arXiv subjects

C. de Sá-Freitas

Publications and source records attributed to C. de Sá-Freitas.

4 recordsLinked to original sources

arXiv2025

The GECKOS Survey: revealing the formation history of a barred galaxy via structural decomposition and resolved spectroscopy

Disentangling the (co-)evolution of individual galaxy structural components remains a difficult task, owing to the inability to cleanly isolate light from spatially overlapping components. In this pilot study of PGC\,044931, observed as part of the GECKOS survey, we utilise a VIRCAM $H$-band image to decompose the galaxy into five photometric components, three of which dominate by contributing $>50\%$ of light in specific regions: a main disc, a boxy/peanut bulge, and a nuclear disc. When the photometric decompositions are mapped onto MUSE observations, we find remarkably good separation in stellar kinematic space. All three structures occupy unique locations in the parameter space of the ratio of the light-weighted stellar line-of-sight mean velocity and velocity dispersion ($\rm{V}_{\star}/σ_{\star}$), and the high-order stellar skew ($h_{3}$). These clear and distinct kinematic behaviours allow us to make inferences about the formation histories of the individual components from observations of the mean stellar ages and metallicities of the three components. A clear story emerges: the main disc built over a sustained and extended star formation phase, possibly partly fuelled by gas from a low-metallicity reservoir. Early on, that disc formed a bar that buckled and subsequently formed a nuclear disc in multiple and enriched star-formation episodes. This result is an example of how careful photometric decompositions, combined with spatially well-resolved stellar kinematic information, can help separate out age-metallicity relations of different components and therefore disentangle the formation history of a galaxy. The results of this pilot study can be extended to a differential study of all GECKOS survey galaxies to assert the true diversity of Milky Way-like galaxies.

astro-ph.GA↗

arXiv2025

The GECKOS Survey: Identifying kinematic sub-structures in edge-on galaxies

The vertical evolution of galactic discs is governed by the sub-structures within them. We examine the diversity of kinematic sub-structure present in the first 12 galaxies observed from the GECKOS survey, a VLT/MUSE large programme providing a systematic study of 36 edge-on, Milky Way-mass disc galaxies. Employing the nGIST analysis pipeline, we derive the mean line-of-sight stellar velocity ($V_{\star}$), velocity dispersion ($σ_{\star}$), skew ($h_{3}$), and kurtosis ($h_{4}$) for the sample, and examine 2D maps and 1D line profiles. Visually, the majority of this sample (8/12) are found to possess boxy-peanut bulges and host the corresponding kinematic structure predicted for stellar bars viewed in projection. Four galaxies exhibit strong evidence for the presence of nuclear discs, including central $h_{3}$-$V_{\star}$ sign mismatch, `croissant'-shaped central depressions in $σ_{\star}$ maps, strong gradients in $h_{3}$, and positive $h_{4}$ plateaus over the expected nuclear disc extent. The strength of the $h_{3}$ feature corresponds to the size of the nuclear disc, measured from the $h_{3}$ turnover radius. We can explain the features within the kinematic maps of all sample galaxies via disc structure(s) alone. We do not find any need to invoke the existence of dispersion-dominated bulges. Obtaining the specialised data products for this paper and the broader GECKOS survey required significant development of existing integral field spectroscopic (IFS) analysis tools. Therefore, we also present the nGIST pipeline: a modern, sophisticated, and easy-to-use pipeline for the analysis of galaxy IFS data. We conclude that the variety of kinematic sub-structures seen in GECKOS galaxies requires a contemporary view of galaxy morphology, expanding on the traditional view of galaxy structure, and uniting the kinematic complexity observed in the Milky Way with the extragalactic.

astro-ph.GA↗

arXiv2020

Women in academia: a warning on selection bias in gender studies from the astronomical perspective

The recent paper by AlShebli et al. (2020) investigates the impact of mentorship in young scientists. Among their conclusions, they state that female protégés benefit more from male than female mentorship. We herein expose a critical flaw in their methodological design that is a common issue in Astronomy, namely "selection biases". An effect that if not treated properly may lead to unwarranted causality claims. In their analysis, selection biases seem to be present in the response rate of their survey (8.35%), the choice of database, success criterion, and the overlook of the numerous drawbacks female researchers face in academia. We discuss these issues and their implications -- one of them being the potential increase in obstacles for women in academia. Finally, we reinforce the dangers of not considering selection bias effects in studies aimed at retrieving causal relations.

physics.soc-ph↗

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↗