arXiv subjects

E. V. R. Lima

Publications and source records attributed to E. V. R. Lima.

8 recordsLinked to original sources

arXiv2026

S-PLUS Clusters And Large-scale Environments (SCALE): I. A catalog of known clusters and groups in DR5 and a pilot study of Abell 4038

Within the framework of the Southern Photometric Local Universe Survey (S-PLUS), we introduce ${\bf S}$-PLUS ${\bf C}$lusters ${\bf A}$nd ${\bf L}$arge-scale ${\bf E}$nvironments (SCALE), a project dedicated to the study of galaxy clusters, groups, and their environments using 12-band photometry of S-PLUS combined with spectroscopic and photometric data from the literature. In this first paper, we present a catalog of 83 previously known systems in the redshift range $0.008 \leq z_{\rm spec} \leq 0.1$, for which we derive $R_{200}$, $M_{200}$, and velocity dispersions. Spectroscopic members are selected and matched with S-PLUS photometric redshifts (photo-$z$s). We find very good agreement between literature spectroscopic redshifts (spec-$z$s) and S-PLUS photometric redshifts (photo-$z$s), demonstrating the potential of the latter for cluster and group membership determination. As a proof of concept, we obtain photometric memberships for Abell 4038 using the Reliable Photometric Membership technique. A two- and three-dimensional analysis of the region within $10 h^{-1}$ Mpc ($10\times R_{200}$) from the center of Abell 4038 reveals about a dozen substructures including two additional clusters within $1.3\times R_{200}$ (Abell 4038B and Abell 4049). A color-luminosity segregation analysis shows that more luminous (less luminous) galaxies are redder (bluer), as expected. Low-concentration galaxies ($C \leq 2.5$) exhibit a weaker color-luminosity dependence, compared to higher-concentration ones, indicating mass-dependent evolutionary pathways that challenge a simple morphology-color dichotomy, with low-luminosity galaxies presenting bluer colors largely independent of concentration. The SCALE catalog provides a valuable basis for future studies of large-scale structures and their connection to galaxy evolution.

astro-ph.CO↗

arXiv2025

The role of supercluster filaments in shaping galaxy clusters

In a hierarchical $Λ$CDM Universe, cosmic filaments serve as the primary channels for matter accretion into galaxy clusters, influencing the shape of their dark matter halos. We investigate whether the elongation of galaxy clusters correlates with the orientation of surrounding filaments, providing the first observational test of this relationship in large supercluster regions. We identified and characterized cosmic filaments in two dimensions within the two superclusters that are part of the low-redshift sub-survey of the Chilean Cluster Galaxy Evolution Survey (CHANCES): the Shapley supercluster and the Horologium-Reticulum supercluster. We analyzed the alignment between filament directions -- traced by galaxy distributions -- and the triaxiality of cluster gravitational potentials -- traced by X-ray emission- using publicly available optical and X-ray data. We have found that most (82%) of the X-ray clusters are associated with and interconnected by the optically detected filaments. The clusters-filaments alignment analysis shows that the elongation of most clusters is well aligned with nearby filaments, providing observational confirmation of theoretical predictions, with the alignment progressively reducing at larger cluster-centric distances ($> 1.6 r_{200}$). Overall, our results support the notion that filaments are the main source of galaxy accretion at redshift below 0.1 and additionally provide evidence that matter accretion through filaments shapes the gravitational potential of galaxy clusters. We propose this measurement as a simple observational proxy to determine the direction of accretion in clusters, which is key to understanding both galaxy evolution and the merger history of galaxy clusters.

astro-ph.CO↗

arXiv2025

CHANCES, the Chilean Cluster Galaxy Evolution Survey: Selection and initial characterisation of clusters and superclusters

CHANCES, the CHileAN Cluster galaxy Evolution Survey, will study the evolution of galaxies in and around 100 massive galaxy clusters from the local Universe out to $z = 0.45$, and two superclusters at $z \sim 0.05$ that contain roughly 25 Abell clusters each. CHANCES will use the new 4MOST Spectroscopic Survey Facility on the VISTA 4m telescope to obtain spectra for $\sim$500,000 galaxies with magnitudes $r_\mathrm{AB} < 20.4$, providing comprehensive spectroscopic coverage of each cluster out to $5r_{200}$. Its wide and deep scope will trace massive and dwarf galaxies from the surrounding filaments and groups to the cores of galaxy clusters. This will enable the study of galaxy preprocessing and of the role of the evolving environment on galaxy evolution. In this paper, we present and characterise the sample of clusters and superclusters to be targeted by CHANCES. We used literature catalogues based on X-ray emission and the Sunyaev-Zel'dovich effect to define the cluster sample in a homogeneous way, with attention to cluster mass and redshift, as well as the availability of ancillary data. We calibrated literature mass estimates from various surveys against each other and provide an initial mass estimate for each cluster, which we used to define the radial extent of the 4MOST coverage. We also present an initial assessment of the structure surrounding these clusters based on the redMaPPer red-sequence algorithm as a preview of some of the science CHANCES will enable.

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arXiv2024

The Quasar Catalogue for S-PLUS DR4 (QuCatS) and the estimation of photometric redshifts

The advent of massive broad-band photometric surveys enabled photometric redshift estimates for unprecedented numbers of galaxies and quasars. These estimates can be improved using better algorithms or by obtaining complementary data such as narrow-band photometry, and broad-band photometry over an extended wavelength range. We investigate the impact of both approaches on photometric redshifts for quasars using data from Southern Photometric Local Universe Survey (S-PLUS) DR4, Galaxy Evolution Explorer (GALEX) DR6/7, and the unWISE catalog for the Wide-field Infrared Survey Explorer (WISE) in three machine learning methods: Random Forest, Flexible Conditional Density Estimation (FlexCoDE), and Bayesian Mixture Density Network (BMDN). Including narrow-band photometry improves the root-mean-square error by 11% in comparison to a model trained with only broad-band photometry. Narrow-band information only provided an improvement of 3.8% when GALEX and WISE colours were included. Thus narrow bands play a more important role for objects that do not have GALEX or WISE counterparts, which respectively makes 92% and 25% of S-PLUS data considered here. Nevertheless, the inclusion of narrow-band information provided better estimates of the probability density functions obtained with FlexCoDE and BMDN. We publicly release a value-added catalogue of photometrically selected quasars with the photo-z predictions from all methods studied here. The catalogue provided with this work covers the S-PLUS DR4 area (~3000deg$^2$), containing 645 980, 244 912, 144 991 sources with the probability of being a quasar higher than, 80%, 90%, 95% up to r < 21.3 and good photometry quality in the detection image. More quasar candidates can be retrieved from the S-PLUS data base by considering less restrictive selection criteria.

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arXiv2023

An Extended Catalogue of galaxy morphology using Deep Learning in Southern Photometric Local Universe Survey Data Release 3

The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation. However, in large sky surveys, even the morphological classification of galaxies into two classes, like late-type (LT) and early-type (ET), still represents a significant challenge. In this work we present a Deep Learning (DL) based morphological catalog built from images obtained by the Southern Photometric Local Universe Survey (S-PLUS) Data Release 3 (DR3). Our DL method achieves an precision rate of 98.5$\%$ in accurately distinguishing between spiral, as part of the larger category of late type (LT) galaxies, and elliptical, belonging to early type (ET) galaxies. Additionally, we have implemented a secondary classifier that evaluates the quality of each galaxy stamp, which allows to select only high-quality images when studying properties of galaxies on the basis of their DL morphology. From our LT/ET catalog of galaxies, we recover the expected color--magnitude diagram in which LT galaxies display bluer colors than ET ones. Furthermore, we also investigate the clustering of galaxies based on their morphology, along with their relationship to the surrounding environment. As a result, we deliver a full morphological catalog with $164314$ objects complete up to $r_{petro}<18$, covering $\sim 1800$ deg$^2$, including a significant area of the Southern hemisphere that was not covered by previous morphology catalogues.

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arXiv2022

Deep Learning Assessment of galaxy morphology in S-PLUS DataRelease 1

The morphological diversity of galaxies is a relevant probe of galaxy evolution and cosmological structure formation, but the classification of galaxies in large sky surveys is becoming a significant challenge. We use data from the Stripe-82 area observed by the Southern Photometric Local Universe Survey (S-PLUS) in twelve optical bands, and present a catalogue of the morphologies of galaxies brighter than $r=17$ mag determined both using a novel multi-band morphometric fitting technique and Convolutional Neural Networks (CNNs) for computer vision. Using the CNNs we find that, compared to our baseline results with 3 bands, the performance increases when using 5 broad and 3 narrow bands, but is poorer when using the full $12$ band S-PLUS image set. However, the best result is still achieved with just 3 optical bands when using pre-trained network weights from an ImageNet data set. These results demonstrate the importance of using prior knowledge about neural network weights based on training in unrelated, extensive data sets, when available. Our catalogue contains 3274 galaxies in Stripe-82 that are not present in Galaxy Zoo 1 (GZ1), and we also provide our classifications for 4686 galaxies that were considered ambiguous in GZ1. Finally, we present a prospect of a novel way to take advantage of $12$ band information for morphological classification using morphometric features, and we release a model that has been pre-trained on several bands that could be adapted for classifications using data from other surveys. The morphological catalogues are publicly available.

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arXiv2022

Photometric redshifts for the S-PLUS Survey: is machine learning up to the task?

The Southern Photometric Local Universe Survey (S-PLUS) is a novel project that aims to map the Southern Hemisphere using a twelve filter system, comprising five broad-band SDSS-like filters and seven narrow-band filters optimized for important stellar features in the local universe. In this paper we use the photometry and morphological information from the first S-PLUS data release (S-PLUS DR1) cross-matched to unWISE data and spectroscopic redshifts from Sloan Digital Sky Survey DR15. We explore three different machine learning methods (Gaussian Processes with GPz and two Deep Learning models made with TensorFlow) and compare them with the currently used template-fitting method in the S-PLUS DR1 to address whether machine learning methods can take advantage of the twelve filter system for photometric redshift prediction. Using tests for accuracy for both single-point estimates such as the calculation of the scatter, bias, and outlier fraction, and probability distribution functions (PDFs) such as the Probability Integral Transform (PIT), the Continuous Ranked Probability Score (CRPS) and the Odds distribution, we conclude that a deep-learning method using a combination of a Bayesian Neural Network and a Mixture Density Network offers the most accurate photometric redshifts for the current test sample. It achieves single-point photometric redshifts with scatter ($σ_\text{NMAD}$) of 0.023, normalized bias of -0.001, and outlier fraction of 0.64% for galaxies with r-auto magnitudes between 16 and 21.

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arXiv2019

Assessing the photometric redshift precision of the S-PLUS survey: the Stripe-82 as a test-case

In this paper we present a thorough discussion about the photometric redshift (photo-z) performance of the Southern Photometric Local Universe Survey (S-PLUS). This survey combines a 7 narrow + 5 broad passband filter system, with a typical photometric-depth of r$\sim$21 AB. For this exercise, we utilize the Data Release 1 (DR1), corresponding to 336 deg$^{2}$ from the Stripe-82 region. We rely on the \texttt{BPZ2} code to compute our estimates, using a new library of SED models, which includes additional templates for quiescent galaxies. When compared to a spectroscopic redshift control sample of $\sim$100k galaxies, we find a precision of $σ_{z}<$0.8\%, $<$2.0\% or $<$3.0\% for galaxies with magnitudes r$<$17, $<$19 and $<$21, respectively. A precision of 0.6\% is attained for galaxies with the highest \texttt{Odds} values. These estimates have a negligible bias and a fraction of catastrophic outliers inferior to 1\%. We identify a redshift window (i.e., 0.26$<z<$0.32) where our estimates double their precision, due to the simultaneous detection of two emission-lines in two distinct narrow-bands; representing a window opportunity to conduct statistical studies such as luminosity functions. We forecast a total of $\sim$2M, $\sim$16M and $\sim$32M galaxies in the S-PLUS survey with a photo-z precision of $σ_{z}<$1.0\%, $<$2.0\% and $<$2.5\% after observing 8000 $deg^{2}$. We also derive redshift Probability Density Functions, proving their reliability encoding redshift uncertainties and their potential recovering the $n(z)$ of galaxies at $z<0.4$, with an unprecedented precision for a photometric survey in the southern hemisphere.

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