We present a new library of semi-empirical stellar spectra that is based on the empirical Medium resolution Isaac Newton Library of Empirical Spectra (MILES) library. A new, high-resolution library of theoretical stellar spectra is generated that is specifically designed for use in stellar population studies. We test these models across their full

Self-gravitating astronomical objects often show a central plateau in the density profile (core) whose physical origin is hotly debated. Cores are theoretically expected in N-body systems of maximum entropy, however, they are not present in the canonical N-body numerical simulations of cold dark matter (CDM). Our work shows that despite this

Stellar population studies provide unique clues to constrain galaxy formation models. So far, detailed studies based on absorption line strengths have mainly focused on the optical spectral range although many diagnostic features are present in other spectral windows. In particular, the near-infrared (NIR) can provide a wealth of information about

We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantized variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (1) consideration of the clustering performance simultaneously when learning features from images; (2

By way of the projected phase space (PPS), we investigate the relation between galaxy properties and cluster environment in a subsample of groups from the Yang catalogue. The sample is split according to the Gaussianity of the velocity distribution in the group into Gaussian (G) and non-Gaussian (NG). Our sample is limited to massive clusters with

In order to assess the impact of the environment on the formation and evolution of galaxies, accurate assembly histories of such galaxies are needed. However, these measurements are observationally difficult owing to the diversity of formation paths that lead to the same present-day state of a galaxy. In this work, we apply a powerful new technique

There is a growing consensus that the vast majority of ultradiffuse galaxies (UDGs) are dwarf galaxies. However, there remain a few UDGs that seem to be special in terms of their globular cluster (GC) systems. In particular, according to some authors, certain UDGs exhibit large GC populations when compared to expectations from their stellar (or

We test cosmological hydrodynamical simulations of galaxy formation regarding the properties of the blue cloud (BC), green valley (GV), and red sequence (RS), as measured on the 4000Å break strength versus stellar mass plane at z = 0.1. We analyse the RefL0100N1504 run of EAGLE and the TNG100 run of IllustrisTNG project, by comparing them with the

We introduce BAYES-LOSVD, a novel implementation of the non-parametric extraction of line-of-sight velocity distributions (LOSVDs) in galaxies. We employed Bayesian inference to obtain robust LOSVDs and associated uncertainties. Our method relies on a principal component analysis to reduce the dimensionality on the set of templates required for the

Upcoming large-area narrow band photometric surveys, such as Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS), will enable us to observe a large number of galaxies simultaneously and efficiently. However, it will be challenging to analyse the spatially resolved stellar populations of galaxies from such big data to

Inner bars are frequent structures in the local Universe and thought to substantially influence the nuclear regions of disc galaxies. In this study we explore the structure and dynamics of inner bars by deriving maps and radial profiles of their mean stellar population content and comparing them to previous findings in the context of main bars. To

We use deep learning to explore the nature of observed giant clumps in high-redshift disc galaxies, based on their identification and classification in cosmological simulations. Simulated clumps are detected using the 3D gas and stellar densities in the VELA zoom-in cosmological simulation suite, with ${\sim}25\ \rm {pc}$ maximum resolution

Hydrodynamical simulations of galaxy formation and evolution attempt to fully model the physics that shapes galaxies. The agreement between the morphology of simulated and real galaxies, and the way the morphological types are distributed across galaxy scaling relations are important probes of our knowledge of galaxy formation physics. Here, we

Context. Studying the stellar kinematics of galaxies is a key tool in the reconstruction of their evolution. However, the current measurements of the stellar kinematics are complicated by several factors, including dust extinction and the presence of multiple stellar populations. Aims: We use integral field spectroscopic data of four galaxies from

The ubiquitous presence of Galactic cirri in deep optical images represents a major obstacle to study the low surface brightness features of extragalactic sources. To address this issue, we have explored the optical properties of cirri using g, r, i, and z bands in the Sloan Digital Sky Survey (SDSS) Stripe82 region. Using state-of-the-art, custom

The existence of long-lived galaxies lacking dark matter represents a challenge to our understanding of how galaxies form. Here, we present evidence that explains the lack of dark matter in one such galaxy: NGC 1052-DF4. Deep optical imaging of the system has detected tidal tails in this object caused by its interaction with its neighboring galaxy

With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging data sets. The main advantage of such generative models is that they are able to learn complex representations

A significant fraction of high redshift star-forming disc galaxies are known to host giant clumps, whose nature and role in galaxy evolution are yet to be understood. In this work, we first present a new method based on neural networks to detect clumps in galaxy images. We use this method to detect clumps in the rest-frame optical and UV images of

The centres of disc galaxies host a variety of structures built via both internal and external processes. In this study, we constrain the formation and evolution of these central structures, in particular, nuclear rings and nuclear discs, by deriving maps of mean stellar ages, metallicities, and [α/Fe] abundances. We use observations obtained with

The central regions of disc galaxies hold clues to the processes that dominate their formation and evolution. To exploit this, the TIMER project has obtained high signal-to-noise and spatial resolution integral-field spectroscopy data of the inner few kpc of 21 nearby massive barred galaxies, which allows studies of the stellar kinematics in their