Feature guided training and rotational standardization for the morphological classification of radio galaxies. Issue 1 (3rd April 2023)

• Record Type: Journal Article
• Authors: Brand, Kevin; Grobler, Trienko L; Kleynhans, Waldo; Vaccari, Mattia; Prescott, Matthew; Becker, Burger
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 522:Issue 1(2023)
• Page Start: 292
• Page End: 311
• Publication Date: 2023-04-03
• Subjects: radio continuum: galaxies -- methods: data analysis -- methods: statistical -- techniques: image processing; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stad989
• Languages: English
• ISSNs: 0035-8711

Abstract

ABSTRACT: State-of-the-art radio observatories produce large amounts of data which can be used to study the properties of radio galaxies. However, with this rapid increase in data volume, it has become unrealistic to manually process all of the incoming data, which in turn led to the development of automated approaches for data processing tasks, such as morphological classification. Deep learning plays a crucial role in this automation process and it has been shown that convolutional neural networks (CNNs) can deliver good performance in the morphological classification of radio galaxies. This paper investigates two adaptations to the application of these CNNs for radio galaxy classification. The first adaptation consists of using principal component analysis (PCA) during pre-processing to align the galaxies' principal components with the axes of the coordinate system, which will normalize the orientation of the galaxies. This adaptation led to a significant improvement in the classification accuracy of the CNNs and decreased the average time required to train the models. The second adaptation consists of guiding the CNN to look for specific features within the samples in an attempt to utilize domain knowledge to improve the training process. It was found that this adaptation generally leads to a stabler training process and in certain instances reduced overfitting within the network, as well as the number of epochs required for training.