Related papers: Identifying star clusters in a field: A comparison…
Aims: According to some estimations, there are as many as 100000 open clusters in the Galaxy, but less than 2000 of them have been discovered, measured, and cataloged. We plan to undertake data mining of multiwavelength surveys to find new…
We describe the scientific motivation behind, and the methodology of, the Stanford Cluster Search (StaCS), a program to compile a catalog of optically selected clusters of galaxies at intermediate and high (0.3 < z < 1) redshifts. The…
The HST data archive contains images of several nearby spirals, suitable for detailed studies of the properties of individual star clusters and their surroundings. By combining structural information derived from HST images with…
We introduce a new kinematic method to investigate the structure of open star clusters. We adopt a hierarchical clustering algorithm that uses the celestial coordinates and the proper motions of the stars in the field of view of the cluster…
We present an innovative and widely applicable approach for the detection and classification of stellar clusters, developed for the PHANGS-HST Treasury Program, an $NUV$-to-$I$ band imaging campaign of 38 spiral galaxies. Our pipeline first…
We present the results of methodological works on automated analysis of the large scale distribution of galaxies. Selecting candidates for clusters and groups of galaxies was carried out using two complementary methods of determining the…
A novel nonparametric clustering algorithm is proposed using the interpoint distances between the members of the data to reveal the inherent clustering structure existing in the given set of data, where we apply the classical nonparametric…
We present a comparison of three cluster finding algorithms from imaging data using Monte Carlo simulations of clusters embedded in a 25 deg^2 region of Sloan Digital Sky Survey (SDSS) imaging data: the Matched Filter (MF; Postman et al.…
The vast volume of data generated by modern astronomical surveys offers test beds for the application of machine-learning. It is important to evaluate potential existing tools and determine those that are optimal for extracting scientific…
Spectral clustering is one of the most prominent clustering approaches. The distance-based similarity is the most widely used method for spectral clustering. However, people have already noticed that this is not suitable for multi-scale…
Galaxy clusters are the largest structures in Universe. They are very important as both cosmological probes and astrophysical laboratories. Several methods have been developed to detect galaxy clusters with different techniques (optical,…
We have developed a novel method of determining 2D radial density profiles for astronomical systems of discrete objects using Voronoi tessellations. This Voronoi-based method was tested against the standard annulus-based method on 5…
Hierarchical clustering is a common algorithm in data analysis. It is unique among many clustering algorithms in that it draws dendrograms based on the distance of data under a certain metric, and group them. It is widely used in all areas…
We present the preliminary result of our project, consisting in studying the properties of a large sample of galaxy clusters. The M\"{u}nster Red Sky Survey, which is a large galaxy catalogue covering an area of about 5000 square degrees on…
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…
Clusters of galaxies are the most massive objects in the Universe and mapping their location is an important astronomical problem. This paper describes an algorithm (based on statistical signal processing methods), a software architecture…
We compare the existent methods including the minimum spanning tree based method and the local stellar density based method, in measuring mass segregation of star clusters. We find that the minimum spanning tree method reflects more the…
We describe the topological cluster classification (TCC) algorithm. The TCC detects local structures with bond topologies similar to isolated clusters which minimise the potential energy for a number of monatomic and binary simple liquids…
We present a comparison between two optical cluster finding methods: a matched filter algorithm using galaxy angular coordinates and magnitudes, and a percolation algorithm using also redshift information. We test the algorithms on two mock…
Face clustering is a promising way to scale up face recognition systems using large-scale unlabeled face images. It remains challenging to identify small or sparse face image clusters that we call hard clusters, which is caused by the…