Related papers: Galaxy classification: A machine learning analysis…

We apply the spectral energy distribution (SED) fitting code ProSpect to multiwavelength imaging for $\sim$7,000 galaxies from the GAMA survey at $z<0.06$, in order to extract their star formation histories. We combine a parametric…

We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…

Galaxy morphology is a key parameter in galaxy evolution studies. The enormous number of galaxies which current and future surveys will observe demand of automated methods for morphological classification. Supervised learning techniques…

We use KiDS weak lensing data to measure variations in mean halo mass as a function of several key galaxy properties (namely: stellar colour, specific star formation rate, Sersic index, and effective radius) for a volume-limited sample of…

Analysing all Galaxy and Mass Assembly (GAMA) galaxies within a factor two (+/- 0.3 dex) of the stellar mass of the Milky Way (MW), there is a 11.9% chance that one of these galaxies will have a close companion (within a projected…

Rings are important and characteristic features of disc-shaped galaxies. This paper is the first in a series which re-visits galactic rings with the goals of further understanding the nature of the features and for examining their role in…

In this work we present the galaxy clustering measurements of the two DES lens galaxy samples: a magnitude-limited sample optimized for the measurement of cosmological parameters, MagLim, and a sample of luminous red galaxies selected with…

We have investigated a number of factors that can have significant impacts on the classification performance of $\gamma$-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of…

In recent years, automated, supervised classification techniques have been fruitfully applied to labeling and organizing large astronomical databases. These methods require off-line classifier training, based on labeled examples from each…

Massive Ultracompact Galaxies (MUGs) are common at z=2-3, but very rare in the nearby Universe. Simulations predict that the few surviving MUGs should reside in galaxy clusters, whose large relative velocities prevent them from merging,…

We present a new approach based on Supervised Machine Learning (SML) algorithms to infer key physical properties of galaxies (density, metallicity, column density and ionization parameter) from their emission line spectra. We introduce a…

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),…

We present the first cosmological measurement derived from a galaxy density field subject to a `clipping' transformation. By enforcing an upper bound on the galaxy number density field in the Galaxy and Mass Assembly survey (GAMA),…

This is the first in a series of papers studying the astrophysics and cosmology of massive, dynamically relaxed galaxy clusters. Here we present a new, automated method for identifying relaxed clusters based on their morphologies in X-ray…

Morphology is a powerful indicator of a galaxy's dynamical and merger history. It is strongly correlated with many physical parameters, including mass, star formation history and the distribution of mass. The Galaxy Zoo project collected…

We investigate how different galaxy properties - luminosities in u, g, r, J, K-bands, stellar mass, star formation rate and specific star formation rate trace the environment in the local universe. We also study the effect of survey flux…

The classification of galaxy morphology is a hot issue in astronomical research. Although significant progress has been made in the last decade in classifying galaxy morphology using deep learning technology, there are still some…

We present a novel graph-based machine learning classifier for identifying the dark matter cosmic web environments of galaxies. Large galaxy surveys offer comprehensive statistical views of how galaxy properties are shaped by large-scale…

We used 1627 faint (15.5< R<23) stars in five fields of the Calar Alto Deep Imaging Survey (CADIS) to estimate the structure parameters of the Galaxy. The results were derived by applying two complementary methods: first by fitting the…

We have generated complementary halo mass estimates for all groups in the Galaxy And Mass Assembly Galaxy Group Catalogue (GAMA G3Cv1) using a modified caustic mass estimation algorithm, originally developed by Diaferio & Geller (1997). We…