A Catalog of 971 FR-I Radio Galaxies from the FIRST Survey via Hybrid Deep Learning and Ridgeline Flux Density Distribution Analysis
Baoqiang Lao, Xiaolong Yang, Wenjun Xiao, Tapan K. Sasmal, Yanli Zou, Didi Liu, Zhixian Liao, Ye Lu, Rushuang Zhao

TL;DR
This paper introduces a catalog of 971 FR-I radio galaxies identified through a hybrid deep learning and flux analysis method, revealing new sources and detailed host galaxy properties across a wide range of redshifts and powers.
Contribution
The study presents a novel hybrid approach combining deep learning with flux distribution analysis to identify FR-I radio galaxies, including 845 new discoveries, and provides comprehensive host galaxy characterization.
Findings
Identified 971 FR-I radio galaxies, 845 of which are new.
Most host galaxies are red, with some blue high-power sources.
The sample spans a wide range of redshifts, powers, and black hole masses.
Abstract
We present a catalog of 971 FR-I radio galaxies (FR-Is) identified from the Very Large Array Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) survey. The identifications were made using a hybrid method that combines deep learning with ridgeline flux density distribution analysis. Among these sources, 845 are new discoveries. The catalog comprises sources characterized by edge-darkened double jets, an absence of significant bent morphology, and angular sizes ranging from 23 to 159 arcseconds. Optical and/or infrared counterparts have been identified for 813 FR-Is. Among these, the host galaxies are predominantly (88.1\%) red galaxies, with the remainder (11.9\%) being blue galaxies; notably, most blue galaxies exhibit high radio power. The FR-I sample spans a radio power range of at 1400 MHz…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Radio Astronomy Observations and Technology · Astrophysics and Cosmic Phenomena
