Related papers: Deriving structural parameters of semi-resolved st…
Stars mostly form in groups consisting of a few dozen to several ten thousand members. For 30 years, theoretical models provide a basic concept of how such star clusters form and develop: they originate from the gas and dust of collapsing…
We study structural clustering on graphs in dynamic scenarios, where the graphs can be updated by arbitrary insertions or deletions of edges/vertices. The goal is to efficiently compute structural clustering results for any clustering…
We have developed a method for detecting clusters in large imaging surveys, based on the detection of structures in galaxy density maps made in slices of photometric redshifts. This method was first applied to the Canada France Hawaii…
This paper introduces {\em fusion subspace clustering}, a novel method to learn low-dimensional structures that approximate large scale yet highly incomplete data. The main idea is to assign each datum to a subspace of its own, and minimize…
We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances. The proposed method efficiently combines data density and geometry to…
We present a selection of candidates of clusters and protoclusters of galaxies identified in the photometric data of the HSC-SSP Wide Public Data Release 3 (PDR3), spanning the redshift range $\rm 0.1 \leq z \leq 2$. The selection method,…
Gravitational lensing by clusters of galaxies offers a powerful probe of their structure and mass distribution. Deriving a lens magnification map for a galaxy cluster is a classic inversion problem and many methods have been developed over…
We have obtained HST-NICMOS observations of five of M31's most metal rich globular clusters. These data allow photometry of individual stars in the clusters and their surrounding fields. However, to achieve our goals -- obtain accurate…
We present a description of the observations and data reduction procedures for an extensive spectroscopic and multi-band photometric study of nine high redshift, optically-selected cluster candidates. The primary goal of the survey is to…
Context. Simulating stellar dynamics in a molecular cloud environment is numerically challenging due to the strong coupling between young stars and their surrounding gas, and the large range of length and time scales. Aims. This paper is…
The clustering of galaxy clusters is a powerful cosmological tool, which can help to break degeneracies between parameters when combined with other cosmological observables. We aim to demonstrate its potential in constraining cosmological…
Star formation in molecular clouds is clumpy, hierarchically subclustered. Fractal structure also emerges in hydro-dynamical simulations of star-forming clouds. Simulating the formation of realistic star clusters with hydro-dynamical…
Quality assessments of models in unsupervised learning and clustering verification in particular have been a long-standing problem in the machine learning research. The lack of robust and universally applicable cluster validity scores often…
We present an approach that improves the search for reliable astrophysical parameters (e.g. age, mass, and distance) of differentially-reddened, pre-main sequence-rich star clusters. It involves simulating conditions related to the…
According to the current cosmological paradigm, large scale structures form hierarchically in the Universe. Clusters of galaxies grow through a continuous accretion of mass. Nevertheless, the rate and manner of mass accretion events are…
In computer vision, the estimation of the fundamental matrix is a basic problem that has been extensively studied. The accuracy of the estimation imposes a significant influence on subsequent tasks such as the camera trajectory…
Dictionary learning and sparse coding have been widely studied as mechanisms for unsupervised feature learning. Unsupervised learning could bring enormous benefit to the processing of hyperspectral images and to other remote sensing data…
Observations of galaxy clustering are made in redshift space, which results in distortions to the underlying isotropic distribution of galaxies. These redshift-space distortions (RSD) not only degrade important features of the matter…
While foundation models drive steady progress in image segmentation and diffusion algorithms compose always more realistic images, the seemingly simple problem of identifying recurrent patterns in a collection of images remains very much…
We used the dendrogram algorithm to decompose the surface density distributions of stars into hierarchical structures. These structures were tied to the multiscale structures of star clusters. A similar power-law for the mass-size relation…