Identification of 4876 Bent-Tail Radio Galaxies in the FIRST Survey Using Deep Learning Combined with Visual Inspection

TL;DR

This paper presents a large catalog of 4876 bent-tail radio galaxies identified through a deep learning approach combined with visual inspection, significantly expanding known samples and providing insights into their properties and cluster associations.

Contribution

The study introduces a novel combined deep learning and visual inspection method to identify BTRGs, resulting in the largest catalog to date with 3871 new discoveries.

Findings

Catalog includes 4876 BTRGs with 3871 new detections.

Optical counterparts identified for 4193 BTRGs.

1825 BTRGs are associated with galaxy clusters.

Abstract

Bent-tail radio galaxies (BTRGs) are characterized by bent radio lobes. This unique shape is mainly caused by the movement of the galaxy within a cluster, during which the radio jets are deflected by the intra-cluster medium. A combined method, which involves a deep learning-based radio source finder along with visual inspection, has been utilized to search for BTRGs from the Faint Images of the Radio Sky at Twenty-centimeters survey images. Consequently, a catalog of 4876 BTRGs has been constructed, among which 3871 are newly discovered. Based on the classification scheme of the opening angle between the two jets of the galaxy, BTRGs are typically classified as either wide-angle-tail (WAT) sources or narrow-angle-tail (NAT) sources. Our catalog comprises 4424 WATs and 652 NATs. Among these, optical counterparts are identified for 4193 BTRGs. This catalog covers luminosities in the range of $1.91\times10^{20} \leq L_{\rm 1.4\,GHz} \leq 1.45\times10^{28}$ ${\rm W\,Hz^{-1}}$ and redshifts from $z = 0.0023$ to $z = 3.43$. Various physical properties of these BTRGs and their statistics are presented. Particularly, by the nearest neighbor method, we found that 1825 BTRGs in this catalog belong to galaxy clusters reported in literature.