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Rafael S. de Souza

Publications and source records attributed to Rafael S. de Souza.

At least 19 recordsLinked to original sources
arXiv2026

RadialPaths: Radial profiles in multi-centred astronomical structures

Radial profiles are routinely used to locate structural changes within resolved astronomical systems, but they generally define radial position relative to a single centre. In interacting, clumpy, or otherwise multi-centred structures, the radial location assigned to a feature can depend on the adopted centre. We introduce a centre-conditioned radial representation for connected structures with multiple centres. Distances are measured through the observed source footprint rather than across excluded background, and can be viewed as arrival times of fronts propagating from the centres. Each location is associated with the centre reached first and described by two complementary coordinates: relative centre--boundary depth and normalized progression away from that centre. For an ideal centred circular source, both reduce exactly to the familiar normalized radius $r/R$; in folded, elongated, or tailed geometries they separate because proximity to a lateral boundary and progression through the structure represent different notions of ``outward''. Synthetic examples show that single-centre and equivalent-area radial descriptions do not retain the association between localized features and individual centres, whereas centre-conditioned profiles do. In two JADES DR2 systems, the resulting F277W profiles remain stable under modest changes in the adopted source footprint and one-pixel centre shifts. The construction assumes neither a light-profile law nor an overlapping flux decomposition, and the same radial geometry can be applied to registered maps of continuum light, colour, emission lines, stellar-population properties, and line-of-sight kinematics. An accompanying Python package, \textsf{RadialPaths}, provides the reference implementation together with documentation and online tutorials at \href{https://rafaelsdesouza.com.br/radialpaths} {GitHub}.

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arXiv2026

The Hidden Geometry of Astrophysical Spectra: Path-Signatures of Line Profiles

The morphology of a spectral-line profile contains information beyond scalar summaries of line strength, centroid, width, global asymmetry, or diagnostic line ratios. Broad wings, shoulders, double peaks, secondary components, and composite emission--absorption structures encode how flux is ordered across wavelength but can remain indistinguishable under conventional summaries. We introduce an interpretable geometric representation of line profiles inspired by rough path theory. Each wavelength-sampled profile is mapped to a common systemic rest-frame velocity grid and treated as a trajectory in velocity--flux space, traversed from blue to red. From this path, we define a compact set of low-order descriptors measuring signed velocity--flux area, blue--red imbalance localization, higher-order shape complexity, and emission--absorption ordering. Using synthetic profiles, we show that these descriptors separate morphologies with similar full width at half maximum (FWHM), non-parametric velocity width ($W_{80}$), and low-order moment summaries. We then apply the method to MaNGA integral-field spectroscopy by computing H$α$ descriptors in individual spaxels and clustering them in a low-dimensional feature space. The resulting classes form spatially coherent regions of similar ordered line morphology. Although no external velocity field is supplied to the clustering, stacked spectra within these regions recover coherent large-scale centroid-velocity patterns broadly consistent with the MaNGA reference velocity fields. We release a minimalist MIT-licensed package ${\it spectropath}$, available at \href{https://rafaelsdesouza.com.br/spectropath/}{the project website}.

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arXiv2026

Blowing star formation away in AGN hosts (BAH) -- V: The Feeding-Feedback Cycle in local AGNs as revealed by their stellar populations

We present a spatially resolved analysis of the stellar populations in the inner kiloparsec of NGC 3884, 3C 293, and CGCG 012-070. Using near-infrared spectroscopy, we reconstruct their star formation histories (SFHs) by comparing the M13, XSL, and FSPS stellar population synthesis models. The stellar light is dominated by intermediate-age to old populations (t >= 1 Gyr) with super-solar metallicities (Z >= 1 Z_sun). All models clearly indicate recent star formation (rejuvenation) in these AGN hosts, with young to intermediate-age populations contributing significantly in the nuclear regions. The SFHs from M13 and XSL broadly agree in showing coexisting old and young components, whereas FSPS favours a larger fraction of very young (t < 50 Myr) stars. Moreover, XSL- and FSPS-based SFHs are generally more irregular and "bumpy," while M13 yields smoother, more continuous SFHs. In NGC 3884 and 3C 293, stars with 0.2 < t <= 0.7 Gyr form a ring-like structure around the nucleus. The nuclear spectra further require non-stellar components: a featureless power-law continuum (FC) and hot dust emission (HD). In 3C 293, the FC component appears in two spatially separated regions, possibly indicating a dual active galactic nucleus, though a heavily reddened starburst origin for the secondary component cannot be excluded. Nearly all fits show a central drop in stellar metallicity, consistent with inflow of metal-poor gas that fuels recent accretion and AGN activity. Radial profiles show that HD and FC contributions decrease with radius, while younger stellar populations become more prominent outward. Together, these results support a feeding-feedback scenario in which gas inflows trigger circumnuclear star formation and, via stellar mass loss, help sustain ongoing AGN activity. .

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arXiv2026

Lightstack: A Python Package for Creating Photometric Data Cubes

Multi-band photometry traces diverse physical processes across a wide range of wavelengths. In recent decades, this field has been driven by the rapid growth of multi-imaging datasets, from high-resolution observation from Hubble Space Telescope and James Webb Space Telescope to the forthcoming large-scale surveys enabled by the Roman Space Telescope and Rubin Observatory, for example. In this work, we present lightstack, a Python package for combining standalone images into photometric data cubes. The workflow consists of three main steps: cropping a region of interest from a mosaic across all available filters; stacking the images to construct the data cube; and performing PSF matching on the cube. This package is intended for preparing data for studies involving multi-band photometry. The code is released under an MIT license and is available on GitHub together with a Jupyter tutorial notebook. The version used for this publication (v0.2.1) is archived on Zenodo.

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arXiv2026

ELMA: ELlipse-based bar MAjor axis estimator

Galactic bars are key non-axisymmetric structures in disk galaxies, driving angular-momentum redistribution and contributing to secular evolution, central mass build-up, and the formation of nuclear structures. Robust and homogeneous measurements of bar length, however, remain challenging, particularly for large imaging surveys, where manual estimates are time-consuming and sensitive to methodological choices. We introduce elma, a standalone, pip-installable Python package for automated bar-length estimation in galaxies already identified as candidate barred systems. The method operates directly on two-dimensional imaging data, using iterative elliptical-isophote fitting to trace the radial ellipticity profile and identify a projected bar-length estimate from the semi-major axis associated with the local maximum in ellipticity. Using the image WCS information and a user-supplied redshift, elma converts angular measurement into a projected physical length. We demonstrate the package on JWST/NIRCam imaging of barred galaxies in the GOODS-South field. The code is released under the MIT license at a repository in Github.

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arXiv2026

SAGUI: SED-based Segmentation of Multi-band Galaxy Images -- Application to JADES in GOODS-South

We present sagui, a modular framework for the analysis of multi-band imaging data in spatially resolved galaxies, with synergies to integral-field spectroscopy (IFS). Building on the spectro-spatial paradigm introduced by capivara for IFS data, sagui extends this approach to imaging datasets, enabling a coherent, pixel-level treatment of spatial and spectral information across multiple bands. The method follows a two-stage strategy: a starlet-based decomposition is first used to identify and mask spatial structures across multiple scales while suppressing noise, and a spectral-similarity analysis then partitions the image into coherent pixel groups that preserve spectral consistency. In addition to compact and high-contrast structures, the framework incorporates a dedicated statistical treatment, based on a copula transform, to identify and recover faint, diffuse low-surface-brightness components. We demonstrate the method across a diverse range of galaxy morphologies, highlighting its ability to characterize complex spatial structures, including clumps, bars, interacting systems, and low-surface-brightness features. As a case study, we apply it to eleven morphologically diverse galaxies from the James Webb Space Telescope Advanced Deep Extragalactic Survey in the GOODS--South field. sagui is released under an MIT license and is available at https://rafaelsdesouza.github.io/sagui/.

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arXiv2026

The contribution of the color space in LSST-like photometry for the selection of extragalactic globular cluster candidates

Globular clusters (GCs) are excellent tracers of their host galaxies' evolutionary histories. Traditional methods for identifying GCs in galaxies rely on cuts over photometric catalogs and can yield source lists with high levels of contamination from compact background galaxies and foreground stars. In an era when large-scale sky surveys produce photometry for millions of sources, it is essential to employ flexible and scalable tools to reliably identify GCs in external galaxies. To prepare for surveys like Rubin/LSST, we need to explore practical methodological improvements and quantify the limitations inherent in the datasets. This paper investigates the selection of point-like extragalactic GCs exclusively in the $ugrizY$ color space. We use archival data to assemble an LSST-like photometric catalog for the Fornax Cluster containing labeled confirmed GCs, galaxies, and stars. From this catalog, using principal component analysis and non-linear auto-encoders (AEs), we construct inputs to random forest and multi-layer perceptron classifiers. We show that selecting GCs using all the 15 available colors can lead to a minimum contamination rate of ~30%, whereas the use of color-color diagrams may double such rate. If only the first 4 principal components of the colors are used instead, the same minimum contamination rate is achieved without increasing incompleteness. The AEs did not improve GC identification. To further reduce contamination and extract the full potential of LSST for star cluster studies, we argue for the need to augment photometric information with ancillary data (morphology from space-based missions and near-infrared photometry) before attempting to leverage more complex models.

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arXiv2026

PowerSpectR: An R Package for Radial Power Spectrum Estimation

I present here PowerSpectR, an R package for computing and visualizing median-based radial Fourier power spectra from imaging data. Power spectra provide a representation of spatial structure by decomposing contributions across spatial scales, and the resulting slopes can serve as compact, low-dimensional summaries of morphological complexity across images. PowerSpectR provides a workflow for estimating these slopes, combining edge-effect mitigation through Hann windowing, Fourier-domain analysis, and radial binning with azimuthal median statistics. The use of median aggregation helps to reduce sensitivity to bright compact sources, masking artifacts, and other localized features that can bias standard estimators. PowerSpectR is released under the MIT license at \href{https://github.com/RafaelSdeSouza/PowerSpectR}{this repository}.

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arXiv2026

SpectralUnmix: A Torch-Based Regularized Non-negative Matrix Factorization

We present SpectralUnmix, an R package for regularized non-negative matrix factorization (NMF), implemented in torch with optional GPU acceleration. The package estimates low-rank non-negative representations through proximal-gradient updates and allows smoothness regularization along the spectral axis. As a compact demonstration, we apply the method to a subset of stellar spectra and compare the recovered NMF components with principal-component directions and representative stellar spectra. The package is released under the MIT license at \href{https://rafaelsdesouza.github.io/SpectralUnmix/}{this repository}.

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arXiv2025

AT2022zod: An Unusual Tidal Disruption Event in an Elliptical Galaxy at Redshift 0.11

Tidal Disruption Events (TDEs) have long been hypothesized as valuable indicators of black holes, offering insight into their demographics and behaviour out to high redshift. TDEs have also enabled the discovery of a few Massive Black Holes (MBHs) with inferred masses of $10^4$--$10^6\,M_\odot$, often associated with the nuclei of dwarf galaxies or ultra-compact dwarf galaxies (UCDs). Here we present AT2022zod, an extreme and short-lived optical flare in an elliptical galaxy at z=0.11, residing within 3kpc of the galaxy's centre. Its luminosity and ~30-day duration make it unlikely to have originated from the host galaxy's central supermassive black hole (SMBH), which estimate to have a mass around $10^8\,M_\odot$. Assuming that the emission mechanism is consistent with known observed TDEs, we find that such a rapidly evolving transient could either be produced by a MBH in the intermediate-mass range or, alternatively, result from the tidal disruption of a star on a non-parabolic orbit around the central SMBH. We suggest that the most plausible origin for AT2022zod is the tidal disruption of a star by a MBH embedded in a UCD. As the Vera C. Rubin Observatory's Legacy Survey of Space and Time comes online, we propose that AT2022zod serves as an important event for refining search strategies and characterization techniques for intermediate-mass black holes.

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arXiv2025

Modeling Globular Cluster Counts with Bayesian Latent Models

We present a Bayesian latent model to describe the scaling relation between globular cluster populations and their host galaxies, updating the framework proposed in de Souza 2015. GC counts are drawn from a negative-binomial (NB) process linked to host stellar mass, augmented with a newly introduced Gaussian observation layer that enables efficient propagation of measurement errors. The revised formulation preserves the underlying NB process while improving computational tractability. The code snippets, implemented in Nimble and PyMC are released under the MIT license at https://github.com/COINtoolbox/Generalized-Linear-Models-Tutorial/blob/master/Count/readme.md

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arXiv2025

A Brief History of Inference in Astronomy

In this short review, we trace the evolution of inference in astronomy, highlighting key milestones rather than providing an exhaustive survey. We focus on the shift from classical optimization to Bayesian inference, the rise of gradient-based methods fueled by advances in deep learning, and the emergence of adaptive models that shape the very design of scientific datasets. Understanding this shift is essential for appreciating the current landscape of astronomical research and the future it is helping to build.

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arXiv2025

Capivara: A Spectral-based Segmentation Method for IFU Data Cubes

We present capivara, a fast and scalable spectral-based segmentation package designed to study astrophysical properties within distinct structural components of galaxies. This spectro-segmentation code for integral field unit (IFU) data provides a holistic view of galactic structure, moving beyond conventional radial gradients and the bulge-plus-disk dichotomy. It enables detailed comparisons of stellar ages and metallicities across components, and naturally identifies outliers by grouping spaxels according to dominant spectral features. The algorithm leverages Torch's scalability and GPU acceleration, outputting a masked FITS file that assigns each pixel to its respective group and generates the corresponding one-dimensional spectrum per group, without relying on Voronoi binning. We demonstrate the capabilities of the method using a sample of MaNGA galaxies, combining capivara segmentation with the starlight spectral fitting code to derive stellar population and ionized gas properties. The method effectively identifies regions with similar spectral properties across both continuum and emission lines. By aggregating the spectra of these regions, we enhance the signal-to-noise ratio of the analysis while preserving the spectral coherence within each group. capivara is released under an MIT license and is available at https://github.com/RafaelSdeSouza/capivara.

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arXiv2024

From Galaxy Zoo DECaLS to BASS/MzLS: detailed galaxy morphology classification with unsupervised domain adaption

The DESI Legacy Imaging Surveys (DESI-LIS) comprise three distinct surveys: the Dark Energy Camera Legacy Survey (DECaLS), the Beijing-Arizona Sky Survey (BASS), and the Mayall z-band Legacy Survey (MzLS). The citizen science project Galaxy Zoo DECaLS 5 (GZD-5) has provided extensive and detailed morphology labels for a sample of 253,287 galaxies within the DECaLS survey. This dataset has been foundational for numerous deep learning-based galaxy morphology classification studies. However, due to differences in signal-to-noise ratios and resolutions between the DECaLS images and those from BASS and MzLS (collectively referred to as BMz), a neural network trained on DECaLS images cannot be directly applied to BMz images due to distributional mismatch. In this study, we explore an unsupervised domain adaptation (UDA) method that fine-tunes a source domain model trained on DECaLS images with GZD-5 labels to BMz images, aiming to reduce bias in galaxy morphology classification within the BMz survey. Our source domain model, used as a starting point for UDA, achieves performance on the DECaLS galaxies' validation set comparable to the results of related works. For BMz galaxies, the fine-tuned target domain model significantly improves performance compared to the direct application of the source domain model, reaching a level comparable to that of the source domain. We also release a catalogue of detailed morphology classifications for 248,088 galaxies within the BMz survey, accompanied by usage recommendations.

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arXiv2024

Systematic analysis of jellyfish galaxy candidates in Fornax, Antlia, and Hydra from the S-PLUS survey: A self-supervised visual identification aid

We study 51 jellyfish galaxy candidates in the Fornax, Antlia, and Hydra clusters. These candidates are identified using the JClass scheme based on the visual classification of wide-field, twelve-band optical images obtained from the Southern Photometric Local Universe Survey. A comprehensive astrophysical analysis of the jellyfish (JClass > 0), non-jellyfish (JClass = 0), and independently organized control samples is undertaken. We develop a semi-automated pipeline using self-supervised learning and similarity search to detect jellyfish galaxies. The proposed framework is designed to assist visual classifiers by providing more reliable JClasses for galaxies. We find that jellyfish candidates exhibit a lower Gini coefficient, higher entropy, and a lower 2D Sérsic index as the jellyfish features in these galaxies become more pronounced. Jellyfish candidates show elevated star formation rates (including contributions from the main body and tails) by $\sim$1.75 dex, suggesting a significant increase in the SFR caused by the ram-pressure stripping phenomenon. Galaxies in the Antlia and Fornax clusters preferentially fall towards the cluster's centre, whereas only a mild preference is observed for Hydra galaxies. Our self-supervised pipeline, applied in visually challenging cases, offers two main advantages: it reduces human visual biases and scales effectively for large datasets. This versatile framework promises substantial enhancements in morphology studies for future galaxy image surveys.

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arXiv2024

Galmoss: A package for GPU-accelerated Galaxy Profile Fitting

We introduce galmoss, a python-based, torch-powered tool for two-dimensional fitting of galaxy profiles. By seamlessly enabling GPU parallelization, galmoss meets the high computational demands of large-scale galaxy surveys, placing galaxy profile fitting in the LSST-era. It incorporates widely used profiles such as the Sérsic, Exponential disk, Ferrer, King, Gaussian, and Moffat profiles, and allows for the easy integration of more complex models. Tested on 8,289 galaxies from the Sloan Digital Sky Survey (SDSS) g-band with a single NVIDIA A100 GPU, galmoss completed classical Sérsic profile fitting in about 10 minutes. Benchmark tests show that galmoss achieves computational speeds that are 6 $\times$ faster than those of default implementations.

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arXiv2024

NSCs from groups to clusters: A catalogue of dwarf galaxies in the Shapley Supercluster and the role of environment in galaxy nucleation

Nuclear star clusters (NSCs) are dense star clusters located at the centre of galaxies spanning a wide range of masses and morphologies. Analysing NSC occupation statistics in different environments provides an invaluable window into investigating early conditions of high-density star formation and mass assembly in clusters and group galaxies. We use HST/ACS deep imaging to obtain a catalogue of dwarf galaxies in two galaxy clusters in the Shapley Supercluster: the central cluster Abell 3558 and the northern Abell 1736a. The Shapley region is an ideal laboratory to study nucleation as it stands as the highest mass concentration in the nearby Universe. We investigate the NSC occurrence in quiescent dwarf galaxies as faint as $M_{I} = -10$ mag and compare it with all other environments where nucleation data is available. We use galaxy cluster/group halo mass as a proxy for the environment and employ a Bayesian logistic regression framework to model the nucleation fraction ($f_{n}$) as a function of galaxy luminosity and environment. We find a notably high $f_n$ in Abell 3558: at $M_{I} \approx -13.1$ mag, half the galaxies in the cluster host NSCs. This is higher than in the Virgo and Fornax clusters but comparable to the Coma Cluster. On the other hand, the $f_n$ in Abell 1736a is relatively lower, comparable to groups in the Local Volume. We find that the probability of nucleation varies with galaxy luminosity remarkably similarly in galaxy clusters. These results reinforce previous findings of the important role of the environment in NSC formation/growth.

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arXiv2024

The 2022-2023 accretion outburst of the young star V1741 Sgr

V1741 Sgr (= SPICY 71482/Gaia22dtk) is a Classical T Tauri star on the outskirts of the Lagoon Nebula. After at least a decade of stability, in mid-2022, the optical source brightened by ~3 mag over two months, remained bright until early 2023, then dimmed erratically over the next four months. This event was monitored with optical and infrared spectroscopy and photometry. Spectra from the peak (October 2022) indicate an EX Lup-type (EXor) accretion outburst, with strong emission from H I, He I, and Ca II lines and CO bands. At this stage, spectroscopic absorption features indicated a temperature of T ~ 4750 K with low-gravity lines (e.g., Ba II and Sr II). By April 2023, with the outburst beginning to dim, strong TiO absorption appeared, indicating a cooler T ~ 3600 K temperature. However, once the source had returned to its pre-outburst flux in August 2023, the TiO absorption and the CO emission disappeared. When the star went into outburst, the source's spectral energy distribution became flatter, leading to bluer colours at wavelengths shorter than ~1.6 microns and redder colours at longer wavelengths. The brightening requires a continuum emitting area larger than the stellar surface, likely from optically thick circumstellar gas with cooler surface layers producing the absorption features. Additional contributions to the outburst spectrum may include blue excess from hotspots on the stellar surface, emission lines from diffuse gas, and reprocessed emission from the dust disc. Cooling of the circumstellar gas would explain the appearance of TiO, which subsequently disappeared once this gas had faded and the stellar spectrum reemerged.

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