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Record W4220726645 · doi:10.1051/0004-6361/202142832

Variations in the Σ<sub>SFR</sub> − Σ<sub>mol</sub> − Σ<sub>⋆</sub>plane across galactic environments in PHANGS galaxies

2022· article· en· W4220726645 on OpenAlexafffund
Ismael Pessa, Eva Schinnerer, Adam K. Leroy, Eric W. Koch, Erik Rosolowsky, Thomas G. Williams, Hsi-An Pan, Andreas Schruba, A. Usero, Francesco Belfiore, Frank Bigiel, Guillermo A. Blanc, Mélanie Chevance, Daniel A. Dale, Éric Emsellem, Jindra Gensior, Simon C. O. Glover, Kathryn Grasha, Brent Groves, Ralf S. Klessen, Kathryn Kreckel, J. M. Diederik Kruijssen, Daizhong Liu, Sharon E. Meidt, J. Pety, Miguel Querejeta, Toshiki Saito, P. Sánchez–Blázquez, Elizabeth J. Watkins

Bibliographic record

VenueAstronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Alberta
FundersNational Institutes of Natural SciencesAgencia Nacional de Investigación y DesarrolloNational Astronomical Observatory of JapanMinisterio de Ciencia e InnovaciónCentre National de la Recherche ScientifiqueAgence Nationale de la RechercheDeutsche ForschungsgemeinschaftEuropean Regional Development FundEuropean CommissionCentre National d’Etudes SpatialesSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungAssociated UniversitiesAgencia Estatal de InvestigaciónNational Radio Astronomy ObservatoryNatural Sciences and Engineering Research Council of CanadaSmithsonian InstitutionKorea Astronomy and Space Science InstituteNational Science Foundation
KeywordsPhysicsAstrophysicsGalactic planeGalaxyStar formationScalingStarsSpiral galaxyStellar massLow MassGeometry

Abstract

fetched live from OpenAlex

Aims.There exists some consensus that the stellar mass surface density (Σ⋆) and molecular gas mass surface density (Σmol) are the main quantities responsible for locally setting the star formation rate. This regulation is inferred from locally resolved scaling relations between these two quantities and the star formation rate surface density (ΣSFR), which have been extensively studied in a wide variety of works. However, the universality of these relations is debated. Here, we probe the interplay between these three quantities across different galactic environments at a spatial resolution of 150 pc. Methods.We performed a hierarchical Bayesian linear regression to find the best set of parametersC⋆,Cmol, andCnormthat describe the star-forming plane conformed by Σ⋆, Σmol, and ΣSFR, such that logΣSFR = C⋆logΣ⋆ + CmollogΣmol + Cnorm. We also explored variations in the determined parameters across galactic environments, focusing our analysis on theC⋆andCmolslopes. Results.We find signs of variations in the posterior distributions ofC⋆andCmolacross different galactic environments. The dependence of ΣSFRon Σ⋆spans a wide range of slopes, with negative and positive values, while the dependence of ΣSFRon Σmolis always positive. Bars show the most negative value ofC⋆(−0.41), which is a sign of longer depletion times, while spiral arms show the highestC⋆among all environments (0.45). Variations inCmolalso exist, although they are more subtle than those found forC⋆. Conclusions.We conclude that systematic variations in the interplay of Σ⋆, Σmol, and ΣSFRacross different galactic environments exist at a spatial resolution of 150 pc, and we interpret these variations to be produced by an additional mechanism regulating the formation of stars that is not captured by either Σ⋆or Σmol. Studying environmental variations in single galaxies, we find that these variations correlate with changes in the star formation efficiency across environments, which could be linked to the dynamical state of the gas that prevents it from collapsing and forming stars, or to changes in the molecular gas fraction.

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)0.001
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations32
Published2022
Admission routes2
Has abstractyes

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