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High-dimensional clustering analysis is a challenging problem in statistics and machine learning, with broad applications such as the analysis of microarray data and RNA-seq data. In this paper, we propose a new clustering procedure called…

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…

Hierarchical structure from stellar clusters, to subgroups, to associations and star complexes is discussed in the context of the Orion stellar grouping and its origin. The analogous structure in gas clouds is also reviewed, with an…

A cluster finding method was developed and applied in four Local Group Galaxies (SMC, M31, M33 and NGC 6822). The aim is to study the young stellar population of these galaxies by identifying stellar structures in small and large scales.…

Machine learning techniques can reveal hidden structure in large data amounts and can potentially extent or even replace analytical scientific methods. In nanophotonics, modes can increase the light yield from emitters located inside the…

We present initial results on the use of Mixture Models for density estimation in large astronomical databases. We provide herein both the theoretical and experimental background for using a mixture model of Gaussians based on the…

Many young star clusters appear to be fractal, i.e. they appear to be concentrated in a nested hierarchy of clusters within clusters. We present a new algorithm for statistically analysing the distribution of stars to quantify the level of…

We study the morphological properties of mid-infrared selected galaxies at $1.0<z<1.7$ in the SMACS J0723.3-7327 cluster field, to investigate the mechanisms of galaxy mass assembly and structural formation at cosmic noon. We develop a new…

We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder…

Subspace clustering refers to the task of finding a multi-subspace representation that best fits a collection of points taken from a high-dimensional space. This paper introduces an algorithm inspired by sparse subspace clustering (SSC) [In…

We present a novel approach for finding and evaluating structural models of small metallic nanoparticles. Rather than fitting a single model with many degrees of freedom, the approach algorithmically builds libraries of nanoparticle…

We present a novel method to estimate accurate redshifts of star-forming galaxies by measuring the flux ratio of the same emission line observed through two adjacent narrow-band filters. We apply this method to our NB912 and new NB921 data…

We present improved methods for segmenting CO emission from galaxies into individual molecular clouds, providing an update to the CPROPS algorithms presented by Rosolowsky & Leroy (2006; arXiv:astro-ph/0601706 ). The new code enables both…

We review recent high spatial resolution millimeter continuum and spectral line observations of (proto-)cluster regions. These observations reveal that the mass distribution of prestellar cores is consistent with the initial mass function…

We apply the clustering algorithm HDBSCAN on the Gaia early third data release astrometry combined with the Gaia second data release radial velocity measurements of almost 5.5 million stars to identify the local stellar kinematic…

With rapidly increasing data, clustering algorithms are important tools for data analytics in modern research. They have been successfully applied to a wide range of domains; for instance, bioinformatics, speech recognition, and financial…

Clusters of galaxies can be used for very different kinds of cosmological tests. I review a few of the methods: determination of cluster masses and dark matter content, metal enrichment and its connection to the origin of the intra-cluster…

We measure the angular clustering of galaxies from the Sloan Digital Sky Survey Data Release 7 in order to probe the spatial distribution of satellite galaxies within their dark matter halos. Specifically, we measure the angular correlation…

This article synthesizes observational data from an extensive program aimed toward a comprehensive understanding of star formation in a low-mass star-forming molecular cloud. New observations and published data spanning from the centimeter…

We have designed a powerful new algorithm to detect stellar streams in an automated and systematic way. The algorithm, which we call the STREAMFINDER, is well suited for finding dynamically cold and thin stream structures that may lie along…