A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification

Today is the age of Data-Driven astronomy, with sky surveys generating large amounts of data, and many new ones are lining up, such as the large synoptic survey telescope (LSST). One of the key motives of such surveys is to classify objects as stars or galaxies. However, manual classification can not be done for petabytes of data and large intra-class variation, which raises the need for an automated and robust classification model. Recently, several research works have been developed to help astronomers by automatically classifying the galaxies (Soumagnac et al., 2015; Ba Alawi & Al-Roainy, 2021; Chaini et al., 2022; Kim & Brunner, 2016; Garg et al., 2022). However, these models perform well but are complex. In contrast to the existing work, due to the complexity of our star-galaxy system, in this research, we have proposed the development of a classification approach utilizing a sector-based division of the sky. The prime motivation for such division can be seen in Figure 1, reflecting the variation present in different sectors and difficulties in classification. By utilizing these differences, we have developed a star-galaxy classification system that surpasses existing algorithms and yields a low computational cost.