Related papers: A Novel Approach to Identifying Open Star Cluster …
Membership studies characterising open clusters with Gaia data, most using DR2, are so far limited at magnitude G = 18 due to astrometric uncertainties at the faint end. Our goal is to extend current open cluster membership lists with faint…
The unprecedented precision of Gaia has led to a paradigm shift in membership determination of open clusters where a variety of machine learning (ML) models can be employed. In this paper, we apply the unsupervised Gaussian Mixture Model…
Star clusters are interesting laboratories to study star formation, single and binary stellar evolution, and stellar dynamics. We have used the exquisite data from $Gaia$'s data release 3 (DR3) to study 21 relatively rich and nearby open…
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…
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…
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…
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…
Context. Since the first publication of the Gaia catalogue a new view of our Galaxy has arrived. Its astrometric and photometric information has improved the precision of the physical parameters of open star clusters obtained from them.…
In this work, starting from the well-accepted relations in literature, we introduce a new formalism to compute the astrometric membership probabilities for sources in star clusters, and we provide an application to the case of the open…
Open clusters (OCs) are regarded as tracers to understand stellar evolution theory and validate stellar models. In this study, we presented a robust approach to identifying OCs. A hybrid method of pyUPMASK and RF is first used to remove…
Young stellar clusters harbour complex spatial structures emerging from the star formation process. Identifying stellar over-densities is a key step to constrain better how these structures are formed. The high accuracy of distances derived…
We present a homogeneous catalog of 1,232 open clusters with precisely determined ages, metallicities, distances, extinctions, and stellar mass function (MF) slopes, derived from Gaia DR3 data. The parameters are inferred using the Mixture…
Open clusters are among the most useful and widespread tracers of Galactic structure. The completeness of the Galactic open cluster census, however, remains poorly understood. For the first time ever, we establish the selection function of…
Open clusters (OCs) are common in the Milky Way, but most of them remain undiscovered. There are numerous techniques (e.g., machine-learning algorithms) available for the exploration of OCs. However, each method has its limitations and…
The outer Galaxy presents a distinctive environment for investigating star formation. This study develops a novel approach to identify true cluster members based on unsupervised clustering using astrometry with significant uncertainties. As…
Membership analysis is an important tool for studying star clusters. There are various approaches to membership determination, including supervised and unsupervised machine learning (ML) methods. We perform membership analysis using the…
Context. Stars tend to form in clusters, but many escape their birth clusters very early. Identifying the escaped members of clusters can inform us about the dissolution of star clusters, but also about the stellar dynamics in the galaxy.…
We systematically searched for open clusters in the solar neighborhood within 500 pc using pyUPMASK and HDBSCAN clustering algorithms based on {\it Gaia} DR3. Taking into consideration that the physical size for most open clusters is less…
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…
A rare multiple open cluster system has been analyzed using Gaia DR3 astrometry and photometry data. Using Agglomerative Hierarchical clustering and Bayesian-HDBSCAN, we identify a compact core consisting of seven known open clusters and…