# Traces of Galaxy Formation: Stellar populations, Dynamics and Morphology

Start year
2006
Organizational Unit
Organizing institutions

### Grants related:

General
Description

#### Welcome to the Traces of Galaxy Formation research group website.

We are a large, diverse, and very active research group aiming to provide a comprehensive picture for the formation of galaxies in the Universe. Rooted in detailed stellar population analysis, we are constantly exploring and developing new tools and ideas to understand how galaxies came to be what we now observe.

A complex star formation history, as the one expected to describe galaxy evolution, needs a multidisciplinary approach to be fully understood. Our group at the IAC consists of experienced researchers in cosmological simulations, dynamical studies, stellar populations and morphological properties of galaxies up to high redshift. We combine different approaches (e.g. observations and theory, secular and cosmological evolution studies) to obtain a complete view of the dominant mechanisms driving the evolution of galaxies.

Within this general framework, we are currently exploring three main areas of research:

1. Stellar population synthesis models
• Development of new stellar population synthesis models
• Stellar population analysis tools
• Universality of the stellar initial mass function (IMF)

2. Cosmic evolution of galaxies
• Massive galaxy evolution
• Stellar populations in different environments
• Low surface brightness science
• Machine learning and cosmological simulations

3. Evolutionary processes in nearby galaxies
• The role of black holes in the evolution of galaxies
• Surveys of nearby galaxies
• Stellar kinematics and dynamical models

If you want to get in contact or work with us, please send an email to the head of the group (Ignacio Martín-Navarro ignacio.martin [at] iac.es).

Here you can find some of our most recent highlights:

### Related publications

• A comparison between X-shooter spectra and PHOENIX models across the HR-diagram

Aims: The path towards robust near-infrared extensions of stellar population models involves the confrontation between empirical and synthetic stellar spectral libraries across the wavelength ranges of photospheric emission. Indeed, the theory of stellar emission enters all population synthesis models, even when this is only implicit in the

Lançon, A. et al.

5
2021
• sMILES: a library of semi-empirical MILES stellar spectra with variable [α/Fe] abundances

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

6
2021
• Numerical simulations of dark matter haloes produce polytropic central cores when reaching thermodynamic equilibrium

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

Sánchez Almeida, Jorge et al.

6
2021
• Fingerprints of stellar populations in the near-infrared: an optimized set of spectral indices in the JHK bands 0

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

Eftekhari, Elham et al.

6
2021
• Beyond the hubble sequence - exploring galaxy morphology with unsupervised machine learning

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

Cheng, Ting-Yun et al.

5
2021
• Investigating the projected phase space of Gaussian and non-Gaussian clusters

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

Sampaio, V. M. et al.

5
2021
• The Fornax3D project: Assembly histories of lenticular galaxies from a combined dynamical and population orbital analysis

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

Poci, A. et al.

3
2021
• The number of globular clusters around the iconic UDG DF44 is as expected for dwarf galaxies

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

Saifollahi, Teymoor et al.

4
2021
• Evaluating hydrodynamical simulations with green valley galaxies

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

Angthopo, J. et al.

4
2021
• BAYES-LOSVD: A Bayesian framework for non-parametric extraction of the line-of-sight velocity distribution of galaxies

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

Falcón-Barroso, J. et al.

2
2021
• Constraining stellar population parameters from narrow band photometric surveys using convolutional neural networks

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

Liew-Cain, Choong Ling et al.

3
2021
• Galaxies within galaxies in the TIMER survey: stellar populations of inner bars are scaled replicas of main bars

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

2
2021
• The nature of giant clumps in high-z discs: a deep-learning comparison of simulations and observations

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

Ginzburg, Omri et al.

2
2021
• A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations

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

Zanisi, Lorenzo et al.

3
2021
• The kinematics of young and old stellar populations in nuclear rings of MUSE TIMER galaxies

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

12
2020
• Galactic cirri in deep optical imaging

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

Román, Javier et al.

12
2020
• The Galaxy "Missing Dark Matter" NGC 1052-DF4 is Undergoing Tidal Disruption

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

Montes, Mireia et al.

12
2020
• Detecting outliers in astronomical images with deep generative networks

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

Margalef-Bentabol, Berta et al.

6
2020
• Stellar masses of giant clumps in CANDELS and simulated galaxies using machine learning

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

Huertas-Company, Marc et al.

9
2020
• Inside-out formation of nuclear discs and the absence of old central spheroids in barred galaxies of the TIMER survey

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