Related papers: Finding rare objects and building pure samples: Pr…

Classification of stars and galaxies is a well-known astronomical problem that has been treated using different approaches, most of them relying on morphological information. In this paper, we tackle this issue using the low-resolution…

We consider the problem of estimating cross-spectral quantities in the low-frequency regime, where long observation times limit averaging over large ensembles of periodograms, thereby preventing the use of approximate Gaussian statistics.…

We present the proof-of-concept of a method to find strongly lensed quasars using their spatially-extended photometric variability through difference imaging in cadenced imaging survey data. We apply the method to Pan-STARRS, starting with…

Near-future data from ESA's Gaia mission will provide precise, full phase-space information for hundreds of millions of stars out to heliocentric distances of ~10 kpc. This "horizon" for full phase-space measurements is imposed by the Gaia…

Gaussian time-series models are often specified through their spectral density. Such models present several computational challenges, in particular because of the non-sparse nature of the covariance matrix. We derive a fast approximation of…

In decision-making systems, it is important to have classifiers that have calibrated uncertainties, with an optimisation objective that can be used for automated model selection and training. Gaussian processes (GPs) provide uncertainty…

We discuss whether modern machine learning methods can be used to characterize the physical nature of the large number of objects sampled by the modern multi-band digital surveys. In particular, we applied the MLPQNA (Multi Layer Perceptron…

Microlensing events can be used to directly measure the masses of single field stars to a precision of $\sim$1-10\%. The majority of direct mass measurements for stellar and sub-stellar objects typically only come from observations of…

We present SQUEzE, a software package to classify quasar spectra and estimate their redshifts. SQUEzE is a random forest classifier operating on the parameters of candidate emission peaks identified in the spectra. We test the performance…

The European Southern Observatory's (ESO) Visible and Infrared Survey Telescope for Astronomy (VISTA) is a 4-m class survey telescope for wide-field near-infrared imaging. VISTA is currently running a suite of six public surveys, which will…

This study explores the use of symbolic regression (SR) combined with genetic algorithms (GA) to classify astronomical objects. Using the SDSS17 dataset from Kaggle, which includes 100,000 observations of stars, galaxies, and quasars, we…

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

A new fast Bayesian approach is introduced for the detection of discrete objects immersed in a diffuse background. This new method, called PowellSnakes, speeds up traditional Bayesian techniques by: i) replacing the standard form of the…

We focus on the search for unresolved faint quasars and AGN in the crude combine images using a multicolor imaging analysis that has proven very successful in recent years. Quasar selection was carried out both in multicolor space and in…

We apply instance-based machine learning in the form of a k-nearest neighbor algorithm to the task of estimating photometric redshifts for 55,746 objects spectroscopically classified as quasars in the Fifth Data Release of the Sloan Digital…

Exoplanet catalogs produced by surveys suffer from a lack of completeness (not every planet is detected) and less than perfect reliability (not every planet in the catalog is a true planet), particularly near the survey's detection limit.…

We search for compact objects in binaries based on Gaia DR3. A sample of ten targets is derived under the conditions: radial velocity variable, low temperature ($T_{\rm eff} < 6000$ K), high mass function ($f(M_2) > 1 M_\odot$), and…

We propose a new method to search for light (pseudo-)scalar particles in the spectra of compact astrophysical objects such as magnetars, pulsars, and quasars. On accounts of compact astrophysical objects having intense magnetic fields…

Astronomers are often confronted with funky populations and distributions of objects: brighter objects are more likely to be detected; targets are selected based on colour cuts; imperfect classification yields impure samples. Failing to…

The unprecedented volume and quality of data from space- and ground-based telescopes present an opportunity for machine learning to identify new classes of variable stars and peculiar systems that may have been overlooked by traditional…