  This paper introduces a novel sector-based methodology for star-galaxy
classification, leveraging the latest Sloan Digital Sky Survey data
(SDSS-DR18). By strategically segmenting the sky into sectors aligned with SDSS
observational patterns and employing a dedicated convolutional neural network
(CNN), we achieve state-of-the-art performance for star galaxy classification.
Our preliminary results demonstrate a promising pathway for efficient and
precise astronomical analysis, especially in real-time observational settings.
