The FIRST Classifier: Compact and Extended Radio Galaxy Classification using Deep Convolutional Neural Networks

TL;DR

The paper introduces the FIRST Classifier, a deep learning system that accurately classifies radio galaxy morphologies, including compact, bent, FRI, and FRII types, to aid cosmological research with upcoming radio surveys.

Contribution

It presents a novel deep convolutional neural network model for automated radio galaxy classification with high accuracy, tailored for large-scale survey data.

Findings

Achieved 97% overall accuracy in classification

High recall rates for all classes, especially 100% for FRI

Effective for single sources and lists of sources

Abstract

Upcoming surveys with new radio observatories such as the Square Kilometer Array will generate a wealth of imaging data containing large numbers of radio galaxies. Different classes of radio galaxies can be used as tracers of the cosmic environment, including the dark matter density field, to address key cosmological questions. Classifying these galaxies based on morphology is thus an important step toward achieving the science goals of next generation radio surveys. Radio galaxies have been traditionally been classified as Fanaroff-Riley (FR) I and II, although some exhibit more complex 'bent' morphologies arising from environmental factors or intrinsic properties. In this work we present the FIRST Classifier, an on-line system for automated classification of Compact and Extended radio sources. We developed the FIRST Classifier based on a trained Deep Convolutional Neural Network Model to automate the morphological classification of compact and extended radio sources observed in the FIRST radio survey. Our model achieved an overall accuracy of 97% and a recall of 98%, 100%, 98% and 93% for Compact, BENT, FRI and FRII galaxies respectively. The current version of the FIRST classifier is able to predict the morphological class for a single source or for a list of sources as Compact or Extended (FRI, FRII and BENT).