A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification
This addresses efficient and precise astronomical analysis, especially for real-time observational settings, but appears incremental as it builds on existing CNN approaches.
The paper tackled star-galaxy classification using a sector-based method with SDSS-DR18 data, achieving state-of-the-art performance through a dedicated CNN.
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.