相关论文: UPMASK: unsupervised photometric membership assign…
This review summarizes popular unsupervised learning methods, and gives an overview of their past, current, and future uses in astronomy. Unsupervised learning aims to organise the information content of a dataset, in such a way that…
We review progress in numerical simulations of star cluster formation. These simulations involve the bottom-up assembly of clusters through hierarchical mergers, which produces a fractal stellar distribution at young (~0.5 Myr) ages. The…
We have used Washington photometry for 90 star cluster candidates of small angular size -typically ~ 11" in radius- distributed within nine selected regions in the inner disc of the Large Magellanic Cloud (LMC) to disentangle whether they…
The integrated spectro-photometric properties of star clusters are subject to large cluster-to-cluster variations. They are distributed in non trivial ways around the average properties predicted by standard population synthesis models.…
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…
We study the capability of the UBVRI photometric system to quantify star clusters in terms of age, metallicity, and color excess by their integrated photometry. The well known age-metallicity-extinction degeneracy was analyzed for various…
We present a study of 7 southern open clusters based on UBVRI CCD photomety (Johnsons-Cousins system) and Gaia DR2 data. Dias 4, Dias 6 and four other clusters had UBVRI photometric observations determined for the first time. From the…
A limitation of many clustering algorithms is the requirement to tune adjustable parameters for each application or even for each dataset. Some techniques require an \emph{a priori} estimate of the number of clusters while density-based…
Accurate land cover segmentation of spectral images is challenging and has drawn widespread attention in remote sensing due to its inherent complexity. Although significant efforts have been made for developing a variety of methods, most of…
High resolution spectral models for simple stellar populations (SSP) developed in the past few years have become a standard ingredient in studies of stellar population of galaxies. As more such models become available, it becomes…
Point cloud upsampling aims to generate dense and uniformly distributed point sets from sparse point clouds. Existing point cloud upsampling methods typically approach the task as an interpolation problem. They achieve upsampling by…
Subspace clustering, the task of clustering high dimensional data when the data points come from a union of subspaces is one of the fundamental tasks in unsupervised machine learning. Most of the existing algorithms for this task require…
We measure the velocity dispersions of six, galactic globular clusters using spatially integrated spectra, to test for the effects of internal dynamical evolution in the stellar mass-to-light ratios, Upsilon_*, of star clusters. In…
The physical properties of star cluster populations offer valuable insights into their birth, evolution, and disruption. However, individual stars in clusters beyond the nearest neighbours of the Milky Way are unresolved, forcing analyses…
The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime…
Hyperspectral image unmixing is an inverse problem aiming at recovering the spectral signatures of pure materials of interest (called endmembers) and estimating their proportions (called abundances) in every pixel of the image. However, in…
Variations in the photometric parameters of stellar systems as a function of their evolution and the stellar populations comprising them are investigated. A set of seven evolutionary models with an exponential decrease in the star-formation…
We investigate the stellar populations of galaxies in clusters at different dynamical stages, aiming to identify possible effects of the relaxation state of the cluster or subcluster on the star formation histories of its galaxies. We have…
During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…
In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into…