Spectral observations of geostationary satellites at the Assy-Turgen Observatory in Kazakhstan

2024

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.32014/2023.2518-1483.308
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

Spectral and photometric observations are a unique tool for studying the Resident Space Objects (RSO). Spectral observations are of particular value. They are precious sources of information in identifying and classifying RSO by their spectral features and studying the variation of these features caused by the influence of space weathering. The paper we present shows the results of the geostationary satellite (GEO) reflection spectra analysis. We use spectral observations to analyze reflective spectra and relevant parameters to develop the methodology of satellite identification. Spectral observations were obtained on the spectrograph mounted on a 1.5-meter telescope AZT-20 of the Assy-Turgen Observatory. Photometric characteristics derived from spectral observations show signs of dependence on the platform of a satellite and its service life in orbit. Comparison of observed and model reflective spectra indicates the possibility of identifying the satellite shape and its composite materials. The results of the work will be of interest for the study of space weathering on materials, will allow us to better understand the situation in GEO orbits, and refine the methods for near-miss events prediction, taking into account the geometry of objects and the properties of their surfaces.