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Rosa M. González Delgado

Publications and source records attributed to Rosa M. González Delgado.

13 recordsLinked to original sources
arXiv2026

J-PAS & FLAMINGO: Cosmic voids and void galaxies in the gravitational landscape of photometric surveys

Photometric surveys offer a powerful way to map the large-scale structure of the Universe, but their redshift errors complicate the identification of cosmic voids, challenging studies of their environmental effect on galaxy properties. We present an approach to robustly identify dynamically relevant voids and void galaxies in galaxy mocks of the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS), testing whether known trends in void galaxy properties survive photometric redshift errors. Using FLAMINGO mocks at z = 0.3 and mi < 20, we compare a FLAMINGO-based ideal (FBI) mock to a FLAMINGO-based JP mock with J-PAS-like redshift errors. We mitigate redshift errors using a quasi-gravitational potential field in the two galaxy mocks. We apply a watershed algorithm to the thresholded quasi-potential field to identify dynamically dominant voids, and define massive void galaxies alongside a comparison sample in high-density regions. Photometric errors lead to a slightly lower void abundance and a marginal shift toward larger, less spherical voids, but overall size and ellipticity distributions agree well between mocks. Their main impact is contamination of void interiors in the JP density profiles by galaxies scattered from high-density regions. We recover a reasonable number of FBI sample voids in the JP sample, with excellent size and shape agreement, occupying ~63% of the thresholded quasi-potential volume. In both mocks, void galaxies show lower stellar masses, bluer colours, and enhanced star formation relative to equal-mass galaxies in high-density regions. These results suggest a quasi-potential can mitigate redshift errors at the level expected for J-PAS, enabling identification of reliable, dynamically dominant voids that are less sensitive to small-scale noise. The massive void galaxy population shows the expected trends relative to high-density environments.

astro-ph.CO↗
arXiv2026

The CAVITY project. The spatially resolved SFR of galaxies in voids

The mass in the Universe is distributed non-uniformly, originating the Large Scale Structure (LSS), characterised by clusters, filaments, walls and voids. Galaxies in voids are bluer, later type, less massive, and have slower evolution than galaxies in denser environments. The effect of the void environment on properties such as star formation rate (SFR) is still under discussion. We tackle this by estimating spatially-resolved SFR from extinction-corrected Halpha luminosities of 220 void galaxies from the CAVITY survey. These observations consist of optical integral field unit data cubes from the PMAS/PPaK spectrograph at Calar Alto Observatory. We measure the continuum-subtracted emission lines to obtain maps of SFR, specific star formation rate (sSFR) and extinction. We assess global properties and radial profiles up to 2 half-light radii. We compare with galaxies in filaments and walls from the CALIFA survey using the same methodology, building a control sample matched in morphology and stellar mass. We find no significant differences in SFR and sSFR, although void galaxies tend to have larger SFR, especially for early spirals. This effect is present for Sa galaxies at all galactocentric distances, and in the outer parts of late-type spirals, evidencing slower transition to quiescence and less evolved discs. Void late-type galaxies have lower extinction. Using extinction normalised by stellar mass surface density as a proxy for gas mass fraction, we find it larger for void early spirals, especially in outer regions. This indicates the effect of the void environment on the transition from star forming to passive.

astro-ph.GA↗
arXiv2025

Unveiling the small-scale web around galaxies with miniJPAS and DESI

We present the first statistical observational study detecting filaments in the immediate surroundings of galaxies, i.e. the local web of galaxies. Simulations predict that cold gas, the fuel for star formation, is channeled through filamentary structures into galaxies. Yet, direct observational evidence for this process has been limited by the challenge of mapping the cosmic web at small scales. Using miniJPAS spectro-photometric data combined with spectroscopic DESI redshifts when available, we construct a high-density observational galaxy sample spanning 0.2 10^(10) Msun using all nearby galaxies as tracers, combined with a probabilistic adaptation of the DisPerSE algorithm designed to overcome limitations due to photometric redshift uncertainties. Our methodology is tested and validated using mock catalogues built with random forest models applied to a simulated lightcone. Besides recovering the expected increase in galaxy connectivity (defined as the number of filaments attached to a galaxy) with stellar mass, we show that our connectivity measurements agree with 3D reference estimates from the mock galaxies. Thanks to these filament reconstructions, we explore the relation between small-scale connectivity and galaxy star formation rate, finding a mild positive trend which needs to be confirmed by follow up studies with larger sample sizes. We propose galaxy connectivity to local filaments as a powerful and physically motivated metric of environment, offering new insights into the role of cosmic structure in galaxy evolution.

astro-ph.CO↗
arXiv2025

The miniJPAS and J-NEP surveys: Machine learning for star-galaxy separation

We present a supervised machine learning classification of sources from the Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS) Pathfinder datasets: miniJPAS and J-NEP. Leveraging crossmatches with spectroscopic and photometric catalogs, we construct a robust labeled dataset comprising 14594 sources classified into extended (galaxies) and point-like (stars and quasars) objects. We assess dataset representativeness using UMAP analysis, confirming broad and consistent coverage of feature space. An XGBoost classifier, with hyperparameters tuned using automated optimization, is trained using purely photometric data (60-band J-PAS magnitudes) and combined photometric and morphological features, with performance thoroughly evaluated via ROC and purity-completeness metrics. Incorporating morphology significantly improves classification, outperforming the baseline classifications available in the catalogs. Permutation importance analysis reveals morphological parameters, particularly concentration, normalized peak surface brightness, and PSF, alongside photometric features around 4000 and 6900 A, as crucial for accurate classifications. We release a value-added catalog with our models for star-galaxy classification, enhancing the utility of miniJPAS and J-NEP for subsequent cosmological and astrophysical analyses.

astro-ph.IM↗
arXiv2024

CAVITY: Calar Alto Void Integral-field Treasury surveY. I. First public data release

The Calar Alto Void Integral-field Treasury surveY (CAVITY) is a legacy project aimed at characterising the population of galaxies inhabiting voids, which are the most under-dense regions of the cosmic web, located in the Local Universe. This paper describes the first public data release (DR1) of CAVITY, comprising science-grade optical data cubes for the initial 100 out of a total of $\sim$300 galaxies in the Local Universe ($0.005 < z < 0.050$). These data were acquired using the integral-field spectrograph PMAS/PPak mounted on the 3.5m telescope at the Calar Alto observatory. The DR1 galaxy sample encompasses diverse characteristics in the color-magnitude space, morphological type, stellar mass, and gas ionisation conditions, providing a rich resource for addressing key questions in galaxy evolution through spatially resolved spectroscopy. The galaxies in this study were observed with the low-resolution V500 set-up, spanning the wavelength range 3745-7500 Å, with a spectral resolution of 6.0 Å (FWHM). Here, we describe the data reduction and characteristics and data structure of the CAVITY datasets essential for their scientific utilisation, highlighting such concerns as vignetting effects, as well as the identification of bad pixels and management of spatially correlated noise. We also provide instructions for accessing the CAVITY datasets and associated ancillary data through the project's dedicated database.

astro-ph.GA↗
arXiv2024

The CAVITY project. The spatially resolved stellar population properties of galaxies in voids

The Universe is shaped as a web-like structure, formed by clusters, filaments, and walls that leave large volumes in between named voids. Galaxies in voids have been found to be of a later type, bluer, less massive, and to have a slower evolution than galaxies in denser environments (filaments and walls). However, the effect of the void environment on their stellar population properties is still unclear. We aim to address this question using 118 optical integral field unit datacubes from the Calar Alto Void Integral-field Treasury surveY (CAVITY), observed with the PMAS/PPaK spectrograph at the 3.5m telescope at the Calar Alto Observatory (Almería, Spain). We used the non-parametric full spectral fitting code STARLIGHT to estimate their stellar population properties: stellar mass, stellar mass surface density, age, star formation rate (SFR), and specific star formation rate (sSFR). We analysed the results through the global and spatially resolved properties. Then, we compared them with a control sample of galaxies in filaments and walls from the CALIFA survey, matched in stellar mass and morphological type. Key findings include void galaxies having a slightly higher half-light radius (HLR), lower stellar mass surface density, and younger ages across all morphological types, and slightly elevated SFR and sSFR (only significant enough for Sas). Many of these differences appear in the outer parts of spiral galaxies in voids (HLR > 1), which are younger and exhibit a higher sSFR, indicative of less evolved discs. This trend is also found for early-type spirals, suggesting a slower transition from star-forming to quiescent states in voids. Our analysis indicates that void galaxies, influenced by their surroundings, undergo a more gradual evolution, especially in their outer regions, with a more pronounced effect for low-mass galaxies.

astro-ph.GA↗
arXiv2024

Multiwavelength exploration of Extreme Emission Line Galaxies detected in miniJPAS survey

Extreme Emission Line Galaxies (EELGs) stand as remarkable objects due to their extremely metal poor environment and intense star formation. Considered as local analogues of high-redshift galaxies in the peak of their star-forming activity, they offer insights into conditions prevalent during the early Universe. Assessment of their stellar and gas properties is, therefore, of critical importance, which requires the assembly of a considerable sample, comprehending a broad redshift range. The Javalambre-Physics of the Accelerating Universe Astrophysical Survey (JPAS) plays a significant role in assembling such a sample, encompassing approximately 8000 deg2 and employing 54 narrow-band optical filters. The present work describes the development and subsequent application of the tools that will be employed in the forthcoming JPAS spectrophotometric data, allowing for the massive and automated characterization of EELGs that are expected to be identified. This fully automated pipeline (requiring only the object coordinates from users) constructs Spectral Energy Distributions (SEDs) by retrieving virtually all the available multi-wavelength photometric data archives, employs SED fitting tools and identifies optical emission lines. It was applied to the sample of extreme line emitters identified in the miniJPAS Survey, and its derived physical properties such as stellar mass and age, coupled with fundamental relations, mirror results obtained through spectral modeling of SDSS spectra. Thorough testing using galaxies with documented photometric measurements across different wavelengths confirmed the pipeline's accuracy, demonstrating its capability for automated analysis of sources with varying characteristics, spanning brightness, morphology, and redshifts. The modular nature of this pipeline facilitates any addition from the user.

astro-ph.GA↗
arXiv2023

The miniJPAS survey quasar selection IV: Classification and redshift estimation with SQUEzE

We present a list of quasar candidates including photometric redshift estimates from the miniJPAS Data Release constructed using SQUEzE. This work is based on machine-learning classification of photometric data of quasar candidates using SQUEzE. It has the advantage that its classification procedure can be explained to some extent, making it less of a `black box' when compared with other classifiers. Another key advantage is that using user-defined metrics means the user has more control over the classification. While SQUEzE was designed for spectroscopic data, here we adapt it for multi-band photometric data, i.e. we treat multiple narrow-band filters as very low-resolution spectra. We train our models using specialized mocks from Queiroz et al. (2022). We estimate our redshift precision using the normalized median absolute deviation, $σ_{\rm NMAD}$ applied to our test sample. Our test sample returns an $f_1$ score (effectively the purity and completeness) of 0.49 for quasars down to magnitude $r=24.3$ with $z\geq2.1$ and 0.24 for quasars with $z<2.1$. For high-z quasars, this goes up to 0.9 for $r<21.0$. We present two catalogues of quasar candidates including redshift estimates: 301 from point-like sources and 1049 when also including extended sources. We discuss the impact of including extended sources in our predictions (they are not included in the mocks), as well as the impact of changing the noise model of the mocks. We also give an explanation of SQUEzE reasoning. Our estimates for the redshift precision using the test sample indicate a $σ_{NMAD}=0.92\%$ for the entire sample, reduced to 0.81\% for $r<22.5$ and 0.74\% for $r<21.3$. Spectroscopic follow-up of the candidates is required in order to confirm the validity of our findings.

astro-ph.IM↗
arXiv2023

The miniJPAS survey quasar selection II: Machine learning classification with photometric measurements and uncertainties

Astrophysical surveys rely heavily on the classification of sources as stars, galaxies or quasars from multi-band photometry. Surveys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of larger effort that aims at building a catalogue of quasars from the miniJPAS survey, we present a Machine Learning-based method that employs Convolutional Neural Networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS survey, a proof-of-concept project of the J-PAS collaboration covering $\sim$ 1 deg$^2$ of the northern sky using the 56 narrow-band filters of the J-PAS survey. Due to the scarcity of real data, we trained our algorithms using mocks that were purpose-built to reproduce the distributions of different types of objects that we expect to find in the miniJPAS survey, as well as the properties of the real observations in terms of signal and noise. We compare the performance of the CNNs with other well-established Machine Learning classification methods based on decision trees, finding that the CNNs improve the classification when the measurement errors are provided as inputs. The predicted distribution of objects in miniJPAS is consistent with the putative luminosity functions of stars, quasars and unresolved galaxies. Our results are a proof-of-concept for the idea that the J-PAS survey will be able to detect unprecedented numbers of quasars with high confidence.

astro-ph.GA↗
arXiv2022

The miniJPAS survey quasar selection I: Mock catalogues for classification

In this series of papers, we employ several machine learning (ML) methods to classify the point-like sources from the miniJPAS catalogue, and identify quasar candidates. Since no representative sample of spectroscopically confirmed sources exists at present to train these ML algorithms, we rely on mock catalogues. In this first paper we develop a pipeline to compute synthetic photometry of quasars, galaxies and stars using spectra of objects targeted as quasars in the Sloan Digital Sky Survey. To match the same depths and signal-to-noise ratio distributions in all bands expected for miniJPAS point sources in the range $17.5\leq r<24$, we augment our sample of available spectra by shifting the original $r$-band magnitude distributions towards the faint end, ensure that the relative incidence rates of the different objects are distributed according to their respective luminosity functions, and perform a thorough modeling of the noise distribution in each filter, by sampling the flux variance either from Gaussian realizations with given widths, or from combinations of Gaussian functions. Finally, we also add in the mocks the patterns of non-detections which are present in all real observations. Although the mock catalogues presented in this work are a first step towards simulated data sets that match the properties of the miniJPAS observations, these mocks can be adapted to serve the purposes of other photometric surveys.

astro-ph.GA↗
arXiv2007

An Atlas of the circumnuclear regions of 75 Seyfert galaxies in the near-UV with HST Advanced Camera for Surveys

We present an atlas of the central regions of 75 Seyfert galaxies imaged in the near-UV with the Advanced Camera for Surveys of the Hubble Space Telescope at an average resolution of ~10pc. These data complement archival high resolution data from the Space Telescope at optical and near-IR wavelengths, creating an extremely valuable dataset for astronomers with a broad range of scientific interests. Our goal is to investigate the nature of the near-UV light in these objects, its relation to the circumnuclear starburst phenomenon, and the connection of this to the evolution and growth of the galaxy bulge and central black hole. In this paper, we describe the near-UV morphology of the objects and characterize the near-UV emission. We estimate the size and the luminosity of the emitting regions and extract the luminosity profile. We also determine the presence of unresolved compact nuclei. In addition, the circumnuclear stellar cluster population is identified, and the contribution of the stellar clusters to the total light, at this wavelength, is estimated. The size of the sample allows us to draw robust statistical conclusions. We find that {Seyfert 1} galaxies are completely dominated by its bright and compact nucleus, that remains point-like at this resolution, while we find almost no unresolved nucleus in Seyfert 2. The Seyfert types 1 and 2 are quite segregated in an asymmetry vs compactness plot. Stellar clusters are found somewhat more frequently in Sy2 (in ~70% of the galaxies) than in Sy1 (~57%), and contribute more to the total light in Sy2, but this two differences seem to be mostly due to the large contribution of the compact nucleus in Sy1, as the luminosity distribution of the clusters is similar in both Sy types.

astro-ph↗
arXiv2005

The ALHAMBRA Survey: For a systematic Study of Cosmic Evolution

ALHAMBRA is a project to gather data to sample a fraction of the Universe with enough precision to follow the evolution of its content and properties with z, a kind of Cosmic Tomography. It is defined as a 4 square degrees photometric survey with 20 contiguous, equal width, medium band filters covering from 3500 to 9700 A, plus the JHKs NIR bands. It was optimized to get (for a fixed observing time) the maximum number of objects with accurate classification and redshift and to be sensitive to relatively faint emission features. The observations will be carried out with the 3.5m telescope in Calar Alto (Spain). We intend to reach the limit AB=25 in all the optical filters from the bluest to 8300 A, and from 24.7 to 23.4 for the remainder. The expected limit in the NIR is fixed at Ks=20, H=21, J=22. The homogeneous and contiguous spectral coverage will result in several hundred thousand objects with accurate SED identification and z-values. The accuracy of the survey will allow to make detailed studies, including galactic, without the need for any further follow-up. Indeed, it will provide exciting targets for large telescopes, the GTC in particular.

astro-ph↗