Resumen:

Globular Clusters (GCs) are directly correlated to the mass build up of a galaxy, being related to the halo creation, the formation of the bulge, or subsequent encounters. In galaxy clusters, GCs can be also bound to the cluster potential, being stripped from the infalling galaxies. They are therefore precious to study the cluster environment as well as group pre-processing. We carried out a photometric study of GCs in the Fornax Cluster using the 12 bands in the 2x2 square-degree field of view of the S-PLUS data. In this study we pretend to analyze the 98 S-PLUS pointings covering the Fornax Cluster for searching GC candidates. Studying the properties of spectroscopically confirmed GCs, we have designed a method to select GC candidates using structural and photometric parameters, as well as SED fitting techniques using the 12-bands from S-PLUS.

Resumen:

Type II supernovae (SNe II) have been extensively investigated as cosmological probes, both as alternatives to and independently of Type Ia supernovae (SNe Ia). However, SNe II exhibit a Hubble diagram dispersion of approximately 12%, higher than SNe Ia. To address this issue, we explored methods to improve the accuracy of the Hubble diagram for SNe II. In the case of SNe Ia, the luminosity dependence on the host-galaxy mass has been shown to reduce the dispersion in the Hubble diagram by around 1%. Motivated by this, we selected host galaxies of SNe II from the Dark Energy Survey (DES) to investigate whether SNe II follow a similar trend. We utilized spectra from our observation programs at VLT/ESO and Magellan/LCO to calculate the σ* (stellar velocity dispersions) of the selected galaxies to achieve our goal. To determine the characteristic luminosity of SNe II, we employed the photometric color method (PCM) introduced by Jaeger et al. (2015, 2017b). Using the DES i'-band light curves, we calculated the luminosity of SN II. During this presentation, we will share the outcomes of our investigation into possible correlations between σ* values and SN II luminosity. The next step would be to evaluate whether the connection could serve as a scale relation to calibrate the characteristic brightness of SNe II, hoping to reduce the scatter in the Hubble diagram.

Resumen:

Globular clusters (GCs) are stellar systems relics. In the core of a galaxy cluster such as Fornax, they might reside either in the gravitational potential of their parent galaxies or in the galaxy cluster, intra-cluster medium (ICM). The scaling relations of GC populations with respect to the environment they inhabit provide evidence in the assembly history of the galaxy cluster.\\ We use the Next Generation Fornax Survey (NGFS) that maps the Fornax cluster up to its virial radius (1.4Mpc), in the optical with u'g'i' (DECam) and Near-infrared with JKs (VIRCam). This pilot sample covers the Fornax core region (r $\sim 350$kpc $\sim 3deg^2$) with u'g'i'JKs psf photometry. To have the cleanest photometric selection, we implement a supervised machine-learning method (support vector machine, SVM) for source classification. More than 1500 objects were selected as Fornax GCs. We compute individual age, metallicity and mass of GCs using color, magnitudes and simple stellar population models.\\ In this talk, we will present the analysis and results of the GC population in the Fornax core region, in terms of color and mass distributions, fractions of metal poor and metal rich and old vs “young” GCs and the spatial distributions of the above properties, together with their scaling relations for their host galaxy and for the galaxy core cluster overall.\\ One of the many interesting results of this research, is about the GC spatial distribution in terms of metallicity sub-populations: a large number of GCs with intermediate-metallicity are preferentially located along the East-West direction of Fornax, centered on NGC 1399, and the most metal-rich GCs are concentrated in the vicinity of NGC 1399 and the brightest Fornax galaxies. These different distributions constrain the mass assembly history of the Fornax cluster. More evidence will come soon, when expanding this methodology to the survey area, Fornax virial radius.

Resumen:

The bars are elongated central structure composed of gas and stars, frequent in Local Universe - present in $\sim 65\% $ of spirals. Bars strongly transforms its host galaxy, causing intense movement of gas and stars, breaking the axisymmetric disk dynamics and affecting its evolution. We study the stellar mass in this structure, and how it might influence the galaxy as a whole. We use 371 barred galaxies from the Spitzer Survey of Stellar Structure in Galaxies ($S^4G$; consists of imaging in the 3.6 and 4.5 $\mu m$ bands for more than 2300 nearby, large and bright galaxies). We choose the mid-infrared to focus on lower mass stars and avoid obscuration caused by dust. The GALFIT-based decomposition in the 3.6$\mu m$ band is publicly available. This 2D image decomposition fits different stellar components — bulge, disk, bar, nuclear point source and/or secondary disk — resulting in a final decomposition model with 1-4 components for each galaxy. Knowing the flux contained in each component from the decomposition, we can estimate the stellar mass of such structures. We compared samples of galaxies with different global masses and found that there seems to be a trend towards more massive galaxies, and with a bulge in the model, with bars with a higher relative mass. We found that bars are less present in lower mass galaxies ($M_* < 10^9 M_{\odot} $, about 38\%). This is consistent with bar fraction studies that suggest that lower-mass galaxies are still acquiring and growing their bars. We are working to include the 4.5$\mu m$ band, with a multi-band decomposition of the bands 3.6 and 4.5$\mu m$ using a GALFIT's version. With our multi-band decomposition, we will be able to draw more accurate conclusions on how the stellar mass in the bar influences other properties of the host galaxy.

Resumen:

This study explores a new method for estimating stellar population parameters from galaxy photometry with the aid of Machine Learning techniques. By analyzing data from the S-PLUS photometric survey, we compared different regression models, including Random Forest, XGboost, and Neural Networks, to identify the most suitable estimator. To address challenges like missing data and calibrations, we developed a robust synthetic training sample that closely resembled real observations. The Neural Network model exhibited promising results, with a low error of 0.06 dex for stellar mass estimation [attached image] and accurate predictions of nine other stellar population parameters. The approach allows for constraints-free, fast parameter estimation compared to traditional methods constrained by redshift. As a testament to the effectiveness of our approach, we present new data analysis of nearby galaxy clusters, such as Fornax and Abell 85, showcasing the potential of our method in studying diverse galactic populations. We plan to apply this model to the S-PLUS and CHANCES surveys, with a focus on obtaining previously unseen galaxy parameters and intend to make this data public. This novel approach has the potential to significantly advance our understanding of stellar populations and contribute to the progress of galaxy research.

Resumen:

Galaxy spectral energy distributions (SEDs) are well known for being a key factor of galaxy studies. When analyzing them, we can understand their properties and evolution. This work aims to look for alternative methods to analyse and visualise galaxy SEDs in a new manner by applying machine learning techniques. We implement unsupervised machine learning methods to order the zoo of synthetic single stellar population (SSP) of galaxy SEDs into a self-organizing map (SOM). The catalogue of synthetic SSP is generated using Flexible Stellar Population Synthesis (FSPS), where the galaxies are sampled at a fixed redshift of $z=0.496$ with metallicities in range of [-0.5, 0.2] $log(Z/Z_{\odot})$. To achieve this, we build a compressed representation for each galaxy SED by implementing an autoencoder algorithm, which is used as the input for the SOM. As galaxy SEDs are grouped by similarity, they will be assigned to a particular category (or node), where neighbouring nodes represent similar spectra. Finally, we prove that this implementation is an efficient method to derive physical properties of galaxies, providing insights and techniques that will be useful for photometric redshifts and more generally to build a model of the galaxy population useful for future cosmological galaxy surveys.

Resumen:

Low-surface-brightness galaxies (LSBGs) are defined as galaxies with a central surface brightness fainter than the night sky. They consist of an exceptional population of galaxies challenging to observe and difficult to characterize since they do not show any clear patterns but populate a wide range of properties. Moreover, it is assumed that this population even dominates the volume density of galaxies in the Universe. As a matter of fact, little is known about their evolution. Whether low-surface brightness features are predestined by internal factors or are rather a manifestation of random or unexpected events in their history remains an open question. Furthermore, studies realized with numerical techniques featuring LSBGs are highly under-represented in the literature compared to other populations of galaxies. All in all, we find it of extraordinary importance and necessity to account for these shortcomings. Therefore, we use hydro-dynamically simulated galaxies from the EAGLE-project and test various selection strategies in order to extract a sample of LSBGs. Thereby we use publicly available properties and general characteristics such as star formation densities, luminosities, and optical radii and study their large-scale distribution in the cosmic web as well as their redshift evolution. The main goal of this work within our project called \emph{Hidden Figures} is to provide detailed information on the evolution of properties related to galaxies which show low-surface brightness characteristics and to investigate their evolution in a cosmological context. (Title inspired by a book of C. Criado Perez)