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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. [ABSTRACT FROM AUTHOR]

High-noise astronomical-image denoising has always been a research hotspot in deep space exploration. Compressive sensing (CS) is an advanced technology used for high-dimensional signal processing. It is useful for processing high-resolution astronomical images. To obtain high-quality astronomical images, a CS spatially adaptive total variation iterative (CSSATVI) method is proposed herein. In this method, a curvelet transform based on an adaptive curvelet soft thresholding operator is proposed to adaptively remove hidden noise information in the process of image sparse representation, and a novel CS denoising reconstruction model proposed is used to deeply mine the texture, edge and other detailed information. Moreover, a novel reconstruction strategy is proposed for preserving detailed image information in the iterative reconstruction process to obtain high-quality astronomical images. Simulation results indicated that the proposed CSSATVI method can quickly reconstruct a high-quality astronomical image and preserve a large amount of astronomical image details; thus, it can be effectively applied in deep space exploration. [ABSTRACT FROM AUTHOR]

One of the most valuable regions for telecommunication and space science is the geostationary earth orbit (GEO). On 27 November 2019, Egypt successfully launched its telecommunications satellite TIBA-1 into space. This paper aims to effectively identify and concurrently track this satellite from our Kottamia observatory station, National Research Institute of Astronomy and Geophysics (NRIAG) - Egypt. The images of the satellite are processed by the Apex II astronomical image processing package to obtain optical measurements, the apparent magnitude, and the phase angle. The initial orbit of the TIBA-1 satellite is determined using angle-only optical measurements for 4 months. The Double-r iteration method is used to determine the state distance vectors for orbital elements under different perturbation forces. Then, the results are compared with the positions predicted using the North American Aerospace Defense Command (NORAD) reference catalog. [ABSTRACT FROM AUTHOR]

We are pursuing a project to build a database of phase calibration sources suitable for Giant Metrewave Radio Telescope (GMRT). Here we present the first release of 45 low frequency calibration sources at 235 MHz and 610 MHz. These calibration sources are broadly divided into quasars, radio galaxies and unidentified sources. We provide their flux densities, models for calibration sources, ( u, v) plots, final deconvolved restored maps and clean-component lists/files for use in the Astronomical Image Processing System ( aips) and the Common Astronomy Software Applications ( casa). We also assign a quality factor to each of the calibration sources. These data products are made available online through the GMRT observatory website. In addition we find that (i) these 45 low frequency calibration sources are uniformly distributed in the sky and future efforts to increase the size of the database should populate the sky further, (ii) spectra of these calibration sources are about equally divided between straight, curved and complex shapes, (iii) quasars tend to exhibit flatter radio spectra as compared to the radio galaxies or the unidentified sources, (iv) quasars are also known to be radio variable and hence possibly show complex spectra more frequently, and (v) radio galaxies tend to have steeper spectra, which are possibly due to the large redshifts of distant galaxies causing the shift of spectrum to lower frequencies. [ABSTRACT FROM AUTHOR]

Ground level enhancements (GLEs) of cosmic-ray intensity occur, on average, once a year. Because they are rare, studying the solar sources of GLEs is especially important to approach understanding their origin. The SOL2001-12-26 eruptive-flare event responsible for GLE63 seems to be challenging in some aspects. Deficient observations limited our understanding of it. Analysis of additional observations found for this event provided new results that shed light on the flare configuration and evolution. This article addresses the observations of this flare with the Siberian Solar Radio Telescope (SSRT). Taking advantage of its instrumental characteristics, we analyze the detailed SSRT observations of a major long-duration flare at 5.7 GHz without cleaning the images. The analysis confirms that the source of GLE63 was associated with an event in active region 9742 that comprised two flares. The first flare (04:30 - 05:03 UT) reached a GOES importance of about M1.6. Two microwave sources were observed, whose brightness temperatures at 5.7 GHz exceeded 10 MK. The main flare, up to an importance of M7.1, started at 05:04 UT and occurred in strong magnetic fields. The observed microwave sources reached a brightness temperature of about 250 MK. They were not static. After appearing on the weaker-field periphery of the active region, the microwave sources moved toward each other nearly along the magnetic neutral line, approaching the stronger-field core of the active region, and then moved away from the neutral line like expanding ribbons. These motions rule out an association of the non-thermal microwave sources with a single flaring loop. [ABSTRACT FROM AUTHOR]

An application of active contours without edges is presented as an efficient and effective means of extracting and characterizing coronal holes. Coronal holes are regions of low-density plasma on the Sun with open magnetic field lines. The detection and characterization of these regions is important for testing theories of their formation and evolution, and also from a space weather perspective because they are the source of the fast solar wind. Coronal holes are detected in full-disk extreme ultraviolet (EUV) images of the corona obtained with the Solar Dynamics Observatory Atmospheric Imaging Assembly (SDO/AIA). The proposed method detects coronal boundaries without determining any fixed intensity value in the data. Instead, the active contour segmentation employs an energy-minimization in which coronal holes are assumed to have more homogeneous intensities than the surrounding active regions and quiet Sun. The segmented coronal holes tend to correspond to unipolar magnetic regions, are consistent with concurrent solar wind observations, and qualitatively match the coronal holes segmented by other methods. The means to identify a coronal hole without specifying a final intensity threshold may allow this algorithm to be more robust across multiple datasets, regardless of data type, resolution, and quality. [ABSTRACT FROM AUTHOR]

Detection and localization of astronomical objects are two of the most fundamental topics in astronomical science where localization uses detection results. Object localization is based on modeling of point spread function and estimation of its parameters. Commonly used models as Gauss or Moffat in objects localization provide good approximation of analyzed objects but cannot be sufficient in the case of exact applications such as object energy estimation. Thus the use of sophisticated models is upon the place. One of the key roles plays also the way of the objective function estimation. The least square method is often used, but it expects data with normal distribution, thus there is a question of a maximum likelihood method application. Another important factor of presented problem is choice of the right optimization method. Classical methods for objective function minimization usually require a good initial estimate for all parameters and differentiation of the objective function with respect to model parameters. The results indicated that stochastic methods such as simulated annealing or harmony search achieved better results than the classical optimization methods. [ABSTRACT FROM AUTHOR]

Recent lossless still image compression formats are powerful tools for compression of all kind of common images (pictures, text, schemes, etc.). Generally, the performance of a compression algorithm depends on its ability to anticipate the image function of the processed image. In other words, a compression algorithm to be successful, it has to take perfectly the advantage of coded image properties. Astronomical data form a special class of images and they have, among general image properties, also some specific characteristics which are unique. If a new coder is able to correctly use the knowledge of these special properties it should lead to its superior performance on this specific class of images at least in terms of the compression ratio. In this work, the novel lossless astronomical image data compression method will be presented. The achievable compression ratio of this new coder will be compared to theoretical lossless compression limit and also to the recent compression standards of the astronomy and general multimedia. [ABSTRACT FROM AUTHOR]

This paper presents an automated algorithm to extract dynamical features, such as stability constraints, from phase space maps. The functional representation of these constraints allows their inclusion in optimization problems and thus expands the use of dynamical tools in space mission design. The challenge to autonomously detect the regions of interest in stability maps is discussed through utilizing image processing algorithms to cluster map data. Additionally, to use the detected regions, both discrete and smooth functional representations are studied. Based on similar clustering techniques that have been considered in extracting and representing features of phase space maps, we proposed an adaptively map generation algorithm. It creates a nonuniform grid of points on a map which is denser near the boundaries of the regions of interest. Both representation and map generation algorithms provide significant performance enhancements in phase space analysis. All these techniques are illustrated on examples of stability maps in small body dynamics. [ABSTRACT FROM AUTHOR]

The possibility of an automatic search for galaxy clusters in WISE infrared images using SDSS optical data is discussed. Preliminary results of applying such a procedure to fields of the 400 square degree (400d) X-ray galaxy cluster survey performed previously based on ROSAT pointing data are presented. We show that rich galaxy clusters can be detected in the WISE infrared survey both at moderate redshifts and up to high redshifts, z ≈ 1-2. These data can be used to identify clusters detected in X-rays or via the Sunyaev-Zeldovich effect. [ABSTRACT FROM AUTHOR]

Image restoration is an ill-posed problem that requires regularization to solve. Many existing regularization terms in the literature are the convex function. However, nonconvex nonsmooth regularization has advantages over convex regularization for restoring images, but its practical interest used to be limited by the difficulty of the computational stage which requires a nonconvex nonsmooth minimization. In this paper, an adaptive nonconvex nonsmooth regularization is proposed for image restoration by using the spatial information indicator. Moreover, an efficient numerical algorithm for solving the resulting minimization problem is provided by applying the variable splitting and the penalty techniques. Finally, its advantages are shown in deblurring edges and restoring fines of image simultaneously in experiments. [ABSTRACT FROM AUTHOR]

Soil heat flux is an important input component of surface energy balance. Estimates of soil heat flux were made in the year 2008 using soil temperature data at Astronomical Observatory, Thiruvananthapuram, south Kerala. Hourly values of soil heat flux from 00 to 24 LST are presented for selected days typical of the winter, pre-monsoon, SW monsoon and NE monsoon seasons. The diurnal variation is characterized by a cross-over from negative to positive values at 0700 h, occurrence of maximum around noon and return to negative values in the late evening. The energy storage term for the soil layer 0-0.05 m is calculated and the ground heat flux G is estimated in all seasons. Daytime surface energy balance at the surface on wet and dry seasons is investigated. The average Bowen's ratio during the wet and dry seasons were 0.541 and 0.515, respectively indicating that considerable evaporation takes place at the surface. The separate energy balance components were examined and the mean surface energy balance closure was found to be 0.742 and 0.795 for wet and dry seasons, respectively. When a new method that accounts for both soil thermal conduction and soil thermal convection was adopted to calculate the surface heat flux, the energy balance closure was found to be improved. Thus on the land surface under study, the soil vertical water movement is significant. [ABSTRACT FROM AUTHOR]

We have implemented a multiscale vision model based on the wavelet transform to analyse field astronomical images. The discrete transform is performed by the à trous algorithm. The vision model is based on the notion of significant structures. We identify the pixels of the associated wavelet transform space (WTS) with the objects. For each scale a region labelling is carried out. An interscale connectivity graph is then established. In accordance with some rules that permit false detections to be removed, the objects and their sub-objects are identified. They define respectively trees and sub-trees in the graph. In this way, the identification of the WTS pixels of the tree related to a given object leads to the reconstruction of its image by the conjugate gradient method. The model has been tested successfully on simulated images of stars and galaxies which allow us to show the capabilities of the detection and restoration procedures of the model. Finally, tests on real images show that one can analyse complex structures better than with classical astronomical vision models. [ABSTRACT FROM AUTHOR]