arXiv subjects

A. M. Conrado

Publications and source records attributed to A. M. Conrado.

4 recordsLinked to original sources

arXiv2026

CAVITY: Calar Alto Void Integral-field Treasury surveY: II. Second public data release

Studying galaxy evolution under the unique environmental conditions of cosmic voids provides an opportunity to disentangle the role of the large-scale environment in shaping mass assembly (both baryonic and dark matter), regulating galaxy physical properties, and driving the transformation from star-forming to quiescent systems. We present the second public data release (DR2) of the Calar Alto Void Integral-field Treasury Survey (CAVITY), an ongoing legacy programme designed to study void galaxies (VGs) across a range of nearby ($0.005 \leq z \leq 0.050$) voids with different sizes and dynamical stages, providing 200 science-grade optical integral-field spectroscopy (IFS) data cubes. By doubling the number of galaxies relative to DR1 while maintaining the same selection criteria, DR2 significantly increases the dataset's statistical power, enabling more robust characterization of galaxy properties and their correlations with environment. The observations were obtained with the PMAS/PPAK spectrograph on the 3.5-m Calar Alto telescope using the V500 configuration, covering the optical spectral range of 3745-7500 A at a resolution of 6 A (FWHM). The DR2 VG sample spans a broad range of stellar masses, morphologies, colours, and gas ionisation properties. We describe the sample selection, observing strategy, data reduction pipeline, quality-control procedures, and the public access to the CAVITY datasets and associated ancillary data on the survey's database. In addition, we present a characterisation of the large- and local-scale environments of CAVITY galaxies, showing that the 15 surveyed voids encompass a wide variety of sizes, galaxy richness, galaxy populations, and local structures, including galaxy groups. This release comprises 200 IFS data cubes, publicly available together with the master catalogues at the survey's dedicated webpage: https://cavity.caha.es/data/dr2/.

astro-ph.GA↗

arXiv2026

OJALÁ: Optimizing J-PAS Astronomy for Large-scale Analysis. A foundation model for the SED of galaxies, QSOs and stars

The advent of large-scale surveys requires efficient ML techniques to exploit the information of massive datasets. We present OJALA, a transformer-based autoregressive foundation model designed to simultaneously classify astronomical objects and infer their physical parameters using 54 narrow bands from J-PAS, combined with broad bands from the DESI Legacy Imaging Surveys and WISE. The model is trained on $\sim20$ million synthetic SEDs generated from DESI DR1 spectra. We validate OJALA using a cross-matched sample of $\sim121,000$ objects between J-PAS and DESI. The model achieves a weighted F1-score of approximately 0.9 for spectral classification (stars, galaxies, and QSOs) at $i < 21$. For galaxies, we recover photo-z with a precision of $σ_{\rm NMAD} < 0.01$, while for QSOs, the precision improves significantly at $z > 1.5$, reaching $σ_{\rm NMAD} \approx 0.006$ at $z \approx 3.5$. We demonstrate robust estimation of physical properties for galaxies, recovering stellar masses and SFR with a scatter of approximately 0.11 dex and 0.22 dex, respectively. Furthermore, the model accurately predicts EWs for major optical emission lines, allowing for the derivation of extinction-corrected H$α$ luminosities with a scatter of 0.29 dex. OJALA successfully reproduces the BPT and WHAN diagnostic diagrams, classifying SF, AGN, and passive galaxies with F1-scores typically ranging from 70% to 90% depending on the diagnostic class. For stars, the model reliably infers effective temperature and metallicity, though surface gravity remains challenging. Finally, we show the modularity of the architecture by fine-tuning the pre-trained embeddings to predict BH masses, a property not included in the primary training, recovering spectroscopic virial estimates with a precision of approximately 0.5 dex. We release the code, model weights, and a comprehensive VAC for the J-PAS EDR.

astro-ph.GA↗

arXiv2025

Spatially resolved stellar populations and emission lines properties in nearby galaxies with J-PLUS -- I. Method and first results for the M101 group

Spatially resolved maps of stellar populations and nebular emission are key tools for understanding the physical properties and evolutionary stages of galaxies. We aim to characterize the spatially resolved stellar population and emission line properties of galaxies in the M101 group using Javalambre Photometric Local Universe Survey (J-PLUS) data. The datacubes first go through pre-processing steps, which include masking, noise suppression, PSF homogenization, and spatial binning. The improved data are then analyzed with the spectral synthesis code \alstar, which has been previously shown to produce excellent results with the unique 12 bands filter system of J-PLUS and S-PLUS. We produce maps of stellar mass surface density ($Σ_\star$), mean stellar age and metallicity, star formation rate surface density ($Σ_{\rm SFR}$), dust attenuation, and emission line properties such as fluxes and equivalent widths of the main optical lines. Relations among these properties are explored. All galaxies exhibit a well-defined age-$Σ_\star$ relation, except for the dwarfs. Similarly, all of the galaxies follow local $Σ_\star$-$Σ_{\rm SFR}$ star-forming MS relations, with specific star formation rates that grow for less massive systems. A stellar $Σ_\star$-metallicity relation is clearly present in M101, while other galaxies have either flatter or undefined relations. Nebular metallicities correlate with $Σ_\star$ for all galaxies. This study demonstrates the ability of J-PLUS to perform IFS-like analysis of galaxies, offering robust spatially resolved measurements of stellar populations and emission lines over large fields of view. The M101 group analysis showcases the potential for expanding such studies to other groups and clusters, contributing to the understanding of galaxy evolution across different environments.

astro-ph.GA↗

arXiv2024

Exploring Galaxy Properties of eCALIFA with Contrastive Learning

Contrastive learning (CL) has emerged as a potent tool for building meaningful latent representations of galaxy properties across a broad spectrum of wavelengths, ranging from optical and infrared to radio frequencies. These representations facilitate a variety of downstream tasks, including galaxy classification, similarity searches, and parameter estimation, which is why they are often referred to as foundation models. In this study, we employ CL on the latest extended DR from CALIFA survey, which encompasses 895 galaxies with enhanced spatial resolution. We demonstrate that CL can be applied to IFU surveys, even with small training sets, to meaningful embedding where galaxies are well-separated based on their physical properties. We discover that the strongest correlations in the embedding space are observed with the EW of Ha morphology, stellar metallicity, age, stellar surface mass density, the [NII]/Ha ratio, and stellar mass, in descending order of correlation strength. Additionally, we illustrate the feasibility of unsupervised separation of galaxy populations along the SFMS, successfully identifying the BC and the RS in a two-cluster scenario, and the GV population in a three-cluster scenario. Our findings indicate that galaxy luminosity profiles have minimal impact on the construction of the embedding space, suggesting that morphology and spectral features play a more significant role in distinguishing between galaxy populations. Moreover, we explore the use of CL for detecting variations in galaxy population distributions across different environments, including voids, clusters, filaments and walls. Nonetheless, we acknowledge the limitations of the CL and our specific training set in detecting subtle differences in galaxy properties, such as the presence of an AGN or other minor scale variations that exceed the scope of primary parameters like stellar mass or morphology.

astro-ph.GA↗