相关论文: The LSST AGN Data Challenge: Selection methods

This is the fifth paper in a series of investigations of the clustering properties of luminous, broad-emission-line active galactic nuclei (AGN) identified in the ROSAT All-Sky Survey (RASS) and Sloan Digital Sky Survey (SDSS). In this work…

Abstract abridged. The eROSITA X-ray telescope aboard the SRG orbital observatory, in the course of its all-sky survey, is expected to detect about three million active galactic nuclei (AGN) and hundred thousand clusters and groups of…

The Sloan Lens ACS (SLACS) Survey is an efficient Hubble Space Telescope Snapshot imaging survey for new galaxy-scale strong gravitational lenses. The targeted lens candidates are selected spectroscopically from within the Sloan Digital Sky…

We present an application of self-adaptive supervised learning classifiers derived from the Machine Learning paradigm, to the identification of candidate Globular Clusters in deep, wide-field, single band HST images. Several methods…

The Legacy Survey of Space and Time (LSST) will provide a ground-breaking data set for cosmology, but to achieve the precision needed, the data, data reduction, and algorithms measuring the cosmological data vectors must be thoroughly…

We used a supervised machine learning algorithm (probabilistic random forest) to classify ~130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features…

A growing number of weak- and unsupervised machine learning approaches to anomaly detection are being proposed to significantly extend the search program at the Large Hadron Collider and elsewhere. One of the prototypical examples for these…

The time delays between point-like images in gravitational lens systems can be used to measure cosmological parameters. The number of lenses with measured time delays is growing rapidly; the upcoming \emph{Large Synoptic Survey Telescope}…

Machine learning models can greatly improve the search for strong gravitational lenses in imaging surveys by reducing the amount of human inspection required. In this work, we test the performance of supervised, semi-supervised, and…

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will monitor tens of millions of active galactic nuclei (AGNs) for a period of 10 years with an average cadence of 3 days in six broad photometric bands. This…

The Vera C. Rubin Observatory will soon survey the southern sky, delivering a depth and sky coverage that is unprecedented in time domain astronomy. As part of commissioning, Data Preview 1 (DP1) has been released. It comprises a LSSTComCam…

We aim to study the effect of environment on the presence and fuelling of Active Galactic Nuclei (AGN) in massive galaxy clusters. We explore the use of different AGN detection techniques with the goal of selecting AGN across a broad range…

This paper introduces cosmoDC2, a large synthetic galaxy catalog designed to support precision dark energy science with the Large Synoptic Survey Telescope (LSST). CosmoDC2 is the starting point for the second data challenge (DC2) carried…

Building on the first paper in this series (Duncan et al. 2018), we present a study investigating the performance of Gaussian process photometric redshift (photo-z) estimates for galaxies and active galactic nuclei detected in deep radio…

We present the first ROSAC results of an AGN clustering analysis. This study comprises a sample of 200 AGNs, 75% of which being at low redshifts z<0.5, in the Ursa Major constellation. The spatial 2-point-correlation function (SCF) as well…

We present a mock catalog of gravitationally lensed quasars at $z_\text{qso}<7.5$ with simulated images for the Rubin Observatory Legacy Survey of Space and Time (LSST). We adopt recent measurements of quasar luminosity functions to model…

Building a large sample of kiloparsec (kpc)-scale dual active galactic nuclei (AGNs) amongst merging galaxies is of vital importance to understand the co-evolution between host galaxies and their central super massive black holes (SMBHs).…

In this paper, the performance of three deep learning methods for predicting short-term evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz 96 system is examined. The methods are: echo state…

Measuring the morphological parameters of galaxies is a key requirement for studying their formation and evolution. Surveys such as the Sloan Digital Sky Survey (SDSS) have resulted in the availability of very large collections of images,…

We combine multiwavelength data in the AEGIS-XD and C-COSMOS surveys to measure the typical dark matter halo mass of X-ray selected AGN [Lx(2-10keV)>1e42 erg/s] in comparison with far-infrared selected star-forming galaxies detected in the…