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

TL;DR

This paper presents a CNN-based model that classifies radio galaxies into four morphological classes with high accuracy using data augmentation techniques, demonstrating effective application of machine learning in astronomy.

Contribution

The work introduces a novel CNN architecture combined with specific data augmentation methods for accurate radio galaxy classification.

Findings

Achieved 96% precision, recall, and F1 score on test data.

Rotation, flips, and brightness increase improved model performance.

Model effectively distinguishes four radio galaxy classes.

Abstract

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