arXiv subjects

T. C. Moura

Publications and source records attributed to T. C. Moura.

4 recordsLinked to original sources

arXiv2022

Abundance analysis of APOGEE spectra for 58 metal-poor stars from the bulge spheroid

The central part of the Galaxy host a multitude of stellar populations, including the spheroidal bulge stars, stars moved to the bulge through secular evolution of the bar, inner halo, inner thick disk, inner thin disk, as well as debris from past accretion events. We identified a sample of 58 candidate stars belonging to the stellar population of the spheroidal bulge, and analyse their abundances. The present calculations of Mg, Ca, and Si lines are in agreement with the APOGEE-ASPCAP abundances, whereas abundances of C, N, O, and Ce are re-examined. We find normal $α$-element enhancements in oxygen, similar to magnesium, Si, and Ca abundances, which are typical of other bulge stars surveyed in the optical in Baade's Window. The enhancement of [O/Fe] in these stars suggests that they do not belong to accreted debris. No spread in N abundances is found, and none of the sample stars is N-rich, indicating that these stars are not second generation stars originated in globular clusters. Ce instead is enhanced in the sample stars, which points to an s-process origin such as due to enrichment from early generations of massive fast rotating stars, the so-called spinstars

astro-ph.SR↗

arXiv2019

Machine and Deep Learning Applied to Galaxy Morphology -- A Comparative Study

Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable morphological estimate from galaxy images. Here, we investigate how to substantially improve the galaxy classification within large datasets by mimicking human classification. We combine accurate visual classifications from the Galaxy Zoo project with machine and deep learning methodologies. We propose two distinct approaches for galaxy morphology: one based on non-parametric morphology and traditional machine learning algorithms; and another based on Deep Learning. To measure the input features for the traditional machine learning methodology, we have developed a system called CyMorph, with a novel non-parametric approach to study galaxy morphology. The main datasets employed comes from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). We also discuss the class imbalance problem considering three classes. Performance of each model is mainly measured by Overall Accuracy (OA). A spectroscopic validation with astrophysical parameters is also provided for Decision Tree models to assess the quality of our morphological classification. In all of our samples, both Deep and Traditional Machine Learning approaches have over 94.5% OA to classify galaxies in two classes (elliptical and spiral). We compare our classification with state-of-the-art morphological classification from literature. Considering only two classes separation, we achieve 99% of overall accuracy in average when using our deep learning models, and 82% when using three classes. We provide a catalog with 670,560 galaxies containing our best results, including morphological metrics and classification.

astro-ph.IM↗

arXiv2019

The Mass Density Profile and Star Formation History of Gaussian and Non-Gaussian Clusters

This paper is the third of a series in which we investigate the discrimination between Gaussian (G) and Non-Gaussian (NG) clusters, based on the velocity distribution of the member galaxies. We study a sample of 177 groups from the Yang catalog in the redshift interval of 0.03 $\le$ z $\le$ 0.1 and masses $\ge$ 10$^{14} \rm M_{\odot}$. Examining the projected stellar mass density distributions of G and NG groups we find strong evidence of a higher infall rate in the outskirts of NG groups over the G ones. There is a 61\% excess of faint galaxies in NGs when contrasted with G groups, when integrating $\rm from ~ 0.8 ~to~ 2.0R/R_{200}$. The study of the Star Formation History (SFH) of ellipticals and spirals in the three main regions of the Projected Phase Space (PPS) reveals also that the star formation in faint spirals of NG groups is significantly different from their counterpart in the G groups. The assembled mass for Faint spirals varies from 59\% at 12.7 Gyr to 75\% at 8.0 Gyr, while in G systems this variation is from 82\% to 91\%. This finding may also be interpreted as a higher infall rate of gas rich systems in NG groups. This accretion process through the filaments, disturbing the velocity distribution and modifying not only the stellar population of the incoming galaxies but also their SFH, should be seriously considered in modelling galaxy evolution.

astro-ph.GA↗

arXiv2017

Improving galaxy morphology with machine learning

This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy (H) and gradient pattern analysis parameter (GA). Except concentration, all parameters performed a image segmentation pre-processing. For supervision and to compute confusion matrices, we used as true label the galaxy classification from GalaxyZoo. With a 48145 objects dataset after preprocessing (44760 galaxies labeled as S and 3385 as E), we performed experiments with Support Vector Machine (SVM) and Decision Tree (DT). Whit a 1962 objects balanced dataset, we applied K- means and Agglomerative Hierarchical Clustering. All experiments with supervision reached an Overall Accuracy OA >= 97%.

astro-ph.GA↗