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Record W4389473681 · doi:10.1051/0004-6361/202348117

Density distributions, magnetic field structures, and fragmentation in high-mass star formation

2023· article· en· W4389473681 on OpenAlexaff
H. Beuther, C. Gieser, J. D. Soler, Qizhou Zhang, Ramprasad Rao, D. Semenov, Th. Henning, Ralph E. Pudritz, T. Peters, Pamela Klaassen, M. T. Beltrán, Aina Palau, T. Möller, K. Johnston, H. Zinnecker, J. S. Urquhart, R. Kuiper, A. Ahmadi, Á. Sánchez-Monge, Siyi Feng, S. Leurini, S. E. Ragan

Bibliographic record

VenueAstronomy and Astrophysics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsMcMaster University
FundersSistema Nacional de InvestigadoresAgencia Estatal de InvestigaciónSmithsonian Astrophysical ObservatoryAcademia SinicaDeutsche ForschungsgemeinschaftScience and Technology Facilities CouncilEuropean CommissionSmithsonian Institution
KeywordsPhysicsAstrophysicsFragmentation (computing)Star formationMagnetic fieldAstronomyStar (game theory)Stars

Abstract

fetched live from OpenAlex

Context. The fragmentation of high-mass star-forming regions depends on a variety of physical parameters, including density, the magnetic field, and turbulent gas properties. Aims. We evaluate the importance of the density and magnetic field structures in relation to the fragmentation properties during high-mass star formation. Methods. Observing the large parsec-scale Stokes I millimeter dust continuum emission with the IRAM 30 m telescope and the intermediate-scale (<0.1 pc) polarized submillimeter dust emission with the Submillimeter Array toward a sample of 20 high-mass star-forming regions allows us to quantify the dependence of the fragmentation behavior of these regions on the density and magnetic field structures. Results. Based on the IRAM 30 m data, we infer density distributions n ∝ r−p of the regions with typical power-law slopes p around ~1.5. There is no obvious correlation between the power-law slopes of the density structures on larger clump scales (~1 pc) and the number of fragments on smaller core scales (<0.1 pc). Comparing the large-scale single-dish density profiles to those derived earlier from interferometric observations at smaller spatial scales, we find that the smaller-scale power-law slopes are steeper, typically around ~2.0. The flattening toward larger scales is consistent with the star-forming regions being embedded in larger cloud structures that do not decrease in density away from a particular core. The magnetic fields of several regions appear to be aligned with filamentary structures that lead toward the densest central cores. Furthermore, we find different polarization structures; some regions exhibit central polarization holes, whereas other regions show polarized emission also toward the central peak positions. Nevertheless, the polarized intensities are inversely related to the Stokes I intensities, following roughly a power-law slope of ∝ SI−0.62. We estimate magnetic field strengths between ~0.2 and ~4.5 mG, and we find no clear correlation between magnetic field strength and the fragmentation level of the regions. A comparison of the turbulent to magnetic energies shows that they are of roughly equal importance in this sample. The mass-to-flux ratios range between ~2 and ~7, consistent with collapsing star-forming regions. Conclusions. Finding no clear correlations between the present-day large-scale density structure, the magnetic field strength, and the smaller-scale fragmentation properties of the regions, indicates that the fragmentation of high-mass star-forming regions may not be affected strongly by the initial density profiles and magnetic field properties. However, considering the limited evolutionary range and spatial scales of the presented CORE analysis, future research directions should include density structure analysis of younger regions that better resemble the initial conditions, as well as connecting the observed intermediate-scale magnetic field structure with the larger-scale magnetic fields of the parental molecular clouds.

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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.006
GPT teacher head0.211
Teacher spread0.205 · 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

Citations23
Published2023
Admission routes1
Has abstractyes

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