Related papers: Machine Learning Regression of extinction in the s…

Context. In the current ever increasing data volumes of astronomical surveys, automated methods are essential. Objects of known classes from the literature are necessary for training supervised machine learning algorithms, as well as for…

Aims. We aim to map the 3D distribution of the interstellar extinction of the Milky Way disk up to distances larger than those probed with the Gaia parallax alone. Methods. We apply the FEDReD (Field Extinction-Distance Relation…

The Gaia satellite was selected as a cornerstone mission of the European Space Agency (ESA) in October 2000 and confirmed in 2002 with a current target launch date of 2011. The Gaia mission will gather on the same observational principles…

(Abridged) Gaia aims to make a 3-dimensional map of 1,000 million stars in our Milky Way to unravel its kinematical, dynamical, and chemical structure and evolution. Gaia's on-board detection software discriminates stars from spurious…

We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift…

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…

We present a method for accurately and precisely inferring photometric dust extinction towards stars at mid-to-high Galactic latitudes using probabilistic machine learning to model the colour-magnitude distribution of zero-extinction stars…

The third Gaia Data Release, covering 34 months of data, includes the second Gaia catalogue of long-period variables (LPVs), with G variability amplitudes larger than 0.1 mag (5-95% quantile range). The paper describes the production and…

3D maps of interstellar dust are crucial for understanding the structure of the Milky Way interstellar medium to apply correction to astrophysical observations affected by dust. We aim at providing new extinction estimates in the Gaia BP/RP…

Gaia is an astrometric space experiment that is measuring positions, proper motions as well as parallaxes for a huge number of stars. It operates a medium-dispersion spectrometer, the RVS, that provides spectra and thus radial velocity…

Aims: In this work, we aim to provide a reliable list of gravitational lens (GL) candidates based on a search performed over the entire Gaia Data Release 2 (Gaia DR2). We also show that the sole astrometric and photometric informations…

The Gaia DR2 catalog released in 2018 gives information about more than one billion stars, including their extremely precise positions that are not affected by the atmosphere, as well as the magnitudes in the G, RP, and BP passbands. This…

Here we develop a general statistical procedure for the analysis of finite two-dimensional (2D) patterns inspired by the analysis of heavy-ion data. The method is used in the study of publicly available data obtained by the Gaia-ESA…

The second data release of the Gaia mission contained astrometry and photometry for an incredible 1,692,919,135 sources, but how many sources did Gaia miss and where do they lie on the sky? The answer to this question will be crucial for…

The systematic offset of Gaia parallaxes has been widely reported with Gaia's second data release, and it is expected to persist in future Gaia data. In order to use Gaia parallaxes to infer distances to high precision, we develop a…

The Kepler Mission revolutionized exoplanet science and stellar astrophysics by obtaining highly precise photometry of over 200,000 stars over 4 years. A critical piece of information to exploit Kepler data is its selection function, since…

Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to…

We construct a supervised classifier based on Gaussian Mixture Models to probabilistically classify objects in Gaia data release 2 (GDR2) using only photometric and astrometric data in that release. The model is trained empirically to…

We determine the interstellar extinction in the selected high-latitude areas of the sky based on Gaia EDR3 astrometry and photometry and spectroscopic data from RAVE survey. We approximate the results with the cosecant law in each area thus…

Precise spectroscopic classification of planet hosts is an important tool of exoplanet research at both the population and individual system level. In the era of large-scale surveys, data-driven methods offer an efficient approach to…