# Star-galaxy Classification Using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1608.04369 · HTML: https://mlsanity.com/paper/1608.04369/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2016.

## Built on by (most cited first, 7 of 7)

- [What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction](https://mlsanity.com/paper/2203.07390/) (2022)
- [Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques](https://mlsanity.com/paper/2107.00385/) (2021)
- [deep-REMAP: Parameterization of Stellar Spectra Using Regularized Multi-Task Learning](https://mlsanity.com/paper/2311.03738/) (2023)
- [Morphological Classification of Galaxies Using SpinalNet](https://mlsanity.com/paper/2305.01873/) (2023)
- [A Novel Sector-Based Algorithm for an Optimized Star-Galaxy Classification](https://mlsanity.com/paper/2404.01049/) (2024)
- [Photometry of Saturated Stars with Neural Networks](https://mlsanity.com/paper/2404.15405/) (2024)
- [Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation](https://mlsanity.com/paper/2506.16233/) (2025)

## Abstract (arXiv)

Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine to automatically learn the features directly from data, minimizing the need for input from human experts. We present a star-galaxy classification framework that uses deep convolutional neural networks (ConvNets) directly on the reduced, calibrated pixel values. Using data from the Sloan Digital Sky Survey (SDSS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS), we demonstrate that ConvNets are able to produce accurate and well-calibrated probabilistic classifications that are competitive with conventional machine learning techniques. Future advances in deep learning may bring more success with current and forthcoming photometric surveys, such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), because deep neural networks require very little, manual feature engineering.