Related papers: Detecting Quasars in Large-Scale Astronomical Surv…

We describe the algorithm for selecting quasar candidates for optical spectroscopy in the Sloan Digital Sky Survey. Quasar candidates are selected via their non-stellar colors in "ugriz" broad-band photometry, and by matching unresolved…

We have conducted a pilot survey for z>3.5 quasars by combining the FIRST radio survey with the SDSS. While SDSS already targets FIRST sources for spectroscopy as quasar candidates, our survey includes fainter quasars and greatly improves…

We present a catalog of 1,172,157 quasar candidates selected from the photometric imaging data of the Sloan Digital Sky Survey (SDSS). The objects are all point sources to a limiting magnitude of i=21.3 from 8417 sq. deg. of imaging from…

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 an algorithm for selecting an uniform sample of gravitationally lensed quasar candidates from low-redshift (0.6<z<2.2) quasars brighter than i=19.1 that have been spectroscopically identified in the SDSS. Our algorithm uses…

We present a catalog of 100,563 unresolved, UV-excess (UVX) quasar candidates to g=21 from 2099 deg^2 of the Sloan Digital Sky Survey (SDSS) Data Release One (DR1) imaging data. Existing spectra of 22,737 sources reveals that 22,191 (97.6%)…

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources…

We report the first results of our systematic search for strongly lensed quasars using the spectroscopically confirmed quasars in the Sloan Digital Sky Survey (SDSS). Among 46,420 quasars from the SDSS Data Release 3 (~4188 deg^2), we…

Quasars are objects of high interest in extragalactic astrophysics, cosmology, and astrometry. One of their useful qualities is their potential radio loudness. However, the fraction of radio-loud vs. radio-quiet quasars is subject to…

Radio observations of strongly lensed objects are valuable as cosmological probes. Lensed radio sources have proven difficult to identify in large part due to the limited depth and angular resolution of the previous generation of radio sky…

Thanks to incredible advances in instrumentation, surveys like the Sloan Digital Sky Survey have been able to find and catalog billions of objects, ranging from local M dwarfs to distant quasars. Machine learning algorithms have greatly…

This paper presents the first high-resolution and high-sensitivity study of the radio properties of optically selected type~2 quasars. We used the Very Large Array at 8.4 GHz to observe 59 sources drawn from the Sloan Digital Sky Survey…

Quasars can be used to measure baryon acoustic oscillations at high redshift, which are considered as direct tracers of the most distant large-scale structures in the Universe. It is fundamental to select quasars from observations before…

The angular power spectrum is a powerful statistic for analysing cosmological signals imprinted in the clustering of matter. However, current galaxy and quasar surveys cover limited portions of the sky, and are contaminated by systematics…

We present a new method for quasar target selection using photometric fluxes and a Bayesian probabilistic approach. For our purposes we target quasars using Sloan Digital Sky Survey (SDSS) photometry to a magnitude limit of g=22. The…

We present the results of various automated classification methods, based on machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of the Sloan Digital Sky Survey (SDSS), primarily distinguishing stars from quasars. We…

One of important aims of astronomical data mining is to systematically search for specific rare objects in a massive spectral dataset, given a small fraction of identified samples with the same type. Most existing methods are mainly based…

We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey Seventh Data Release classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of…

We investigated the properties of a sample of red Quasi-stellar Objects (QSOs) using optical, radio, and infrared data. These QSOs were selected from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7) quasar catalog. We only selected…

We exploit the spectral archive of the Sloan Digital Sky Survey (SDSS) Data Release 7 to select unusual quasar spectra. The selection method is based on a combination of the power of self-organising maps and the visual inspection of a huge…