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Record W2609097503 · doi:10.3847/1538-4357/aa6c67

Cloud Structure of Three Galactic Infrared Dark Star-forming Regions from Combining Ground- and Space-based Bolometric Observations

2017· article· en· W2609097503 on OpenAlexaff
Yuxin Lin, Hauyu Baobab Liu, J. E. Dale, Di Li, G. Busquet, Zhi-Yu Zhang, Adam Ginsburg, Roberto Galván-Madrid, A. Kovács, Eric W. Koch, Lei Qian, Ke Wang, Steve Longmore, Huei-Ru Vivien Chen, Daniel L. Walker

Bibliographic record

VenueThe Astrophysical Journal · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsUniversity of Alberta
FundersScience and Technology Facilities Council
KeywordsPhysicsAstrophysicsStar formationMolecular cloudLuminosityStarsGalaxy

Abstract

fetched live from OpenAlex

Abstract We have modified the iterative procedure introduced by Lin et al., to systematically combine the submillimeter images taken from ground-based (e.g., CSO, JCMT, APEX) and space (e.g., Herschel, Planck) telescopes. We applied the updated procedure to observations of three well-studied Infrared Dark Clouds (IRDCs): G11.11−0.12, G14.225−0.506, and G28.34+0.06, and then performed single-component, modified blackbody fits to each pixel to derive ∼10″ resolution dust temperature and column density maps. The derived column density maps show that these three IRDCs exhibit complex filamentary structures embedded with rich clumps/cores. We compared the column density probability distribution functions (N-PDFs) and two-point correlation (2PT) functions of the column density field between these IRDCs with several OB-cluster-forming regions. Based on the observed correlation between the luminosity-to-mass ratio and the power-law index of the N-PDF, and complementary hydrodynamical simulations for a 104 molecular cloud, we hypothesize that cloud evolution can be better characterized by the evolution of the (column) density distribution function and the relative power of dense structures as a function of spatial scales, rather than merely based on the presence of star-forming activity. An important component of our approach is to provide a model-independent quantification of cloud evolution. Based on the small analyzed sample, we propose four evolutionary stages, namely, cloud integration, stellar assembly, cloud pre-dispersal, and dispersed cloud. The initial cloud integration stage and the final dispersed cloud stage may be distinguished from the two intermediate stages by a steeper than −4 power-law index of the N-PDF. The cloud integration stage and the subsequent stellar assembly stage are further distinguished from each other by the larger luminosity-to-mass ratio (>40 ) of the latter. A future large survey of molecular clouds with high angular resolution may establish more precise evolutionary tracks in the parameter space of N-PDF, 2PT function, and luminosity-to-mass ratio.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.030
GPT teacher head0.250
Teacher spread0.220 · 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

Citations47
Published2017
Admission routes1
Has abstractyes

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