Related papers: ML-MOC: Machine Learning (kNN and GMM) based Membe…

We derive astrophysical and structural parameters of open clusters NGC 1245 and NGC 2099 from 2MASS JHKs and Gaia DR2 photometric / astrometric data bases. Their likely members have been determined from Gaia DR2 proper motion data. Our…

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…

We present a new technique designed to take full advantage of the high dimensionality (photometric, astrometric, temporal) of the DANCe survey to derive self-consistent and robust membership probabilities of the Pleiades cluster. We aim at…

The first Gaia Data Release presents an opportunity to characterise the low-mass population of OB associations, providing larger statistical samples and better constraints on the formation and evolution of clusters and associations. Using…

Context. Open clusters that emerged from the star forming regions as gravitationally bound structures are subjected to star evaporation, ejection, and tidal forces throughout the rest of their lives. Consequently they form tidal tails that…

We present a machine learning (ML) framework for the detection of wide binary star systems using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ…

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…

In the Gaia era, a comprehensive analysis of the binary open clusters NGC 869 (h Persei) and NGC 884 (chi Persei) system has been conducted to investigate its structural, astrophysical, kinematic, and Galactic orbital properties, along with…

Sparse open clusters can be found at high galactic latitudes where loosely populated clusters are more easily detected against the lower stellar background. Because most star formation takes place in the thin disk, the observed population…

Context. Observations indicate that the fraction of potential binary star clusters in the Milky Way is either the same or lower than that of the Magellanic Clouds. The unprecedented precision in the parallax measurements by Gaia has allowed…

The membership determination for open clusters in noisy environments of the Milky Way is still an open problem. In this paper, our main aim is provide the membership probability of stars using proper motions and parallax values of stars…

We investigate machine learning (ML) techniques for predicting the number of galaxies (N_gal) that occupy a halo, given the halo's properties. These types of mappings are crucial for constructing the mock galaxy catalogs necessary for…

Feature selection poses a challenge in small-sample high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There isn't a universally optimal…

We report the discovery of four new open clusters (named as QC 1, QC 2, QC3 and QC 4) in the direction of Cygnus Cloud and select their members based on five astrometric parameters (l, b, \varpi, \mu^*_{\alpha}, \mu_{\delta}) of Gaia DR2.…

Open clusters (OCs) are popular tracers of the structure and evolutionary history of the Galactic disk. The OC population is often considered to be complete within 1.8 kpc of the Sun. The recent Gaia Data Release 2 (DR2) allows the latter…

We investigate the evolutionary stages of four open clusters, Berkeley 39, Collinder 261, NGC 6819, and NGC 7789, of ages ranging from 1.6 -- 6 Gyr. These clusters have previously been classified into dynamically young and intermediate age…

Context. Open clusters are very good tracers of the evolution of the Galactic disc. Thanks to Gaia, their kinematics can be investigated with an unprecedented precision and accuracy. Aims. The distribution of open clusters in the 6D phase…

Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…

The precision of the parallax measurements by Gaia is unprecedented. As of Gaia Data Release 2, the number of known nearby open clusters has increased. Some of the clusters appear to be relatively close to each other and form aggregates,…

In this paper, we describe a new method to select globular cluster (GC) candidates, including galaxy subtraction with unsharp masking, template fitting techniques and the inclusion of Gaia's proper motions. We report the use of the 12-band…