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Record W2603740260 · doi:10.1051/0004-6361/201628989

The earliest phases of high-mass star formation, as seen in NGC 6334 by <i>Herschel</i>-HOBYS

2017· article· en· W2603740260 on OpenAlexaff
J. Tigé, F. Motte, D. Russeil, A. Zavagno, M. Hennemann, N. Schneider, T. Hill, Q. Nguyễn Lương, James Di Francesco, S. Bontemps, F. Louvet, P. Didelon, V. Könyves, Ph. André, G. Leuleu, J. Bardagi, L. D. Anderson, D. Arzoumanian, M. Benedettini, J.-P. Bernard, D. Elia, M. Figueira, J. M. Kirk, P. G. Martin, V. Minier, S. Molinari, T. Nony, P. Persi, S. Pezzuto, D. Polychroni, T. Rayner, A. Rivera-Ingraham, H. Roussel, K. L. J. Rygl, L. Spinoglio, G. J. White

Bibliographic record

VenueAstronomy and Astrophysics · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsCanadian Institute for Theoretical AstrophysicsNational Research Council CanadaUniversity of VictoriaUniversity of TorontoHerzberg Institute of Astrophysics
FundersNational Astronomical Observatories, Chinese Academy of SciencesScience and Technology Facilities CouncilMax-Planck-Institut für AstronomieCentre National d’Etudes SpatialesBundesministerium für Verkehr, Innovation und TechnologieCentre National de la Recherche ScientifiqueKU LeuvenLeverhulme TrustAgence Nationale de la RechercheNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyImperial College LondonUK Space Agency
KeywordsPhysicsAstrophysicsStar formationMillimeterMolecular cloudInfraredHigh massAstronomyStars

Abstract

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Aims. To constrain models of high-mass star formation, the Herschel-HOBYS key program aims at discovering massive dense cores (MDCs) able to host the high-mass analogs of low-mass prestellar cores, which have been searched for over the past decade. We here focus on NGC 6334, one of the best-studied HOBYS molecular cloud complexes. Methods. We used Herschel/PACS and SPIRE 70−500 μm images of the NGC 6334 complex complemented with (sub)millimeter and mid-infrared data. We built a complete procedure to extract ~0.1 pc dense cores with the getsources software, which simultaneously measures their far-infrared to millimeter fluxes. We carefully estimated the temperatures and masses of these dense cores from their spectral energy distributions (SEDs). We also identified the densest pc-scale cloud structures of NGC 6334, one 2 pc × 1 pc ridge and two 0.8 pc × 0.8 pc hubs, with volume-averaged densities of ~105 cm-3. Results. A cross-correlation with high-mass star formation signposts suggests a mass threshold of 75 M⊙ for MDCs in NGC 6334. MDCs have temperatures of 9.5−40 K, masses of 75−1000 M⊙, and densities of 1 × 105−7 × 107 cm-3. Their mid-infrared emission is used to separate 6 IR-bright and 10 IR-quiet protostellar MDCs while their 70 μm emission strength, with respect to fitted SEDs, helps identify 16 starless MDC candidates. The ability of the latter to host high-mass prestellar cores is investigated here and remains questionable. An increase in mass and density from the starless to the IR-quiet and IR-bright phases suggests that the protostars and MDCs simultaneously grow in mass. The statistical lifetimes of the high-mass prestellar and protostellar core phases, estimated to be 1−7 × 104 yr and at most 3 × 105 yr respectively, suggest a dynamical scenario of high-mass star formation. Conclusions. The present study provides good mass estimates for a statistically significant sample, covering the earliest phases of high-mass star formation. High-mass prestellar cores may not exist in NGC 6334, favoring a scenario presented here, which simultaneously forms clouds, ridges, MDCs, and high-mass protostars.

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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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.224
Teacher spread0.217 · 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

Citations97
Published2017
Admission routes1
Has abstractyes

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