Related papers: Multidimensional Data Driven Classification of Emi…
We study the spectral classification of emission-line galaxies as star-forming galaxies or Active Galactic Nuclei (AGNs). From the Sloan Digital Sky Survey (SDSS) high quality data, we define an improved classification to be used for high…
Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…
In recent decades, large-scale sky surveys such as Sloan Digital Sky Survey (SDSS) have resulted in generation of tremendous amount of data. The classification of this enormous amount of data by astronomers is time consuming. To simplify…
In this paper we present a classification of emission-line galaxies at intermediate and high redshifts (0.52.5 for near-infrared spectra), using the Dn(4000) index as a supplementary diagnostic. Our goal is to complement the diagnostic…
In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…
We invoke a Gaussian mixture model (GMM) to jointly analyse two traditional emission-line classification schemes of galaxy ionization sources: the Baldwin-Phillips-Terlevich (BPT) and $\rm W_{H\alpha}$ vs. [NII]/H$\alpha$ (WHAN) diagrams,…
We outline here the next generation of cluster-finding algorithms. We show how advances in Computer Science and Statistics have helped develop robust, fast algorithms for finding clusters of galaxies in large multi-dimensional astronomical…
Line intensity mapping is emerging as a novel method that can measure the collective intensity fluctuations of atomic/molecular line emission from distant galaxies. Several observational programs with various wavelengths are ongoing and…
I review here past and present research on clusters and groups of galaxies within the Sloan Digital Sky Survey (SDSS). In particular, I discuss the C4 algorithm which is designed to search for clusters within a 7-dimensional data-space,…
As language models become more general purpose, increased attention needs to be paid to detecting out-of-distribution (OOD) instances, i.e., those not belonging to any of the distributions seen during training. Existing methods for…
Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover…
We present a sample of low-redshift (z<0.133) candidates for extremely low-metallicity star-forming galaxies with oxygen abundances 12+logO/H<7.4 selected from the Data Release 14 (DR14) of the Sloan Digital Sky Survey (SDSS). Three methods…
Three methods for detecting and characterizing structure in point data, such as that generated by redshift surveys, are described: classification using self-organizing maps, segmentation using Bayesian blocks, and density estimation using…
We introduce a novel galaxy classification methodology based on the visible spectra of a sample of over 68,000 nearby ($z\leq 0.1$) Sloan Digital Sky Survey lenticular (S0) galaxies. Unlike traditional diagnostic diagrams, which rely on a…
The morphological classification of galaxies is considered a relevant issue and can be approached from different points of view. The increasing growth in the size and accuracy of astronomical data sets brings with it the need for the use of…
We present new quantitative classification methods for emission-line galaxies, which are specially designed to be used in deep galaxy redshift surveys. A good segregation between starbursts and active galactic nuclei, i.e. Seyferts 2s and…
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…
Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies.…
We explore the capability of deep learning to classify cosmic structures. In cosmological simulations, cosmic volumes are segmented into voids, sheets, filaments and knots, according to the distribution and kinematics of dark matter (DM),…
Clustering procedures suitable for the analysis of very high-dimensional data are needed for many modern data sets. In model-based clustering, a method called high-dimensional data clustering (HDDC) uses a family of Gaussian mixture models…