| dc.description.abstract | Precise assessment of binary star formation remains uncertain, yet it wields substantial influence on our understanding of galaxies' concealed dark matter. As it stands now, there are few methods to efficiently detect binary star systems in stellar groups, one of which is the Binary Information from Open Clusters Using Spectral Energy Distributions (BINOCS) fitting algorithm. With the launch of the European Space Agency Gaia spacecraft and its third data release, spectral data is more widely accessible for use. This project?s endeavor involves enhancing the BINOCS software with Gaia spectroscopic data, empowering the identification of binary stars among one million celestial entities. We modified the BINOCS software to perform online data collection, have a built-in Graphical User Interface (GUI), updated for Python3, and implement modern software engineering principles while retaining algorithm accuracy. The intention of this project serves as an interdisciplinary project between my major in computer science and minor in astronomy, where I can leverage my knowledge in computer science and apply it to a real-world problem. | |
