Related papers: The FIRST Classifier: Compact and Extended Radio G…
Machine learning techniques have been increasingly used in astronomical applications and have proven to successfully classify objects in image data with high accuracy. The current work uses archival data from the Faint Images of the Radio…
We present the application of deep machine learning technique to classify radio images of extended sources on a morphological basis using convolutional neural networks. In this study, we have taken the case of Fanaroff-Riley (FR) class of…
The continuum emission from radio galaxies can be generally classified into different morphological classes such as FRI, FRII, Bent, or Compact. In this paper, we explore the task of radio galaxy classification based on morphology using…
Classifying the morphologies of radio galaxies is important to understand their physical properties and evolutionary histories. A galaxy's morphology is often determined by visual inspection, but as survey size increases robust automated…
Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolutional neural networks have proven to be highly effective in…
The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification through visual inspection unfeasible. We present ClaRAN -…
Modern radio telescope surveys, capable of detecting billions of galaxies in wide-field surveys, have made manual morphological classification impracticable. This applies in particular when the Square Kilometre Array Observatory (SKAO)…
State-of-the-art radio observatories produce large amounts of data which can be used to study the properties of radio galaxies. However, with this rapid increase in data volume, it has become unrealistic to manually process all of the…
The simple, yet profoundly far-reaching classification scheme based on extended radio morphologies of radio galaxies, the Fanaroff-Riley classification has been a cornerstone in our understanding of radio galaxies. Over the decades since…
Generation of science-ready data from processed data products is one of the major challenges in next-generation radio continuum surveys with the Square Kilometre Array (SKA) and its precursors, due to the expected data volume and the need…
Radio galaxies exhibit a rich diversity of characteristics and emit radio emissions through a variety of radiation mechanisms, making their classification into distinct types based on morphology a complex challenge. To address this…
In this study, we examine over 14,000 radio galaxies finely selected from Radio Galaxy Zoo (RGZ) project and provide classifications for approximately 5,900 FRIs and 8,100 FRIIs. We present an analysis of these predicted radio galaxy…
We present the catalogue of Radio sources associated with Optical Galaxies and having Unresolved or Extended morphologies I (ROGUE I). It was generated by cross-matching galaxies from the Sloan Digital Sky Survey Data Release 7 (SDSS DR 7)…
We positionally match the 6 cm GB6, 20 cm FIRST and NVSS, and 92 cm WENSS radio catalogs and find 16,500 matches in ~3,000 deg2 of sky. Using this unified radio database, we construct radio "color-magnitude-morphology" diagrams and find…
Powerful radio galaxies exist as either compact or extended sources, with the extended sources traditionally classified by their radio morphologies as Fanaroff--Riley (FR) type I and II sources. FRI/II and compact radio galaxies have also…
We present an independent catalog (FRIIRGcat) of 45,241 Fanaroff-Riley Type II (FR-II) radio galaxies compiled from the Very Large Array Faint Images of the Radio Sky at Twenty-centimeters (FIRST) survey and employed the deep learning…
The advent of next-generation telescope facilities brings with it an unprecedented amount of data, and the demand for effective tools to process and classify this information has become increasingly important. This work proposes a novel…
We present the catalog of Radio sources associated with Optical Galaxies and having Unresolved or Extended morphologies I (ROGUE~I), consisting of 32,616 spectroscopically selected galaxies. It is the largest handmade catalog of this kind,…
Next-generation radio surveys will yield an unprecedented amount of data, warranting analysis by use of machine learning techniques. Convolutional neural networks are the deep learning technique that has proven to be the most successful in…
The FIRST survey, begun over twenty years ago, provides the definitive high-resolution map of the radio sky. This VLA survey reaches a 20cm detection sensitivity of 1 mJy over 10,575 deg**2 largely coincident with the SDSS area. Images and…