Related papers: Random Forests as a viable method to select and di…

Upcoming surveys such as Euclid, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (Roman) will detect hundreds of high-redshift (z > 7) quasars, but distinguishing them from the…

Bright quasar samples at high redshift are useful for investigating active galactic nuclei evolution. In this study, we describe XQz5, a sample of 83 ultraluminous quasars in the redshift range $4.5 < z < 5.3$ with optical and near-infrared…

With the aim of using machine learning techniques to obtain photometric redshifts based upon a source's radio spectrum alone, we have extracted the radio sources from the Million Quasars Catalogue. Of these, 44,119 have a spectroscopic…

We present WISE All-Sky mid-infrared (IR) survey detections of 55% (17/31) of the known QSOs at z>6 from a range of surveys: the SDSS, the CFHT-LS, FIRST, Spitzer and UKIDSS. The WISE catalog thus provides a substantial increase in the…

The application of supervised artificial neural networks (ANNs) for quasar selection from combined radio and optical surveys with photometric and morphological data is investigated, using the list of candidates and their classification from…

The forthcoming Wide Area Vista Extragalactic Survey (WAVES) on the 4-metre Multi-Object Spectroscopic Telescope (4MOST) has a key science goal of probing the halo mass function to lower limits than possible with previous surveys. For that…

We apply a matched-filter cluster detection algorithm to the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) i-band data for the Deep-1, Deep-2, Deep-3 and Deep-4 fields covering a total of 4square degrees. To test the implemented…

We present photometric selection of type 1 quasars in the $\approx5.3~{\rm deg}^{2}$ XMM-Large Scale Structure (XMM-LSS) survey field with machine learning. We constructed our training and \hbox{blind-test} samples using spectroscopically…

We present a novel method for the optimal selection of quasars using time-series observations in a single photometric bandpass. Utilizing the damped random walk model of Kelly et al. (2009), we parameterize the ensemble quasar structure…

Photometric selection of luminous high-redshift ($z\gtrsim4$) quasars is plagued by contamination from numerous low-mass Galactic stars, reddened lower-redshift quasars, as well as compact luminous red galaxies. Confirmation of these rare…

Forthcoming astronomical surveys are expected to detect new sources in such large numbers that measuring their spectroscopic redshift measurements will be not be practical. Thus, there is much interest in using machine learning to yield the…

The Gaia DR3 has provided a large sample of more than 6.6 million quasar candidates with high completeness but low purity. Previous work on the CatNorth quasar candidate catalog has shown that including external multiband data and applying…

We present the results of the Extremely Luminous Quasar Survey in the $3\pi$ survey of the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS; PS1). This effort applies the successful quasar selection strategy of the Extremely…

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…

Random forests are a widely used machine learning algorithm, but their computational efficiency is undermined when applied to large-scale datasets with numerous instances and useless features. Herein, we propose a nonparametric feature…

Luminous quasars at z>5.6 can be studied in detail with the current generation of telescopes and provide us with unique information on the first gigayear of the universe. Thus far these studies have been statistically limited by the number…

Random Forest has become one of the most popular tools for feature selection. Its ability to deal with high-dimensional data makes this algorithm especially useful for studies in neuroimaging and bioinformatics. Despite its popularity and…

Hash codes are a very efficient data representation needed to be able to cope with the ever growing amounts of data. We introduce a random forest semantic hashing scheme with information-theoretic code aggregation, showing for the first…

We present the quasar catalog from Data Releases 10 to 12 of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Quasar Survey, comprising quasars observed between September 2021 and June 2024. We robustly identified…

Galaxy morphologies provide valuable insights into their formation processes, tracing the spatial distribution of ongoing star formation and encoding signatures of dynamical interactions. While such information has been extensively…