Related papers: The miniJPAS survey quasar selection V: combined a…

A systematic investigation of the relationship between different redshift estimation schemes for more than 91000 quasars in the Sloan Digital Sky Survey (SDSS) Data Release 6 (DR6) is presented. The publicly available SDSS quasar redshifts…

We present SHEEP, a new machine learning approach to the classic problem of astronomical source classification, which combines the outputs from the XGBoost, LightGBM, and CatBoost learning algorithms to create stronger classifiers. A novel…

We present a technique to explore the radio sky into the nanoJansky regime by employing image stacking using the FIRST survey. We first discuss the non-intuitive relationship between the mean and median values of a distribution that is…

Using the most recent releases of WISE and Planck data, we perform updated measurements of the bias and typical dark matter halo mass of infrared-selected obscured and unobscured quasars, using the angular autocorrelation function and…

The number density and correlation function of galaxies are two key quantities to characterize the distribution of the observed galaxy population. High-$z$ spectroscopic surveys, which usually involve complex target selection and are…

Strategic subsampling has become a focal point due to its effectiveness in compressing data, particularly in the Full Matrix Capture (FMC) approach in ultrasonic imaging. This paper introduces the Joint Deep Probabilistic Subsampling…

We apply a recently developed method for classifying broad absorption line quasars (BALQSOs) to the latest QSO catalogue constructed from Data Release 5 of the Sloan Digital Sky Survey. Our new hybrid classification scheme combines the…

Recent technological advances have led to a flood of new data on cosmology rich in information about the formation and evolution of the universe, e.g., the data collected in Sloan Digital Sky Survey (SDSS) for more than 200 million objects.…

The Javalambre Photometric Local Universe Survey (J-PLUS) is a 12-band photometric survey using the 83-cm JAST telescope. Data Release 3 includes 47.4 million sources. J-PLUS DR3 only provides star-galaxy classification so that quasars are…

Advances in optical astrometry allow us to infer the non-radial kinematic structure of the Universe directly from observations. Here I use a supervised machine learning neural network method to predict 1.57 million redshifts based on…

We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is still an unsolved problem…

We combine K-Nearest Neighbors (KNN) with genetic algorithm (GA) for photometric redshift estimation of quasars, short for GeneticKNN, which is a weighted KNN approach supported by GA. This approach has two improvements compared to KNN: one…

Samples of galaxy clusters allow us to better understand the physics at play in galaxy formation and to constrain cosmological models once their mass, position (for clustering studies) and redshift are known. In this context, large optical…

We present the final results from our survey of luminous $z \sim $ 5.5 quasars. This is the first systematic quasar survey focusing on quasars at $z \sim$ 5.5, during the post-reionization epoch. It has been challenging to select quasars at…

Understanding the formation and evolution of the cosmic web of galaxies is a fundamental goal of cosmology, using various tracers of the cosmic large-scale structure at an ever wider range of redshifts. Our principal aim is to advance the…

This article is the second in a series in which we perform an extensive comparison of various galaxy-based cluster mass estimation techniques that utilise the positions, velocities and colours of galaxies. Our aim is to quantify the…

In this work we present a new catalogue of Cosmic Filaments obtained from the latest Sloan Digital Sky Survey (SDSS) public data. In order to detect filaments, we implement a version of the Subspace-Constrained Mean-Shift algorithm, boosted…

We develop and demonstrate a classification system constituted by several Support Vector Machines (SVM) classifiers, which can be applied to select quasar candidates from large sky survey projects, such as SDSS, UKIDSS, GALEX. How to…

We analyze the counts of low-redshift quasar candidates selected using nine-epoch SDSS imaging data. The co-added catalogs are more than 1 mag deeper than single-epoch SDSS data, and allow the selection of low-redshift quasar candidates…