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Record W2016537330 · doi:10.1088/0004-637x/745/2/183

CARMA SURVEY TOWARD INFRARED-BRIGHT NEARBY GALAXIES (STING). II. MOLECULAR GAS STAR FORMATION LAW AND DEPLETION TIME ACROSS THE BLUE SEQUENCE

2012· article· en· W2016537330 on OpenAlexaff
Nurur Rahman, Alberto D. Bolatto, Rui Xue, Tony Wong, Adam K. Leroy, Fabian Walter, Frank Bigiel, Erik Rosolowsky, David B. Fisher, S. N. Vogel, Leo Blitz, Andrew A. West

Bibliographic record

VenueThe Astrophysical Journal · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsOkanagan University CollegeUniversity of British Columbia
Fundersnot available
KeywordsAstrophysicsPhysicsStar formationGalaxySurface brightnessPower lawInfraredAstronomy

Abstract

fetched live from OpenAlex

We present an analysis of the relationship between molecular gas and current star formation rate surface density at sub-kiloparsec and kiloparsec scales in a sample of 14 nearby star-forming galaxies. Measuring the relationship in the bright, high molecular gas surface density (∑H2 ≳ 20M⊙ pc?2) regions of the disks to minimize the contribution from diffuse extended emission, we find an approximately linear relation between molecular gas and star formation rate surface density, Nmol ∼ 0.96 ± 0.16, with a molecular gas depletion time, τdepmol ∼ 2.30 ± 1.32 Gyr. We show that in the molecular regions of our galaxies there are no clear correlations between τdepmol and the free-fall and effective Jeans dynamical times throughout the sample. We do not find strong trends in the power-law index of the spatially resolved molecular gas star formation law or the molecular gas depletion time across the range of galactic stellar masses sampled (M* ∼ 109.710 11.5M⊙). There is a trend, however, in global measurements that is particularly marked for low-mass galaxies. We suggest that this trend is probably due to the low surface brightness CO J = 10, and it is likely associated with changes in CO-to-H2 conversion factor.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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 teacher head, not a consensus.

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

Citations100
Published2012
Admission routes1
Has abstractyes

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