Related papers: The membership of stars, density profile and mass …
In our previous work, we introduced a method that combines two unsupervised algorithms: DBSCAN and GMM. We applied this method to 12 open clusters based on Gaia EDR3 data, demonstrating its effectiveness in identifying reliable cluster…
In this paper, we apply the machine learning clustering algorithm Density Based Spatial Clustering of Applications with Noise (DBSCAN) to study the membership of stars in twelve open clusters (NGC~2264, NGC~2682, NGC~2244, NGC~3293,…
The existing open cluster membership determination algorithms are either prior dependent on some known parameters of clusters or are not automatable to large samples of clusters. In this paper, we present, ML-MOC, a new machine learning…
We present a kinematic and dynamical analysis of six Galactic open clusters NGC~2204, NGC~2660, NGC~2262, Czernik~32, Pismis~18, and NGC~2437, using \textit{Gaia}~DR3. We used Bayesian and Gaussian Mixture Model (GMM) methods to identify…
We present a study of six open clusters (Berkeley 67, King 2, NGC 2420, NGC 2477, NGC 2682 and NGC 6940) using the Ultra Violet Imaging Telescope (UVIT) aboard \textit{ASTROSAT} and \textit{Gaia} EDR3. We used combinations of astrometric,…
Open clusters are groups of stars that form at the same time, making them an ideal laboratory to test theories of star formation, stellar evolution, and dynamics in the Milky Way disk. However, the utility of an open cluster can be limited…
We present a comprehensive analysis of four young open clusters, NGC 663, NGC 2301, NGC 2384, and NGC 7510, utilizing high-precision astrometric and photometric data from Gaia DR3. Cluster membership was determined using the UPMASK…
We derive the proper motions, membership probabilities, and velocity dispersions of stars in the regions of the young (about 2-4 Myr-old) open clusters NGC 2244 (the central cluster in the Monoceros R2 association) and NGC 6530 (the…
M35 is a young open cluster and home to an extensive binary population. Using Gaia DR3, Pan-STARRS, and 2MASS photometry with the Bayesian statistical software, BASE-9, we derive precise cluster parameters, identify single and binary…
We search for mass segregation in the intermediate-aged open cluster NGC 6819 within a carefully identified sample of probable cluster members. Using photometry from the Gaia, 2MASS, and Pan-STARRS surveys as inputs for a Bayesian…
Context: This research presents unsupervised machine learning and statistical methods to identify and analyze tidal tails in open star clusters using data from the Gaia DR3 catalog. Aims: We aim to identify member stars and to detect and…
We present the results of a new study of mass segregation in two-component star clusters, based on a large number of numerical N-body simulations using our recently developed dynamical Monte Carlo code. Specifically, we follow the dynamical…
We carry out a search for tidal tails in a sample of open clusters with known relatively elongated morphology. We identify the member stars of these clusters from the precise astrometric and deep photometric data from $Gaia$ Early Data…
We report 1,656 new star clusters found in the Galactic disk (|b|<20 degrees) beyond 1.2 kpc, using Gaia EDR3 data. Based on an unsupervised machine learning algorithm, DBSCAN, and followed our previous studies, we utilized a unique method…
The distribution of member stars in the surroundings of an Open Cluster (OC) can shed light on the process of its formation, evolution and dissolution. The analysis of structural parameters of OCs as a function of their age and position in…
Recent studies have identified star clusters with multiple components based on accurate spatial distributions and/or proper motions from Gaia DR3, utilising diverse diagnostics to improve our understanding of subgroup evolution. These…
Time-series VI CCD photometry of the globular cluster M10 (NGC 6254) is employed to perform a detailed identification, inspection of their light curves, their classification and their cluster membership, of all the known variables reported…
We present a new geometrical method aimed at determining the members of open clusters. The methodology estimates, in an N-dimensional space, the membership probabilities by means of the distances between every star and the cluster central…
Membership of stars in open clusters is one of the most crucial parameters in studies of star clusters. Gaia opened a new window in the estimation of membership because of its unprecedented 6-D data. In the present study, we used published…
This work proposes a multiple machine learning method (MMLM) aiming to improve the accuracy and robustness in the analysis of star clusters. The MMLM performance is evaluated by applying it to the reanalysis of the old binary cluster…