Related papers: Machine Learning Classification of Gaia Data Relea…
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…
The Gaia satellite will observe the positions and velocities of over a billion Milky Way stars. In the early data releases, the majority of observed stars do not have complete 6D phase-space information. In this Letter, we demonstrate the…
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…
The nearest stars provide a fundamental constraint for our understanding of stellar physics and the Galaxy. The nearby sample serves as an anchor where all objects can be studied and understood with precise data. This work is an update of…
The observation of our home galaxy, the Milky Way (MW), is made difficult by our internal viewpoint. The Gaia survey that contains around 1.6 billion star distances is the new flagship of MW structure and can be combined with other…
Making use of strong correlations between closely separated multiple or double sources and photometric and astrometric metadata in Gaia EDR3, we generate a catalog of candidate double and multiply imaged lensed quasars and AGNs, comprising…
Gaia is undertaking a deep synoptic survey of the Galaxy, but photometry from individual epochs has, as of yet, only been released for a minimal number of sources. We show that it is possible to identify variable stars in Gaia Data Release…
Gaia Data Release 2 (Gaia DR2) contains results for 1693 million sources in the magnitude range 3 to 21 based on observations collected by the European Space Agency Gaia satellite during the first 22 months of its operational phase. We…
The Gaia Data Release 2 (DR2) provided an unprecedented volume of precise astrometric and excellent photometric data. In terms of data mining the Gaia catalogue, machine learning methods have shown to be a powerful tool, for instance in the…
The occurrence of multiple stars, dominantly binaries, is studied using the \textit{Gaia}-ESA DR2 catalogue. We apply the optimized statistical method that we previously developed for the analysis of 2D patterns. The field of stars is…
We present new fully-automatic classification model to select extragalactic objects within astronomy photometric catalogs. Construction of the our classification model is based on the three important procedures: 1) data representation to…
More than half a million of the 1.69 billion sources in Gaia Data Release 2 (DR2) are published with photometric time series that exhibit light variations during the 22 months of observation. An all-sky classification of common…
We construct from Gaia eDR3 an extensive catalog of spatially resolved binary stars within $\approx$ 1 kpc of the Sun, with projected separations ranging from a few au to 1 pc. We estimate the probability that each pair is a chance…
Despite the advances provided by large-scale photometric surveys, stellar features - such as metallicity - generally remain limited to spectroscopic observations often of bright, nearby low-extinction stars. To rectify this, we present a…
The second data release of the Gaia mission includes an advance catalog of variable stars. The classification of these stars are based on sparse photometry from the first 22 months of the mission. We set out to investigate the purity and…
The Gaia mission has detected a large number of active galactic nuclei (AGN) and galaxies, but these objects must be identified among the 1000-fold more numerous stars. Extant astrometric AGN catalogs do not have the uniform sky coverage…
Context. This study has been developed in the framework of the computational simulations executed for the preparation of the ESA Gaia astrometric mission. Aims. We focus on describing the objects and characteristics that Gaia will…
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 mission has observed over 2 billion stars repeatedly across the entire sky over 10 years, revealing the many astronomical objects that vary on human timescales from seconds to years. Its repeated astrometric, photometric,…
Based on astrometric data from Gaia DR2, we employ an unsupervised machine learning method to blindly search for open star clusters in the Milky Way within the Galactic latitude range of |b| < 20 degrees. In addition to 2,080 known…