Related papers: Finding rare objects and building pure samples: Pr…
Low-frequency radio surveys are ideal for selecting orientation-independent samples of extragalactic sources because the sample members are selected by virtue of their isotropic steep-spectrum extended emission. We use the new 7C Redshift…
A star enshrouded in a Dyson sphere with high covering fraction may manifest itself as an optically subluminous object with a spectrophotometric distance estimate significantly in excess of its parallax distance. Using this criterion, the…
Aims. A new method is applied to the segmentation, and further analysis of the outliers resulting from the classification of astronomical objects in large databases is discussed. The method is being used in the framework of the Gaia…
The identification of physically associated kiloparsec-scale quasar pairs is important for understanding galaxy evolution, the growth of supermassive black holes, and their co-evolution with host galaxies. However, their rarity and the high…
Gravitationally lensed (GL) quasars are brighter than their unlensed counterparts and produce images with distinctive morphological signatures. Past searches and target selection algorithms, in particular the Sloan Quasar Lens Search…
Quasars behind the Galactic plane (GPQs) are important astrometric references and useful probes of Milky Way gas. However, the search for GPQs is difficult due to large extinctions and high source densities in the Galactic plane. Existing…
Convolutional sparse coding (CSC) can learn representative shift-invariant patterns from multiple kinds of data. However, existing CSC methods can only model noises from Gaussian distribution, which is restrictive and unrealistic. In this…
With the availability of multiwavelength, multiscale and multiepoch astronomical catalogues, the number of features to describe astronomical objects has increases. The better features we select to classify objects, the higher the…
We present a classification-based approach to identify quasi-stellar radio sources (quasars) in the Sloan Digital Sky Survey and evaluate its performance on a manually labeled training set. While reasonable results can already be obtained…
Gaia mission will offer an exceptional opportunity to perform variability studies. The data homogeneity, its optimised photometric systems, composed of 11 medium and 4-5 broad bands, the high photometric precision in G band of one milli-mag…
We study the query complexity of sampling from high-dimensional Gaussian distributions using gradient information. In the standard oracle model, exact gradients expose only matrix-vector products with the precision matrix, leading to…
Wide-separation lensed quasars (WSLQs) are a rare subclass of strongly lensed quasars produced by massive galaxy clusters. They provide valuable probes of dark-matter halos and quasar host galaxies. However, only about ten WSLQ systems are…
We consider sampling from composite densities on $\mathbb{R}^d$ of the form $d\pi(x) \propto \exp(-f(x) - g(x))dx$ for well-conditioned $f$ and convex (but possibly non-smooth) $g$, a family generalizing restrictions to a convex set,…
We demonstrate that gravitationally lensed quasars are easily recognized using image subtraction methods as time variable sources that are spatially extended. For Galactic latitudes |b|>20 deg, lensed quasars dominate the population of…
Given their extremely large distances and small apparent sizes, quasars are generally considered as objects with near-zero parallax and proper motion. However, some special quasars may have abnormal astrometric characteristics, such as…
Quasars are variable and their variability can both constrain their physical properties and help to identify them. We look for ways to efficiently identify quasars exhibiting consistent variability over multi-year time-scales, based on a…
In the second paper of this series we extend our Bayesian reanalysis of the evidence for a cosmic variation of the fine structure constant to the semi-parametric modelling regime. By adopting a mixture of Dirichlet processes prior for the…
We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks, a probabilistic graphical model, that allows us to perform inference to pre- dict missing values given observed data and…
We introduce Probabilistic Object Detection, the task of detecting objects in images and accurately quantifying the spatial and semantic uncertainties of the detections. Given the lack of methods capable of assessing such probabilistic…
We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised machine learning model, trained on SDSS DR14…