Related papers: Evaluating the feasibility of interpretable machin…

The central region of the Virgo cluster of galaxies contains thousands of globular clusters (GCs), an order of magnitude more than the numbers found in the Local Group. Relics of early star formation epochs in the universe, these GCs also…

In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of…

Deep imaging data from the Keck II telescope are employed to study the globular cluster (GC) populations in the cores of six rich Abell clusters. The sample includes A754, A1644, A2124, A2147, A2151, and A2152, and spans the redshift range…

The evolutionary classification of molecular clumps, crucial for understanding star formation, is commonly based on human-assigned categories derived from infrared (IR) emission and well-established morphological criteria. However, due to…

The study of outer halo globular cluster (GC) populations can give insight into galaxy merging, globular cluster accretion and the origin of GCs. We use archival Subaru Hyper Suprime-Cam (HSC) data in concert with space-based GALEX, IRAC…

The photometric properties that we could observe for Extra-Galactic Globular Clusters (EGGCs) are the integrated light of the system and for nearby EGGCs it also is possible to measure both half-light radii and the color spatial…

Globular clusters (GCs) are powerful tracers of galaxy assembly, frequently used to identify accreted substructure and reconstruct hierarchical merger histories. With advances in GC formation models and cosmological simulations, we can now…

The mass distribution and chemical composition of globular cluster (GC) systems preserve fossil record of the early stages of galaxy formation. The observed distribution of GC colors within massive early-type galaxies in the ACS Virgo…

The Galactic globular clusters (GGCs) located towards the Galactic bulge have generally been excluded from large-scale photometric GGC surveys due to severe total and differential extinction. Here, we present an overview of a Hubble Space…

Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms,…

We present a machine learning framework to simulate realistic galaxies for the Euclid Survey. The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions…

Although the James Webb Space Telescope (JWST) has received much attention for its ability to search deeper into the cosmos than ever before, it also enhances our capability to study objects closer to us in the Universe. We apply a…

We argue that globular clusters (GCs) are good candidates for gravitational lenses in explaining quasar-galaxy associations. The catalog of associations (Bukhmastova 2001) compiled from the LEDA catalog of galaxies (Paturel 1997) and from…

Hubble Space Telescope ACS/WFC data in (B,I) are used to investigate the globular cluster populations around 6 gE galaxies ~40 Mpc distant. The total comprises a sample of ~8000 high-probability globular clusters. PSF-convolved King-model…

We outline a method to measure the detailed chemical composition of extragalactic (unresolved) globular clusters (GCs) from echelle spectra of their integrated light. Our goal is to use this method to measure abundance patterns of GCs in…

This paper surveys visual methods of explainability of Machine Learning (ML) with focus on moving from quasi-explanations that dominate in ML to domain-specific explanation supported by granular visuals. ML interpretation is fundamentally a…

We present a photometric analysis of globular clusters (GCs) in the massive galaxy cluster MACS J0416.1-2403 (z = 0.397) using deep JWST/NIRCam imaging from the PEARLS program. PSF photometry was performed in the short wavelength filters…

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…

In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and…

The environment plays a critical role in galaxy evolution, with galaxy clusters and their infall regions offering diverse conditions that shape galaxies before they enter the dense cluster core, a process known as ``pre-processing''.…