Related papers: StarcNet: Machine Learning for Star Cluster Identi…
Properties of massive galaxy clusters, such as mass abundance and concentration, are sensitive to cosmology, making cluster statistics a powerful tool for cosmological studies. However, favoring a more simplified, spherically symmetric…
Accurately distinguishing between quiescent and star-forming galaxies is essential for understanding galaxy evolution. Traditional methods, such as spectral energy distribution (SED) fitting, can be computationally expensive and may…
Forthcoming large imaging surveys such as Euclid and the Vera Rubin Observatory Legacy Survey of Space and Time are expected to find more than $10^5$ strong gravitational lens systems, including many rare and exotic populations such as…
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.…
Distinguishing galaxies as either fast or slow rotators plays a vital role in understanding the processes behind galaxy formation and evolution. Standard techniques, which are based on the $\lambda_R$-spin parameter obtained from stellar…
Context.Identification of new star cluster candidates in M31 is fundamental for the study of the M31 stellar cluster system. The machine-learning method convolutional neural network (CNN) is an efficient algorithm for searching for new M31…
We present our new, fully-automated method to detect and measure the ages of star clusters in nearby galaxies, where individual stars can be resolved. The method relies purely on statistical analysis of observations and Monte-Carlo…
Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Therefore, identifying such systems allows to…
[Abridged] Galaxy clusters are the most massive gravitationally-bound systems in the universe and are widely considered to be an effective cosmological probe. We propose the first Machine Learning method using galaxy cluster properties to…
The intracluster medium (ICM) records the history of galaxy clusters through its complex dynamical properties. To effectively interpret these properties, robust methods are needed to compare observational data with theoretical models. We…
Context. The intracluster light (ICL) comprises stars that are not bound to individual galaxies within a galaxy cluster, and it provides insights into the cluster mass distribution, evolutionary history, and dynamical state. Aims. We study…
Within the realm of image recognition, a specific category of multi-label classification (MLC) challenges arises when objects within the visual field may occlude one another, demanding simultaneous identification of both occluded and…
The detection of galaxy clusters in present and future surveys enables measuring mass-to-light ratios, clustering properties or galaxy cluster abundances and therefore, constraining cosmological parameters. We present a new technique for…
Through gravitational lensing, galaxy clusters can magnify supernovae (SNe) and create multiple images of the same SN. This enables measurements of cosmological parameters, which will be increasingly important in light of upcoming…
As a representative of a new generation of biometrics, vein identification technology offers a high level of security and convenience.Convolutional neural networks (CNNs), a prominent class of deep learning architectures, have been…
Context. Convolutional neural networks (CNNs) are widely used for automated galaxy morphological classification in large surveys. However, projection effects, image artefacts, and intrinsic degeneracies limit reliable identification of…
We present Classification of Cluster GAlaxy MEmbers (C$^2$-GaMe), a classification algorithm based on a suite of machine learning models that differentiates galaxies into orbiting, infalling, and background (interloper) populations, using…
[Abridged] We exploit the clustering of massive galaxies to perform a high efficiency imaging search for gravitational lenses. Our dataset comprises 44 fields imaged by the Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS),…
Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently.…
Star clusters are often hard to find, as they may lie in a dense field of background objects or, because in the case of embedded clusters, they are surrounded by a more dispersed population of young stars. This paper discusses four…