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

ANUMANCHI AGASTYA SAI RAM LIKHIT, Divyansh Tripathi, Akshay Agarwal

Keywords: Star-Galaxy Classification, Sloan Digital Sky Survey, Large Synoptic Survey Telescope

TL;DR: Proposed a novel and state-of-the-art star galaxy classifier

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.

Submission Number: 58