相关论文: Cluster membership analysis with supervised learni…

We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…

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…

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…

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…

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…

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…

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…

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…

Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87's…

The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…

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…

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,…

We develop a method for membership assignment in stellar clusters using only photometry and positions. The method, UPMASK, is aimed to be unsupervised, data driven, model free, and to rely on as few assumptions as possible. It is based on…

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…

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…

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…

Context. Near open clusters as Pleiades, Praesepe and Blanco 1 have been extensively studied due to their proximity to the Sun. The Gaia data brings the opportunity to investigate these clusters, since it contains valuable astrometric and…

The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…

The identification and characterization of stellar members within a star-forming region are critical to many aspects of star formation, including formalization of the initial mass function, circumstellar disk evolution and star-formation…

To identify member populations of IC 1396, we employ the random forest (RF) classifier of machine learning technique. Random forest classifier is an ensemble of individual decision trees suitable for large, high-dimensional datasets. The…