Most stars in the universe exist in binary or higher-order multiple star systems. Understanding the properties of binary star population parameters is, therefore, crucial for advancements in many areas of astronomy. However, selection effects and the time-consuming nature of classical observational methods used to study binary systems are reflected in the incompleteness of studies attempting to describe these parameters. In this thesis, we test a new method that makes use of extensive databases of astrophotometric data from modern sky surveys such as 2MASS and the Gaia mission. We create a synthetic model of a galaxy with a population of binary stars and then compare the color-absolute magnitude diagram (CAMD) of this galaxy with observational data. Using Approximate Bayesian Computation (ABC), we attempt to find the binary star population parameters for which the CAMD of the synthetic galaxy best fits the observational data. In the first part of the thesis, we present the broader background and theoretical foundation. We then test the method by sampling the parameters of the binary star population while using a synthetic galaxy as “observational” data. This yields very promising results. The first signs of problems with the method arise when we add observational effects to our synthetic galaxy. However, sampling the population parameters of binary stars by comparing observational data to our synthetic galaxy highlights the need for further model development and data analysis. Nevertheless, almost all of the inferred parameter values are consistent with those reported in the literature. In the concluding part of the thesis, we discuss the reasons for the problems and consider possible solutions. We conclude that the method is conceptually promising, but for further advancement, it is necessary to model the diversity of binary star population parameters for systems with different primary star masses.
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