# Morphological classification of compact and extended radio galaxies using convolutional neural networks and data augmentation techniques

arXiv: https://arxiv.org/abs/2107.00385 · HTML: https://mlsanity.com/paper/2107.00385/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 50 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data](https://mlsanity.com/paper/1711.03121/) (2017)
- [Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit](https://mlsanity.com/paper/1702.00403/) (2017)
- [Using transfer learning to detect galaxy mergers](https://mlsanity.com/paper/1805.10289/) (2018)
- [Star-galaxy Classification Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1608.04369/) (2016)

## Built on by (most cited first, 3 of 3)

- [Morphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods](https://mlsanity.com/paper/2304.12729/) (2023)
- [Advances on the classification of radio image cubes](https://mlsanity.com/paper/2305.03435/) (2023)
- [Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak Labels](https://mlsanity.com/paper/2308.05166/) (2023)

## Abstract (arXiv)

Machine learning techniques have been increasingly used in astronomical applications and have proven to successfully classify objects in image data with high accuracy. The current work uses archival data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) to classify radio galaxies into four classes: Fanaroff-Riley Class I (FRI), Fanaroff-Riley Class II (FRII), Bent-Tailed (BENT), and Compact (COMPT). The model presented in this work is based on Convolutional Neural Networks (CNNs). The proposed architecture comprises three parallel blocks of convolutional layers combined and processed for final classification by two feed-forward layers. Our model classified selected classes of radio galaxy sources on an independent testing subset with an average of 96% for precision, recall, and F1 score. The best selected augmentation techniques were rotations, horizontal or vertical flips, and increase of brightness. Shifts, zoom and decrease of brightness worsened the performance of the model. The current results show that model developed in this work is able to identify different morphological classes of radio galaxies with a high efficiency and performance