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The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state prediction efforts. In this paper, a spatial object optical characterization model based on Augmented Implicit Neural Representations (AINRs) is proposed. This model utilizes a neural implicit function to delineate the relationship between the geometric observation model and the apparent magnitude arising from sunlight reflected off the object's surface. Combining the dual advantages of data-driven and physical-driven, a novel pre-training procedure method based on transfer learning is designed. Taking omnidirectional angle simulation data as the basic training dataset and further introducing it with real observational data from ground stations, the Multi-Layer Perceptron (MLP) parameters of the model undergo constant refinement. Pre-fitting experiments on the newly developed S−net, R−net, and F−net models are conducted with a quantitative analysis of errors and a comparative assessment of evaluation indexes. The experiment demonstrates that the proposed F−net model consistently maintains a prediction error for satellite surface magnitude values within 0.2 m V , outperforming the other two models. Additionally, preliminary accomplishment of component-level recognition has been achieved, offering a potent analytical tool for on-orbit services. [ABSTRACT FROM AUTHOR]

This is a way to look at a photos of stars and make sense to get a value that shows the closest star: in the vicinity: It uses star's apparent magnitudes to get theses inverse magnitudes and to get a nearby star of the largest ratio ; of the inverse apparent magnitude divided by the distance. Now the beginning reference star obeys this ratio . For the greater the distance the lower the value. And we want the highest value . The greater the brightness enlarges and the greater the distance makes the value lessons. After trying to use aspects of General Relativity's Tensor Calculus, l found l could not find my formulas to fit the data . In an original way l used the inverse Apparent Magnitudes in Astronomical to get a consistant formula that is data proven with general ideas from Global analysis. It works with Star charts/photos. Before l even tried using the inverse of the determinates of linear algebra. But my attempts were to no avail. Therefore l became inspired by basic formulas in General Relativity but that did not work. Using Global Analysis in an new l succeeded. Keywords Using my new formula l can obtain the distance between a reference star, using its inverse apparent magnitude, l obtain the distance to from another star using certain restrictions which the measurement in centimeters to this other star from its reference star nearby. [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]

In this paper, we considered the study of Friedmann–Robertson–Walker (FRW) model in the framework of f (Q , T) gravity, an extension of symmetric teleparallel gravity, recently defined by Xu et al. [ f (Q , T) gravity, Eur. Phys. J. C 79 (2019) 708]. The nonlinear model f (Q , T) = − α Q − β T 2 , where α > 0 and β > 0 are constants, is taken into account. The equation of state of perfect fluid is assumed and 31 points of Hubble data are used to constrain the value of model parameter. To explore the evolution of the universe, the numerical solutions of cosmological implications, such as Hubble parameter, deceleration parameter, apparent magnitude and luminosity distance, are determined and the energy conditions are examined. The theoretical results of Hubble parameter are compared with Λ CDM model. Further, 57 Supernova data (42 from Supernova cosmology project and 15 from Calán/Tolono supernova survey) are also used to have consistent results of apparent magnitude and luminosity distance. [ABSTRACT FROM AUTHOR]

In the present paper, the work of Moreas et al. [P. H. R. S. Moraes, G. Ribeiro and R. A. C. Correa, A transition from a decelerated to an accelerated phase of the universe expansion from the simplest non-trivial polynomial function of T in the f (R , T) formalism, Astrophys. Space Sci.361 (2016) 227–231] is extended to study the FRW model in f (R , T) gravity. The expressions for deceleration and Hubble parameters are determined in terms of redshift. The age of the universe is established using 4 2 high-redshift type Ia supernovae data from the Supernova cosmology project and 15 low-redshift type Ia supernovae data from the Calán/Tolono Supernova survey [S. Permutter et al., Measurements of Omega and Lambda from 42 High-Redshift Supernovae, Astrophys. J.517 (1999) 565–585]. For these redshifts, the data of observed apparent magnitude and luminosity distance are used for the comparison with the obtained theoretical values. [ABSTRACT FROM AUTHOR]

In this paper, we present a formulation for the computation of the SNR of an image system designed for astronomical star observations by combining research results from different disciplines such as physics, astronomy, optics, and electronics. Starting from the energy emitted by the stars and their catalogued magnitudes, we formulate the amount of electrons formed in the pixels of an image sensor using the camera parameters and then compute the quality of the signal in terms of SNR. We provide formulations for both space bound and earth bound observations of stars and present example numerical data for a camera and a telescope system. The formulation given here, although not novel if considered separately in its components, combines the information from different disciplines in an effort to present a complete resource that relates the full image chain from the source to the finally formed image in the hope that it will be a self-contained reference for other researchers working on astronomical camera systems. [ABSTRACT FROM PUBLISHER]