Related papers: SDSS-RASS: Next Generation of Cluster-Finding Algo…
The XMM Cluster Survey (XCS) is a serendipitous search for galaxy clusters using all publicly available data in the XMM-Newton Science Archive. Its main aims are to measure cosmological parameters and trace the evolution of X-ray scaling…
The Sloan Digital Sky Survey has obtained 450 square degrees of photometric scan data, in five bands (u',g',r',i',z'), which we use to identify clusters of galaxies. We illustrate how we do star-galaxy separation, and present a simple and…
Clustering multidimensional points is a fundamental data mining task, with applications in many fields, such as astronomy, neuroscience, bioinformatics, and computer vision. The goal of clustering algorithms is to group similar objects…
DBSCAN is a typically used clustering algorithm due to its clustering ability for arbitrarily-shaped clusters and its robustness to outliers. Generally, the complexity of DBSCAN is O(n^2) in the worst case, and it practically becomes more…
We present a new two-color algorithm, the "Stellar Bump Sequence" (SBS), that is optimized for robustly identifying candidate high-redshift galaxy clusters in combined wide-field optical and mid-infrared (MIR) data. The SBS algorithm is a…
Clustering is ubiquitous in data analysis, including analysis of time series. It is inherently subjective: different users may prefer different clusterings for a particular dataset. Semi-supervised clustering addresses this by allowing the…
This paper describes updated predictions, as a function of the underlying cosmological model, for a serendipitous galaxy cluster survey that we plan to conduct with the {\em XMM-Newton} X-ray Satellite. We have included the effects of the…
We investigate the quality of associations of astronomical sources from multi-wavelength observations using simulated detections that are realistic in terms of their astrometric accuracy, small-scale clustering properties and selection…
Subspace clustering aims to group data points that lie in a union of low-dimensional subspaces and finds wide application in computer vision, hyperspectral imaging, and recommendation systems. However, most existing methods assume fully…
Subspace clustering refers to the problem of clustering high-dimensional data into a union of low-dimensional subspaces. Current subspace clustering approaches are usually based on a two-stage framework. In the first stage, an affinity…
Context: The VISTA Variables in the Via Lactea (VVV) and its extension (VVVX) are near-infrared surveys mapping the Galactic bulge and adjacent disk. These data have enabled the discovery of numerous star clusters obscured by high and…
A microlensing exoplanet search is a unique method for finding planets orbiting distant stars. However, in the past, the method used to analyze microlensing data could not deal with complex lens systems. The number of lenses was limited…
We present a deep X-ray selected sample of galaxy clusters which has been created from a serendipitous search in ROSAT-PSPC deep pointed observations at high galactic latitude. This survey, hereafter known as the ROSAT Deep Cluster Survey…
We present a novel method for automatically detecting and characterising semi-resolved star clusters: clusters where the observational point-spread function (PSF) is smaller than the cluster's radius, but larger than the separations between…
We measure the angular 2-point correlation functions of galaxies in a volume limited, photometrically selected galaxy sample from the fifth data release of the Sloan Digital Sky Survey. We split the sample both by luminosity and galaxy type…
Searches for continuous gravitational waves target nearly monochromatic gravitational wave emission from e.g. non-axysmmetric fast-spinning neutron stars. Broad surveys often require to explicitly search for a very large number of different…
Detection of interesting (e.g., coherent or anomalous) clusters has been studied extensively on plain or univariate networks, with various applications. Recently, algorithms have been extended to networks with multiple attributes for each…
In many real-world problems, we are dealing with collections of high-dimensional data, such as images, videos, text and web documents, DNA microarray data, and more. Often, high-dimensional data lie close to low-dimensional structures…
Density-based clustering is the most popular clustering algorithm since it can identify clusters of arbitrary shape as long as they are separated by low-density regions. However, a high-density region that is not separated by low-density…
We find clusters and superclusters of galaxies using the Data Release 1 of the Sloan Digital Sky Survey. Density fields with smoothing lengthst of 10 and 0.8 Mpc/h are calculated. We found that galaxies and clusters in a high-density…