Related papers: Radio Galaxy Zoo: Morphological classification by …

The radio luminosity function (RLF) of radio galaxies and radio-loud quasars is often modelled as a broken power-law. The break luminosity is close to the dividing line between the two Fanaroff-Riley (FR) morphological classes for the…

In this work we use variational inference to quantify the degree of epistemic uncertainty in model predictions of radio galaxy classification and show that the level of model posterior variance for individual test samples is correlated with…

We built a catalog of 219 FRI radio galaxies (FRIs), called FRICAT, selected from a published sample and obtained by combining observations from the NVSS, FIRST, and SDSS surveys. We included in the catalog the sources with an edge-darkened…

New-generation radio telescopes like LOFAR are conducting extensive sky surveys, detecting millions of sources. To maximise the scientific value of these surveys, radio source components must be properly associated into physical sources…

We present 2-4 GHz observations of polarized radio galaxies towards eight fast radio bursts (FRBs), producing grids of Faraday rotation measure (RM) sources with sky densities of 9-28 polarized sources per square degree. Using a Bayesian…

We argue that the origin of "FRI/FRI{-.1em}I dichotomy" -- the division between Fanaroff-Riley class I (FRI) with subsonic lobes and class I{-.1em}I (FRI{-.1em}I) radio sources with supersonic lobes is sharp in the radio-optical luminosity…

The classification of galaxy morphology plays a crucial role in understanding galaxy formation and evolution. Traditionally, this process is done manually. The emergence of deep learning techniques has given room for the automation of this…

In this paper, we collect radio and X-ray observations for most Fanaroff-Riley I (FRI) radio galaxies in the Zirbel-Baum radio galaxy sample, and investigate the distribution of the radio-to-X-ray effective spectral index, $\alpha_{rx}$, to…

Since 2008 August the Fermi Large Area Telescope (LAT) has provided continuous coverage of the gamma-ray sky yielding more than 5000 gamma-ray sources, but 54% of the detected sources remain with no certain or unknown association with a low…

We analyze the environmental dependence of galaxy morphology and colour with two-point clustering statistics, using data from the Galaxy Zoo, the largest sample of visually classified morphologies yet compiled, extracted from the Sloan…

We present the data release for Galaxy Zoo 2 (GZ2), a citizen science project with more than 16 million morphological classifications of 304,122 galaxies drawn from the Sloan Digital Sky Survey. Morphology is a powerful probe for…

We present a large-scale clustering analysis of radio galaxies in the Very Large Array (VLA) Faint Images of the Radio Sky at Twenty-cm (FIRST) survey over the Galaxy And Mass Assembly (GAMA) survey area, limited to S1.4 GHz >1 mJy with…

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)…

Machine learning techniques that perform morphological classification of astronomical sources often suffer from a scarcity of labelled training data. Here, we focus on the case of supervised deep learning models for the morphological…

Extragalactic radio sources appear under different morphologies, the most frequent ones are classified as Fanaroff-Riley type I (FR I), typically with lower luminosities, and Fanaroff-Riley type II, (FR II), typically more luminous. This…

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and…

One of the major challenges in astronomy involves accurately classifying galaxies, particularly distinguishing between different galaxy types. While many complex algorithms have shown strong performance in classification tasks, their…

This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a Self-Organising Map (SOM) and a convolutional autoencoder. The rapidly increasing volume of radio-astronomical data has increased demand…

We present deep spectroscopic data for a 24-object subsample of our full 41-object z~0.5 radio galaxy sample in order to investigate the evolution of the Fundamental Plane of radio galaxies. We find that the low-luminosity, FRI-type, radio…

We present quantified visual morphologies of approximately 48,000 galaxies observed in three Hubble Space Telescope legacy fields by the Cosmic And Near-infrared Deep Extragalactic Legacy Survey (CANDELS) and classified by participants in…