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

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).

arXiv comment9 pages, 10 figures. Accepted for publication in MNRAS

References in corpus (23)

• The Sloan Digital Sky Survey: Technical Summary
• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• Rotation-invariant convolutional neural networks for galaxy morphology prediction
• On the fundamental dichotomy in the local radio-AGN population: accretion, evolution, and host galaxy properties
• Science with ASKAP - the Australian Square Kilometre Array Pathfinder
• The Last of FIRST: The Final Catalog and Source Identifications
• Science With The Australian Square Kilometre Array Pathfinder
• Classifying Radio Galaxies with Convolutional Neural Network
• Radio Galaxy Zoo: host galaxies and radio morphologies derived from visual inspection
• FRII{\sl{CAT}}: A FIRST catalog of FR~II radio galaxies
• FRICAT: A FIRST catalog of FRI radio galaxies
• Galaxies, Human Eyes and Artificial Neural Networks
• Radio Galaxy Zoo: Compact and extended radio source classification with deep learning
• Morphological Annotations for Groups in the FIRST Database
• The Environments of a Complete, Moderate-Redshift Sample of FIRST Bent-Double Radio Sources
• FIRST Bent-Double Radio Sources: Tracers of High-Redshift Clusters
• The origin of wide-angle tailed radio galaxies
• The ASKAP/EMU Source Finding Data Challenge
• Discovery of a High-Redshift (z=0.96) Cluster of Galaxies Using a FIRST Wide-Angle Tailed Radio Source
• The CoNFIG Catalogue - II. Comparison of Space Densities in the FR Dichotomy
• The Merger Environment of the WAT Hosting Cluster Abell 562
• Chandra Observation of the Cluster Environment of a WAT Radio Source in Abell 1446
• Replica theory for learning curves for Gaussian processes on random graphs

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