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Record W4413056306 · doi:10.1051/0004-6361/202453110

Traces of the evolution of cosmic void galaxies: An integral field spectroscopy-based analysis

2025· article· en· W4413056306 on OpenAlexfundno aff
Agustín M. Rodríguez-Medrano, Dante J. Paz, D. Mast, F. Stasyszyn, Andrés N. Ruiz

Bibliographic record

VenueAstronomy and Astrophysics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryFondo para la Investigación Científica y TecnológicaUniversity of Colorado BoulderInstituto de Astrofísica de CanariasMinistério da Ciência, Tecnologia e InovaçãoOffice of ScienceMax-Planck-Institut für AstronomieYork UniversityConsejo Nacional de Investigaciones Científicas y TécnicasSecretaría de Ciencia y Técnica, Universidad de Buenos AiresAgence Nationale de la RechercheUniversity of OxfordUniversidad Nacional de CórdobaLeibniz-GemeinschaftUniversity of Notre DameCarnegie Mellon UniversityUniversidad Nacional Autónoma de MéxicoAlfred P. Sloan FoundationUniversity of WashingtonJohns Hopkins UniversityCarnegie Institution of WashingtonUniversity of UtahOhio State UniversityU.S. Department of EnergySmithsonian InstitutionNew Mexico State UniversityUniversity of PortsmouthVanderbilt UniversityYale UniversityMax-Planck-Institut für Astrophysik
KeywordsPhysicsAstrophysicsVoid (composites)GalaxyAstronomyPeculiar galaxyElliptical galaxyDiscGalaxy formation and evolution

Abstract

fetched live from OpenAlex

Context. Galaxies in the most underdense regions of the Universe, known as cosmic voids, exhibit astrophysical properties that suggest a distinct evolutionary path compared to galaxies in denser environments. Numerical simulations indicate that the assembly of void galaxies occurs later, leading to galaxies with younger stellar populations, low metallicities, and high gas content in their halos, which provides the fuel to sustain elevated star formation activity. Aims. Our objective in this work is to test these numerical predictions using observational data by comparing galaxies in voids with galaxies in non-void environments. Methods. We used voids identified in the SDSS data and selected galaxies from the MaNGA survey, which offers integral field spectroscopy (IFS) for each galaxy. This IFS data allows for state-of-the-art modeling of their stellar populations. We separated the galaxies into void and non-void samples, mimicked the magnitude distribution, and compared their integrated astrophysical properties, as well as the metallicity and age profiles, through a stacking technique. We analyzed early-type galaxies (ETGs) and late-type galaxies (LTGs) separately. Results. We find that void galaxies tend to host younger and less metal-rich stellar populations. This trend is observed both as a function of mass and in samples with matched magnitude distributions. With respect to the gas mass, we do not find differences across environments. When dividing galaxies into ETGs and LTGs, we observe that ETGs show negative gradients in both age and metallicity, with void galaxies consistently appearing younger and less metal-rich. For LTGs, age gradients are also negative, indicating younger populations in void galaxies. However, we do not find statistically significant differences in the stellar metallicity gradients between void and non-void environments. Conclusions. Our results show how the astrophysical properties of galaxies in voids differ from those of galaxies in the rest of the Universe. This suggests that the void environment plays a role in the evolution of its galaxies, delaying their assembly and growth.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.207
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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