Related papers: Classification of Radio Sources Through Self-Super…
Radio galaxies are uniquely useful as probes of large-scale structure as their uniform identification with giant elliptical galaxies out to high redshift means that the evolution of their bias factor can be predicted. As the initial stage…
Using radio sources from the Faint Images of the Radio Sky at Twenty-cm (FIRST) survey, and optical counterparts in the Sloan Digital Sky Survey (SDSS), we have identified a large number of galaxy clusters. The radio sources within these…
Most galaxies in the nearby Universe are gravitationally bound to a cluster or group of galaxies. Their optical contents, such as optical richness, are crucial for understanding the co-evolution of galaxies and large-scale structures in…
We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS…
The universe is composed of galaxies that have diverse shapes. Once the structure of a galaxy is determined, it is possible to obtain important information about its formation and evolution. Morphologically classifying galaxies means…
Solar flares are energetic events in the solar atmosphere that are often linked with solar radio bursts (SRBs). SRBs are observed at metric to decametric wavelengths and are classified into five spectral classes (Type I--V) based on their…
In this paper we present three different applications, based on deep learning methodologies, that we are developing to support the scientific analysis conducted within the ASKAP-EMU and MeerKAT radio surveys. One employs instance…
Morphological features in galaxies, like spiral arms, bars, rings, tidal tails etc. carry information about their structure, origin and evolution. It is therefore important to catalog and study such features and to correlate them with other…
The growth of the number of connected devices and network densification is driving an increasing demand for radio network resources, particularly Radio Frequency (RF) spectrum. Given the dynamic and complex nature of contemporary wireless…
We present the source associations, cross-identifications, and multi-wavelength properties of the faint radio source population detected in the deep tier of the LOFAR Two Metre Sky Survey (LoTSS): the LoTSS Deep Fields. The first LoTSS Deep…
Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in models that are adaptable to many different tasks. Until now,…
Building the radio sky template are crucial for detecting the 21 cm emission line signal from the Epoch of Reionization (EoR), as well as for other cosmological research endeavors. Utilizing data from the LOFAR Two-meter Sky Survey (LoTSS)…
Radio-loud active galaxies (RLAGN) can exhibit various morphologies. The Fanaroff-Riley (FR) classifications, which are defined by the locations of peaks in surface brightness, have been applied to many catalogues of RLAGN. The FR…
Diffuse radio emission in galaxy clusters, such as radio halos, relics, and mini halos, is a key tracer of non-thermal processes, turbulence, and magnetic fields within the intra-cluster medium. However, their low surface brightness, as…
Low-frequency radio observations are revealing an increasing number of diffuse synchrotron sources from galaxy clusters, dominantly in the form of radio halos or radio relics. The existence of this diffuse synchrotron emission indicates the…
During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…
The forthcoming generation of radio telescope arrays promises significant advancements in sensitivity and resolution, enabling the identification and characterization of many new faint and diffuse radio sources. Conventional manual…
We apply classical machine vision and machine deep learning methods to prototype signal classifiers for the search for extraterrestrial intelligence. Our novel approach uses two-dimensional spectrograms of measured and simulated radio…
Fine-grained image classification involves identifying different subcategories of a class which possess very subtle discriminatory features. Fine-grained datasets usually provide bounding box annotations along with class labels to aid the…
Classifying the morphologies of galaxies is an important step in understanding their physical properties and evolutionary histories. The advent of large-scale surveys has hastened the need to develop techniques for automated morphological…