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

arXiv:2404.01049v1 Announce Type: cross
Abstract: 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.

abstract algorithm art arxiv astro-ph.im classification cnn convolutional neural network cs.lg data digital galaxy methodology network neural network novel paper patterns performance sector star state survey survey data type