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Record W4312137345 · doi:10.1051/0004-6361/202244718

VERTICO

2022· article· en· W4312137345 on OpenAlexafffund
María J. Jiménez-Donaire, Toby Brown, C. D. Wilson, Ian Roberts, Nikki Zabel, Sara L. Ellison, Mallory Thorp, Vicente Villanueva, Ryan Chown, Dhruv Bisaria, Alberto D. Bolatto, A. Boselli, Barbara Catinella, Aeree Chung, L. Cortese, Timothy A. Davis, Claudia del P. Lagos, Bumhyun Lee, Laura C. Parker, Kristine Spekkens, Adam R. H. Stevens, Jiayi Sun

Bibliographic record

VenueAstronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of TorontoRoyal Military College of CanadaMcMaster UniversityQueen's UniversityCanadian Institute for Theoretical AstrophysicsWestern UniversityUniversity of VictoriaHerzberg Institute of Astrophysics
FundersNational Institutes of Natural SciencesNatural Sciences and Engineering Research Council of CanadaJet Propulsion LaboratoryNational Astronomical Observatory of JapanScience and Technology Facilities CouncilNational Research FoundationAustralian Research CouncilNational Research Foundation of KoreaAgencia Nacional de Investigación y DesarrolloCanada Research ChairsKorea Astronomy and Space Science InstituteNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyAssociated UniversitiesNational Radio Astronomy ObservatoryNational Science Foundation
KeywordsPhysicsAstrophysicsVirgo ClusterStar formationGalaxySpiral galaxyContext (archaeology)AstronomyElliptical galaxyGalaxy clusterLenticular galaxyGalaxy formation and evolution

Abstract

fetched live from OpenAlex

Aims. In this Virgo Environment Traced in CO (VERTICO) science paper, we aim to study how the star formation process depends on the galactic environment and gravitational interactions in the context of galaxy evolution. We explore the scaling relation between the star formation rate surface density (Σ SFR ) and the molecular gas surface density (Σ mol ), also known as the Kennicutt-Schmidt relation, in a subsample of Virgo cluster spiral galaxies. Methods. We used new Atacama Compact Array and Total Power (ACA+TP) observations from the VERTICO-Atacama Large Millimeter/submillimeter Array (ALMA) Large Program at 720 pc resolution to resolve the molecular gas content, as traced by the 12 CO (2 − 1) transition, across the disks of 37 spiral galaxies in the Virgo cluster. In combination with archival UV and IR observations used to determine the star formation rate (SFR), we estimated the parameters of the Kennicutt-Schmidt (KS) relation for the entire ensemble of galaxies, and within individual galaxies. Results. We find the KS slope for the entire population to be N = 0.97 ± 0.07, with a characteristic molecular gas depletion time of 1.86 Gyr for our full sample, which is in agreement with previous work in isolated, nearby star-forming galaxies. In individual galaxies, we find that the KS slope ranges between 0.69 and 1.40, and that typical star formation efficiencies of molecular gas can vary from galaxy to galaxy by a factor of ∼4. These galaxy-to-galaxy variations account for ∼0.20 dex in scatter in the ensemble KS relation, which is characterized by a 0.42 dex scatter. In addition, we find that the HI-deficient galaxies in the Virgo cluster show a steeper resolved KS relation and lower molecular gas efficiencies than HI-normal cluster galaxies. Conclusions. While the molecular gas content in galaxies residing in the Virgo cluster appears – to first order – to behave similarly to that in isolated galaxies, our VERTICO sample of galaxies shows that cluster environments play a key role in regulating star formation. The environmental mechanisms affecting the HI galaxy content also have a direct impact on the star formation efficiency of molecular gas in cluster galaxies, leading to longer depletion times in HI-deficient members.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.258
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2580.147

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 head0.005
GPT teacher head0.182
Teacher spread0.177 · 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

Citations22
Published2022
Admission routes2
Has abstractyes

Explore more

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