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Record W3082307177 · doi:10.1051/0004-6361/202039162

Distribution of star formation in galactic bars as seen with H<i>α</i>and stacked GALEX UV imaging

2020· article· en· W3082307177 on OpenAlexaff
S. Díaz-García, F. D. Moyano, S. Comerón, J. H. Knapen, H. Salo, Alexandre Y. K. Bouquin

Bibliographic record

VenueAstronomy and Astrophysics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsQueen's University
FundersAgencia Estatal de InvestigaciónMinisterio de Ciencia e InnovaciónMinisterio de Economía y CompetitividadEuropean Commission
KeywordsPhysicsAstrophysicsGalaxyStar formationBar (unit)Astronomy

Abstract

fetched live from OpenAlex

Context.Stellar bars are known to gradually funnel gas to the central parts of disk galaxies. It remains a matter of debate why the distribution of ionized gas along bars and in the circumnuclear regions varies among galaxies. Aims.Our goal is to investigate the spatial distribution of star formation (SF) within bars of nearby low-inclination disk galaxies (i < 65°) from the S4G survey. We aim to link the loci of SF to global properties of the hosts (morphological type, stellar mass, gas fraction, and bar-induced gravitational torques), providing constraints for the conditions that regulate SF in bars. Methods.We use archival GALEX far- and near-UV imaging for 772 barred galaxies, and for a control sample of 423 non-barred galaxies. We also assemble a compilation of continuum-subtracted Hαimages for 433 barred galaxies, 70 of which we produced from ancillary photometry and MUSE and CALIFA integral field unit data cubes. We employ two complementary approaches: (i) the analysis of bar (2D) and disk (1D) stacks built from co-added UV images (oriented and scaled with respect to the stellar bars and the extent of disks) of hundreds of galaxies that are binned based on their Hubble stage (T) and bar family; and (ii) the visual classification of the morphology of ionized regions (traced from Hαand UV data) in individual galaxies into three main SF classes: (A) only circumnuclear SF; (B) SF at the bar ends, but not along the bar; and (C) SF along the bar. Barred galaxies with active and passive inner rings are likewise classified. Results.Massive, gas-poor, lenticular galaxies typically belong to SF class A; this is probably related to bar-induced quenching of SF in the disk. The distribution of SF class B peaks for early- and intermediate-type spirals; this most likely results from the interplay of gas flow, shocks, and enhanced shear in massive centrally concentrated galaxies with large bar amplitudes (the latter is supported by the lack of a dip in the radial distribution of SF in non-barred galaxies). Late-type gas-rich galaxies with high gravitational torques are mainly assigned to SF class C; we argue that this is a consequence of low shear among the faintest galaxies. In bar stacks of spiral galaxies the UV emission traces the stellar bars and dominates on their leading side, as witnessed in simulations. Among early-type spirals the central UV emission is ∼0.5 mag brighter in strongly barred galaxies, relative to their weakly barred counterparts; this is probably related to the efficiency of strong bars sweeping the disk gas and triggering central starbursts. On the contrary, in later types the UV emission is stronger at all radii in strongly barred galaxies than in weakly barred and non-barred ones. We also show that the distributions of SF in inner-ringed galaxies are broadly the same in barred and non-barred galaxies, including a UV and Hαdeficit in the middle part of the bar; this hints at the effect of resonance rings trapping gas that is no longer funneled inwards. Conclusions.Distinct distributions of SF within bars are reported in galaxies of different morphological types. Star-forming bars are most common among late-type gas-rich galaxies. Bars are important agents in the regulation of SF in disks.

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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)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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

Citations43
Published2020
Admission routes1
Has abstractyes

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