ﻻ يوجد ملخص باللغة العربية
In the last years, Astroinformatics has become a well defined paradigm for many fields of Astronomy. In this work we demonstrate the potential of a multidisciplinary approach to identify globular clusters (GCs) in the Fornax cluster of galaxies taking advantage of multi-band photometry produced by the VLT Survey Telescope using automatic self-adaptive methodologies. The data analyzed in this work consist of deep, multi-band, partially overlapping images centered on the core of the Fornax cluster. In this work we use a Neural-Gas model, a pure clustering machine learning methodology, to approach the GC detection, while a novel feature selection method ($Phi$LAB) is exploited to perform the parameter space analysis and optimization. We demonstrate that the use of an Astroinformatics based methodology is able to provide GC samples that are comparable, in terms of purity and completeness with those obtained using single band HST data (Brescia et al. 2012) and two approaches based respectively on a morpho-photometric (Cantiello et al. 2018b) and a PCA analysis (DAbrusco et al. 2015) using the same data discussed in this work.
It has long been argued that the radial distribution of globular clusters (GCs) in the Fornax dwarf galaxy requires its dark matter halo to have a core of size $sim 1$ kpc. We revisit this argument by investigating analogues of Fornax formed in E-MOS
We investigate the structural properties of cluster and group galaxies by studying the Fornax main cluster and the infalling Fornax A group, exploring the effects of galaxy preprocessing in this showcase example. Additionally, we compare the structur
This paper continues the series of the Fornax Deep Survey (FDS). Following the previous studies dedicated to extended Fornax cluster members, we present the catalogs of compact stellar systems in the Fornax cluster as well as extended background sour
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, providing se
Globular clusters (GCs) are found ubiquitously in massive galaxies and due to their old ages, they are regarded as fossil records of galaxy evolution. Spectroscopic studies of GC systems are often limited to the outskirts of galaxies, where GCs stand