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

arXiv: https://arxiv.org/abs/2404.01049 · HTML: https://mlsanity.com/paper/2404.01049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [Star-galaxy Classification Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1608.04369/) (2016)
- [Photometric identification of compact galaxies, stars and quasars using multiple neural networks](https://mlsanity.com/paper/2211.08388/) (2022)

## Abstract (arXiv)

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.