Astrophysics and Space Science最新文献

We present a statistical analysis of the ages and metallicities of triple stellar systems that are known to host exoplanets. With controversial cases disregarded, so far 27 of those systems have been identified. Our analysis, based on an exploratory approach, shows that those systems are on average notably younger than stars situated in the solar neighborhood. Though the statistical significance of this result is not fully established, the most plausible explanation is a possible double selection effect due to the relatively high mass of planet-hosting stars of those systems (which spend less time on the main-sequence than low-mass stars) and that planets in triple stellar systems may be long-term orbitally unstable. The stellar metallicities are on average solar-like; however, owing to the limited number of data, this result is not inconsistent with the previous finding that stars with planets tend to be metal-rich as the deduced metallicity distribution is relatively broad.

Low-magnitude (FD(%) (geq -3%)) Forbush decreases (FDs) and their space weather linkages are recently becoming popular in the literature. Accurate timing and correct magnitude measurement of weak FDs are a desideratum. This demands disentangling the effects of the ever present cosmic ray (CR) diurnal wave that exerts undesirable influence on CR data. An enhanced version of a recently developed algorithm has been deployed to decompose raw CR data at Apatity (APTY) and Moscow (MOSC) neutron monitor (NM) stations into low and high frequency signals. A subroutine in the Fast Fourier transform software simultaneously calculates the magnitude and the epoch time of the events from the transformed data. The software selected 335 and 359 low magnitude FDs respectively for APTY and MOSC observatories. The large catalogues of low-amplitude FDs selected compared to those in literature may be attributed to the efficiency of the present algorithm. We carried out a regression analysis on the magnitude of these events and the corresponding solar wind disturbance agents. The analysis shows that for the APTY NM station, the correlation coefficient results (r) for FD-interplanetary magnetic field (IMF) and FD-planetary geomagnetic activity index (ap) relations are statistically significant at 95% confidence level. At the MOSC station, we find (r) ∼ −0.41 statistically significant at 95% confidence level for FD-solar wind speed (SWS) relation (after removing events due to co-rotating interaction regions). Analyzing all (low- and high-amplitude) FDs, (r) is relatively strong and statistically significant at 95% confidence level. The implications of these findings are discussed.

This observational study investigates the effects of interaction on the kinematics and chemical abundance of the peculiar galaxy ESO 287-IG50, which may be a polar ring galaxy in an ongoing formation process. The study utilized BVRI broad band imagery and longslit spectroscopy in the wavelength range of 4240-8700 Å. The STARLIGHT stellar population synthesis code was used to analyze the data, and standard diagnostic diagrams were employed to classify the main ionizing source of selected emission-line regions. Image analysis using filtering techniques revealed an inner ring with perpendicular structures at both ends, which could be the inner part of a bisymmetric spiral structure. Photometric analysis showed dusty filaments crossing the central structure, which was identified as the redder region of the galaxy, dominated by a non-negligible amount of dust. Shell-like structures, which could be remnants of a galaxy merging process, were also found. Image analysis through filtering revealed what appears to be an inner ring, with perpendicular structures at both ends, which could be the inner part of a bisymmetric spiral structure. Photometric analysis shows dusty filaments crossing the central structure. This region would be dominated by a non negligible amount of dust, identified as the redder region of the galaxy. A heliocentric radial velocity of 17 689±45 km s⁻¹ was measured, and the velocity profile exhibited a clear rotational behavior, with peak velocities of 110 km s⁻¹ to the SW and 80 km s⁻¹ to the other side. The analysis of the nuclear region using the STARLIGHT code revealed a stellar population consisting of approximately one-third young stars and two-thirds old stars. The predominance of an aged stellar population, a distinctive feature in galaxies undergoing interaction processes, can be attributed to the prolonged evolutionary period of this galaxy, as evidenced by the shell structures we identified as indicators of this interaction process. The [NII](lambda )6584Å/H(alpha ) ratio suggests that ESO 287-IG50 may be an AGN due to the excess of Nitrogen relative to Hydrogen in the residual spectrum, a feature not yet reported in the literature. Studying the line ratios and EW(H(alpha )), we notice that this galaxy exhibits a peculiar AGN, with a non-stellar origin ionization mechanism.

Accurate classification of celestial objects is essential for advancing our understanding of the universe. MargNet is a recently developed deep learning-based classifier applied to the Sloan Digital Sky Survey (SDSS) Data Release 16 (DR16) dataset to segregate stars, quasars, and compact galaxies using photometric data. MargNet utilizes a stacked architecture, combining a Convolutional Neural Network (CNN) for image modelling and an Artificial Neural Network (ANN) for modelling photometric parameters. Notably, MargNet focuses exclusively on compact galaxies and outperforms other methods in classifying compact galaxies from stars and quasars, even at fainter magnitudes. In this study, we propose enhancing MargNet’s performance by incorporating attention mechanisms and Vision Transformer (ViT)-based models for processing image data. The attention mechanism allows the model to focus on relevant features and capture intricate patterns within images, effectively distinguishing between different classes of celestial objects. Additionally, we leverage ViTs, a transformer-based deep learning architecture renowned for exceptional performance in image classification tasks. We enhance the model’s understanding of complex astronomical images by utilizing ViT’s ability to capture global dependencies and contextual information. Our approach uses a curated dataset comprising 240,000 compact and 150,000 faint objects. The models learn classification directly from the data, minimizing human intervention. Furthermore, we explore ViT as a hybrid architecture that uses photometric features and images together as input to predict astronomical objects. Our results demonstrate that the proposed attention mechanism augmented CNN in MargNet marginally outperforms the traditional MargNet and the proposed ViT-based MargNet models. Additionally, the ViT-based hybrid model emerges as the most lightweight and easy-to-train model with classification accuracy similar to that of the best-performing attention-enhanced MargNet. This advancement in deep learning will contribute to greater success in identifying objects in upcoming surveys like the Vera C. Rubin Large Synoptic Survey Telescope.

Pulsars are believed to be one of the most important celestial objects in the universe. The emission mechanism of pulsars is still a big paradox for physicists, as no completely acceptable theory can reach a suitable consensus with observation. Some complicated coherent plasma processes and acceleration-based mechanisms in the pulsar magnetosphere generate a powerful radio beam. There have been dedicated theories such as the geometrical and relativistic phase shift (RPS) methods. The relativistic phase shift method is owing to the combined effects of aberration-retardation (A/R) and polar cap current effect (PCC), etc., and by implementing this method, we make quantitative inspections to deduce the emission altitude of the pulsar’s radio emission components. Here, we have shown the estimation of the emission height of pulsar PSR B2111+46 for both core and conal components at 925 MHz, 1.25 GHz, 1.65 GHz, and 4.85 GHz. Moreover, we have estimated the foot point, normalized with the last open field line constant, corresponding to pulse edges at multiple bands. Current analysis of the paper shows that at least for PSR B2111+46, the full polar cap is not sensitive to radio emission for most of the cases in the given stretch of radio frequency.

The dynamics of Langmuir modes, waves (LW), and shear Langmuir vortices (SLV) are studied in kinematically complex astrophysical plasma flows. It is found that they exhibit several peculiar, velocity shear-induced, asymptotically persistent phenomena, including efficient energy exchange with the background flow and various kinds of instabilities, leading to their exponential growth and echoing solutions with persistent wave-vortex-wave conversions. There is a remarkable similarity between these phenomena and those happening with compressible acoustic modes. The relevance and possible importance of these phenomena for different types of astrophysical plasma flow patterns with kinematic complexity are discussed. In particular, we argue that these physical processes may account for the persistent appearance of plasma oscillations in the heliosphere and interstellar plasma flows. In particular, we believe that the kinematically complex motion of plasma may naturally lead to the asymptotically persistent appearance of Langmuir modes that are born, grown, fed, sustained and maintained by these flows.

Efficient forecasting of solar flares is of significant importance for better risk prevention. Currently, there is relatively rare research on multi/four-classification of flares, and the influence of the number of time steps and data feature dimensions on the prediction performance of multi-class models has not been considered. In this study, we utilize the Space-weather HMI Active Region Patch (SHARP) data to develop two categories of models for multiclass flare prediction within 24 hr, including direct output four-classification models and four-classification models using a cascading scheme. The former encompasses Random Forest (RF) model, Long Short-Term Memory (LSTM) model, and Bidirectional LSTM (BLSTM) model, while the latter includes BLSTM Cascade (BLSTM-C) model and BLSTM Cascade with Attention Mechanism (BLSTM-C-A) model. These two categories of models are employed to contrast the impact of different numbers of time steps and the predictive performance in solar flare multi/four-classification. Additionally, we conduct, for the first time, feature importance analysis for multi/four-classification solar flare prediction using deep learning models. The main results are as follows: (1) As the number of time steps increases, the True Skill Statistic (TSS) scores of the four deep learning models improve, showing an overall upward trend in predictive performance. The models achieve their optimal performance when the number of time steps reaches 120. (2) Among the direct output four-class models, deep learning models (LSTM and BLSTM) outperform traditional machine learning model (RF). In both multi-class and binary-class predictions using deep learning, the BLSTM-C model performs better than other deep learning models (LSTM, BLSTM, and BLSTM-C-A). (3) In the feature importance analysis, the top-ranked important features include SAVNCPP and R_VALUE, while the least important features include SHRGT45 and MEANPOT.

In this paper, we report the detailed observation of the drift subpulse behavior of PSR J1514–4834 at a central frequency of 1369 MHz using the Parkes 64-m radio telescope. We have found that individual pulses of this pulsar exhibit distinct modulation behaviors for different profile components. The leading and middle components display periodic amplitude modulation with a period of (mathrm{P}_{3}=37.5pm 0.8, mathrm{P}), and a drifting sub-pulse phenomenon is detected in the phase region of trailing component with the measured drifting periods (mathrm{P}_{2}=7.0pm 0.4,mathrm{P}) and (mathrm{P}_{3}=37.5pm 0.8, mathrm{P}). Additionally, it was observed that the leading and trailing components of the pulsar have a clear correlation, the middle and trailing components have a clear anti-correlation, and there is no apparent correlation between the leading and middle components. Moreover, this pulsar deviates from the range of most amplitude-modulated pulsars in the (dot{mathrm{E}}-mathrm{P}_{3}) diagram, but it still falls within the category of subpulse drifting. PSR J1514–4834 exhibits periodic emission modulation and sub-pulse drifting simultaneously in different profile components, which is difficult to understand with the traditional carousel model. Our observational results will provide new observation evidence for theoretical studies of single-pulse emission mechanisms in pulsars.

Phenomenological relations linking thermodynamics and kinetic theory in condensed matter have been empirically verified in numerous systems, yet their theoretical derivation from first principles remains an open question. One such relation is the so-called “excess-entropy scaling”. Do N-body gravitational systems exhibit a similar relation? We provide an affirmative response to this question, albeit with some limitations. Our analysis relies on a well-established thermodynamical quasi-equilibrium model for the cosmological N-body problem, along with an appropriate diffusion model for gravitational interactions. By identifying a scaling region, we were able to estimate diffusion coefficients directly from observational two-particle correlation functions or counts-in-cells distributions in large-scale structures. Intriguingly, the phenomenon of excess-entropy scaling manifests primarily during the nonlinear, asymptotic clustering phase preceding a potential thermodynamic phase transition.

The hyperfine transition of atomic hydrogen at a wavelength of about 21 cm is an essential tool for studies of interstellar gas. It has been argued that also fine-structure transitions of hydrogen could be detected in astronomical sources. Our aim is to detect the fine-structure transition (2^{2}P_{3/2}-2^{2}S_{1/2}) of hydrogen at ∼10 GHz in the radio spectrum of the Sun, with a spectral resolution which enables a detailed study of the line profile. The Sun was observed with the 13.7 m radio telescope at the Metsähovi Radio Observatory, in Finland. We detect emission from two of the three hyperfine components of the transition. The width of the components is ∼15 MHz, much less than the expected natural line width of ∼100 MHz (broadened solely by the uncertainty principle). At red-shifted Doppler velocities, the lines show enhanced emission and possibly self-absorption. If the absorption happens at the chromosphere, our observations challenge the traditional view that chromospheric temperature increases gradually towards higher altitudes. Our unconventional results have to be confirmed by further observations. This transition would be the only known spectral line in the Sun at radio frequencies.