相关论文: Detection of Open Cluster Members Inside and Beyon…

We present a novel approach for identifying members of open star clusters using Gaia DR3 data by combining Minimum Spanning Tree (MST) and Gaussian Mixture Model (GMM) techniques. Our method employs a three-step process: initial filtering…

A combination of two unsupervised machine learning algorithms, DBSCAN and GMM are used to find members with a high probability of twelve open clusters, M38, NGC2099, Coma Ber, NGC752, M67, NGC2243, Alessi01, Bochum04, M34, M35, M41, and…

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…

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 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…

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…

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,…

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…

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 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…

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…

The peripheral regions of globular clusters (GCs) are extremely challenging to study due to their low surface brightness nature and the dominance of Milky Way contaminant populations along their sightlines. We have developed a probabilistic…

With the help of Gaia data, it is noted that in addition to the core components, there are low-density outer halo components in the extended region of open clusters. To study the extended structure beyond the core radius of the cluster…

In this work we present a method to identify possible members of globular clusters using data from Gaia DR2. The method consists of two stages: the first one based on a clustering algorithm, and the second one based on the analysis of the…

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…

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…

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…

Open clusters are key targets for both Galaxy structure and evolution and stellar physics studies. Since \textit{Gaia} DR2 publication, the discovery of undetected clusters has proven that our samples were not complete. Our aim is to…

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 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…