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A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task which suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic…

Materials exhibiting a substitutional disorder such as multicomponent alloys and mixed metal oxides/oxyfluorides are of great importance in many scientific and technological sectors. Disordered materials constitute an overwhelmingly large…

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…

Deep learning solutions of the salient object detection problem have achieved great results in recent years. The majority of these models are based on encoders and decoders, with a different multi-feature combination. In this paper, we show…

We present a study on galaxy detection and shape classification using topometric clustering algorithms. We first use the DBSCAN algorithm to extract, from CCD frames, groups of adjacent pixels with significant fluxes and we then apply the…

We present a new two-color algorithm, the "Stellar Bump Sequence" (SBS), that is optimized for robustly identifying candidate high-redshift galaxy clusters in combined wide-field optical and mid-infrared (MIR) data. The SBS algorithm is a…

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…

Semi-supervised clustering techniques have emerged as valuable tools for leveraging prior information in the form of constraints to improve the quality of clustering outcomes. Despite the proliferation of such methods, the ability to…

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…

There are only two methods for estimating the mass distribution in the outer regions of galaxy clusters, where virial equilibrium does not hold: weak gravitational lensing and identification of caustics in redshift space. For the first…

In this paper, we propose a novel, effective and simpler end-to-end image clustering auto-encoder algorithm: ICAE. The algorithm uses PEDCC (Predefined Evenly-Distributed Class Centroids) as the clustering centers, which ensures the…

We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift…

In this work we present combined optical and X-ray cluster detection methods in an area near the North Galactic Pole area, previously covered by the SDSS and 2dF optical surveys. The same area has been covered by shallow ($\sim 1.8$…

Machine-learning methods are increasingly applied to astronomical surveys, providing powerful tools for detecting and studying galaxy clusters. We investigate the mass-redshift properties and completeness of the ComPACT galaxy cluster…

The Sloan Digital Sky Survey (SDSS) will provide a complete imaging and spectroscopic survey of the high-latitude northern sky. The 2D survey will image the sky in five colors and will contain nearly 5 x 107 galaxies to g ~ 23m. The…

We present a catalog of 799 clusters of galaxies in the redshift range z_est = 0.05 - 0.3 selected from ~400 deg^2 of early SDSS commissioning data along the celestial equator. The catalog is based on merging two independent selection…

We present optimized source galaxy selection schemes for measuring cluster weak lensing (WL) mass profiles unaffected by cluster member dilution from the Subaru Hyper Suprime-Cam Strategic Survey Program (HSC-SSP). The ongoing HSC-SSP…

Determining cosmological parameters with high precision, as well as resolving current tensions in their values derived from low and high redshift probes, is one of the main objectives of the new generation of cosmological surveys. The…

Based on the photometric redshift catalog of Zou H. et al. (2019), we apply a fast clustering algorithm to identify 540,432 galaxy clusters at $z\lesssim1$ in the DESI legacy imaging surveys, which cover a sky area of about 20,000 deg$^2$.…

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…