Morphological-based Classifications of Radio Galaxies Using Supervised Machine-learning Methods Associated with Image Moments

Abstract

With the advent of new high-resolution instruments for detecting and studying radio galaxies with different morphologies, the need for the use of automatic classification methods is undeniable. Here, we focused on the morphological-based classification of radio galaxies known as Fanaroff–Riley (FR) type I and type II via supervised machine-learning approaches. Galaxy images with a resolution of 5″ at 1.4 GHz provided by the Faint Images of the Radio Sky at Twenty centimeters (FIRST) survey are employed. The radial Zernike polynomials are exploited to extract image moments. Then, the rotation, translation, and scale-invariant moments of images are used to form a training set (65% of the radio galaxy sample) and a test set (the remaining 35%). The classes of the test set are determined by two classifiers: a support vector machine and a twin support vector machine (TWSVM). In addition the genetic algorithm is employed to optimize the length of moment series and to find the optimum values of the parameters of the classifiers. The labels of outputs are compared to identify the best performance classifier. To do this the confidence level of classifications is estimated by four different metrics: precision, recall, F1 score, and accuracy. All tests show that implementing TWSVM with the radial basis function as a kernel achieves a confidence level of more than 95% in grouping galaxies.

The Planck clusters in the LOFAR sky

“…It is clear that the strategy used in this work needs further development to properly assess significantly larger cluster samples or to eventually conduct a blind search for cluster emission. In this respect, machine-based techniques represent an appealing solution to classify the emission in large object samples (e.g., Aniyan & Thorat 2017;Alhassan et al 2018;Domínguez Sánchez et al 2018;Lukic et al 2018Lukic et al , 2019Sadeghi et al 2021;Vavilova et al 2021). As part of this work, we have made public all our images and the detailed results of our decision-tree-based classification, which we hope can provide a good training set for algorithms that attempt either the full classification or to aid the automation at specific intersections in a decision-tree-type approach.…”

Modularized Filter-Wrapper Feature Selection Scheme for Finding Optimal Image Moments in Maps of Radio Galaxies

“…It was shown that the best order of the Zernike is 𝑝 𝑢𝑝 = 31, which has the least reconstruction error [16]. The reconstructed image is very similar with the original one [7,16]. ZMs give general and detailed properties of a shape from an image.…”

“…In the age of data, receiving a vast amount of data with different dimensions in each branch of science necessitates automatic techniques for feature subset selection (FSS). In astronomy, so many classification methods were developed to predict the class of received data sets (for radio galaxy classification, refer to [7] and references therein). But, to reduce the curse of dimensionality leading to increase both the efficiency of the algorithm and the flexibility of the classification method, it seems that the FSS algorithm must be developed and used in the interface of received data and classification approaches (See Section 2).…”

Morphological-based Analyses for Parameterizing Symmetry in Radio Galaxies

“…As new high-resolution and sensitive radio surveys have been arriving, the need for revisiting these morphological classifications is inevitable. In addition, machine-learning techniques (e.g., Aniyan & Thorat 2017;Lukic et al 2018;An et al 2018;Tang et al 2019;Sadeghi et al 2021) have been applied to introduce automatic methods for morphologicalbased classification of radio galaxies. In terms of machinelearning algorithms and image analysis which are applied to LOFAR, a composition of novel models is exploited to involve optical features of host galaxies and morphological parameters in their classifications (Alegre et al 2022;Barkus et al 2022).…”