Related papers: Classification of Radio Sources Through Self-Super…
The LOw Frequency ARray Two-metre Sky Survey second data release (LoTSS DR2) covers 27\% of the northern sky and contains around four million radio sources. The development of this catalogue involved a large citizen science project (Radio…
Machine learning techniques have been successfully used to classify variable stars on widely-studied astronomical surveys. These datasets have been available to astronomers long enough, thus allowing them to perform deep analysis over…
In this work we study the flux density dependence of the redshift distribution of low-frequency radio sources observed in the LOFAR Two-metre Sky Survey (LoTSS) deep fields and apply it to estimate the clustering length of the large-scale…
The classification of radio galaxies is central to understanding galaxy evolution, active galactic nuclei dynamics, and the large-scale structure of the universe. However, traditional manual techniques are inadequate for processing the…
Diffuse radio emission has been detected in a considerable number of galaxy clusters and groups, revealing the presence of pervasive cosmic magnetic fields, and of relativistic particles in the large-scale structure (LSS) of the Universe.…
Modern radio telescopes will daily generate data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries.…
Morphologically classifying radio sources in continuum images with the SKA has the potential to address some of the key questions in cosmology and galaxy evolution. In particular, we may use different classes of radio sources as independent…
Aiming at improving performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called Deep Co-Space (DCS). Unlike many conventional semi-supervised learning methods…
We present a novel unsupervised learning approach to automatically segment and label images in astronomical surveys. Automation of this procedure will be essential as next-generation surveys enter the petabyte scale: data volumes will…
The Low Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) is a sensitive, high-resolution 120-168 MHz survey split across multiple tiers over the northern sky. The first LoTSS Deep Fields data release consists of deep radio continuum…
With the development of deep learning, supervised learning methods perform well in remote sensing images (RSIs) scene classification. However, supervised learning requires a huge number of annotated data for training. When labeled samples…
Galaxy morphology classification plays a crucial role in understanding the structure and evolution of the universe. With galaxy observation data growing exponentially, machine learning has become a core technology for this classification…
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…
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…
Aims. With the recent preliminary release of the LOFAR LBA Sky Survey (LoLSS), the first wide-area, ultra-low frequency observations from LOFAR were published. Our aim is to combine this data set with other surveys at higher frequencies to…
Solar Radio Bursts (SRBs) are generally observed in dynamic spectra and have five major spectral classes, labelled Type I to Type V depending on their shape and extent in frequency and time. Due to their complex characterisation, a…
The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and…
We propose to learn latent space representations of radio galaxies, and train a very deep variational autoencoder (\protect\Verb+VDVAE+) on RGZ DR1, an unlabeled dataset, to this end. We show that the encoded features can be leveraged for…
The relative positions of the high and low surface brightness regions of radio-loud active galaxies in the 3CR sample were found by Fanaroff and Riley to be correlated with their luminosity. We revisit this canonical relationship with a…
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…