trove for stellar variability, asteroseismology and exoplanet studies. In order to unlock the full scientific potential of these massive data sets, automated data-driven methods are needed. In this review, I illustrate how machine learning is bringing asteroseismology toward an era of automated scientific discovery, covering the full cycle from data cleaning to variability classification and parameter inference, while highlighting the recent advances in representation learning, multimodal datasets and foundation models. This invited review offers a guide to the challenges and opportunities machine learning brings for stellar variability research and how it could help unlock new frontiers in time-domain astronomy.

This study investigates the reliability of the African Regional Ionospheric Total Electron Content (AfriTEC) model during the descending phase of Solar Cycle 24 (2016-2017) across East Africa. Using GNSS-derived TEC data from five equatorial and low-latitude stations MOIU, MAL2, ZAMB, ADIS, and MBAR the model’s performance is assessed through statistical metrics, including Mean Absolute Error (MAE) and correlation coefficient ((r)). Results indicate that the AfriTEC model effectively captures the diurnal and seasonal behavior of TEC, particularly during equinoxes, with MAE values generally below 1.5 TECU and correlation coefficients exceeding 0.80. However, discrepancies emerge during solstice periods and post-sunset hours, reflecting the model’s limitations in representing complex ionospheric processes such as the Equatorial Ionization Anomaly (EIA). To benchmark its performance, AfriTEC is also compared against the widely used NeQuick model. AfriTEC demonstrates superior regional adaptability and reduced error under most conditions, though it remains sensitive to localized ionospheric disturbances. These findings suggest that while AfriTEC is a valuable tool for ionospheric modeling in whole Africa especially at East African sector, enhancements incorporating real-time solar and geomagnetic indices could further improve its predictive capabilities.

Thermonuclear fusion reactions within stellar interiors are primarily responsible for generating energy and synthesizing the elements that compose the universe. Calculating the reaction rates provides essential information about the lifespan and luminosity of Sun-like stars, eventually, it has siginificant role in big-bang nucleosynthesis. In this article, we consider the exact thermonuclear reaction rate functions in standard, cut-off, and depleted tail cases. Since 1984, analytic solution of these thermonuclear reaction rates were obtained by many authors and a number of possible generalizations and their closed form solutions are available in the literature. The present study unifies all such generalizations through a single thermonuclear rate function via the techniques in statistical mechanics. A novel velocity distribution function is developed for interacting particles, extending their applicability to the maximum. Since real stellar scenarios often deviate from strict hydrostatic equilibrium case, this improved distribution captures these deviations effectively. The paper gives more emphasis on non-resonant reaction rates in depleted tail case and obtain the closed-form solution in terms of Buschman H-function of two variables.

The plasma in the sun causes various magnetic activities on the surface of the sun, for example, the appearance of dark regions on the sun’s surface, known as sunspots. These dark regions are temporary and are cooler than their surroundings. The sunspot number is a variable that follows a periodic function having a period of 9 to 13 years. The sunspot phenomena are closely related to the solar flares and coronal mass ejection phenomena. Mathematical modeling and artificial neural networks have been used in this study to predict the number of sunspots. The sunspot cycles vary according to the magnetic activities, and the variation in profile affects shape and scale parameters. Weibull distribution with two parameters (shape and scale) has been used to model the profile of sunspot cycles. The shape parameters are modeled using the sine function, and the scale parameters are predicted using regression and Artificial Neural Network (ANN). The amplitude of cycle 25 is predicted using the precursor method applied via deep learning and found to be 166 ± 28. The expected occurrence time of the amplitude of cycle 25 is April 2025. The amplitude of cycle 26 is also determined.

The Moon very likely has polar caps, but not as conspicuous as the Earth or Mars; the Moon’s caps must be hidden under the surface. The southern polar cap is probably more aquiferous than the northern one. Our indication of ground water at the poles has been obtained by a remote sensing method. We use the gravity aspects, namely the combed strike angles, derived from a global gravity field model of the Moon (now providing the ground resolution ∼10 km already sufficient for this purpose). We cannot estimate the absolute amount of the lunar water, only the contrast between the polar areas and the other regions; the contrast is high, statistically significant – to 8 times more groundwater at the poles. For the southern polar zone, we confirm the results achieved by others, and we do it in a completely independent way. The lunar water is necessary for future permanent human missions on the Moon, like Artemis; they will start near the southern pole. Thus, our findings would have immediate applications. Observe and download: https://www.asu.cas.cz/~jklokocn/MOON25_supplements/

Using the isotropic and spherically symmetric but inhomogeneous Szekeres–Szafron metric, a model for the spherical dark halo around galaxies has been presented. By considering a scalar field with a second-order potential as the source of energy-momentum tensor, we have demonstrated that the energy density on small scales can provide an explanation for dark matter presence in proximity to galaxies (or galaxy clusters). In other words, this model can justify the flattening of the rotation curves at distances far from the center of the galaxy.