Related papers: Star Cluster Classification in the PHANGS-HST Surv…

We present a new, objectively defined catalog of candidate galaxy clusters based on the galaxy catalogs from the Digitized Second Palomar Observatory Sky Survey (DPOSS). This cluster catalog, derived from the best calibrated plates in the…

We have carried out HST snapshot observations at 1.1 $\mu$m of 281 candidate strongly lensed galaxies identified in the wide-area extragalactic surveys conducted with the Herschel space observatory. Our candidates comprise systems with flux…

We evaluate the ability of Convolutional Neural Networks (CNNs) to predict galaxy cluster masses in the BAHAMAS hydrodynamical simulations. We train four separate single-channel networks using: stellar mass, soft X-ray flux, bolometric…

We have undertaken an ambitious program to visually classify all galaxies in the five CANDELS fields down to H<24.5 involving the dedicated efforts of 65 individual classifiers. Once completed, we expect to have detailed morphological…

Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…

We present the initial results of a study of globular clusters in M31, using the Faint Object Camera (FOC) on the Hubble Space Telescope (HST). The sample of objects consists of 13 clusters spanning a range of properties. Three inde-…

Hubble Space Telescope (HST) photometry is providing an extensive analysis of globular clusters (GCs). In particular, the pseudo two-colour diagram dubbed 'chromosome map (ChM)' allowed to detect and characterize their multiple populations…

We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by…

Cluster cosmology depends critically on how optical clusters are selected from imaging surveys. We compare the conditional luminosity function (CLF) and weak lensing halo masses between two different cluster samples at fixed richness,…

Traditional studies of stellar clusters in external galaxies use surface photometry and therefore focus on systems that are still bright and compact enough to be separated from the stellar background. Consequently, the latter stages of…

Despite the huge recent breakthroughs in neural networks (NNs) for artificial intelligence (specifically deep convolutional networks) such NNs do not achieve human-level performance: they can be hacked by images that would fool no human and…

We present a study of the properties of the star-cluster systems around pseudo-bulges of late-type spiral galaxies using a sample of 11 galaxies with distances from 17 to 37 Mpc. Star clusters are identified from multiband HST ACS and WFPC2…

We investigate and demonstrate the use of convolutional neural networks (CNNs) for the task of distinguishing between merging and non-merging galaxies in simulated images, and for the first time at high redshifts (i.e. $z=2$). We extract…

An automated search for star clusters close to the Galactic plane (|b| < 5 deg) was carried out on the Point Source Catalogue of the DENIS survey. 44% of the Galactic plane have been observed and calibrated. The method allowed to retrieve…

With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of millions of galaxies, spectra classification will become overwhelming for standard analysis techniques. To prepare for this challenge, we…

We present a study of the hierarchical clustering of the young stellar clusters in six local (3--15 Mpc) star-forming galaxies using Hubble Space Telescope broad band WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy…

We present a new cluster detection algorithm designed for the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) survey but with generic application to any multiband data. The method makes no prior assumptions about the…

Understanding how humans and machines learn from sparse data is central to cognitive science and machine learning. Using a species-fair design, we compare children and convolutional neural networks (CNNs) in a few-shot semi-supervised…

We study the star/galaxy classification efficiency of 13 different decision tree algorithms applied to photometric objects in the Sloan Digital Sky Survey Data Release Seven (SDSS DR7). Each algorithm is defined by a set of parameters…

We present a comprehensive analysis of strong-lensing, weak-lensing shear and magnification data for a sample of 16 X-ray-regular and 4 high-magnification galaxy clusters selected from the CLASH survey. Our analysis combines constraints…