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Record W4400273977 · doi:10.1051/0004-6361/202348840

Massive star cluster formation

2024· article· en· W4400273977 on OpenAlexafffund
Brooke Polak, Mordecai‐Mark Mac Low, Ralf S. Klessen, Jia Wei Teh, Claude Cournoyer-Cloutier, Eric P. Andersson, Sabrina M. Appel, Aaron Tran, Sean C. Lewis, Maite J. C. Wilhelm, Simon Portegies Zwart, Simon C. O. Glover, Steven Rieder, Long Wang, Stephen L. W. McMillan

Bibliographic record

VenueAstronomy and Astrophysics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsMcMaster University
FundersNational Supercomputing Center, Korea Institute of Science and Technology InformationOffice of Advanced CyberinfrastructureFundamental Research Funds for the Central UniversitiesSun Yat-sen UniversityNational Natural Science Foundation of ChinaNatural Sciences and Engineering Research Council of CanadaUniversität HeidelbergInternational Max Planck Research School for Advanced Methods in Process and Systems EngineeringDeutsche ForschungsgemeinschaftInternational Max Planck Research School for Environmental, Cellular and Molecular MicrobiologyNational Science Foundation
KeywordsPhysicsAstrophysicsStar clusterCluster (spacecraft)AstronomyStar (game theory)Star formationStars

Abstract

fetched live from OpenAlex

The mode of star formation that results in the formation of globular clusters and young massive clusters is difficult to constrain through observations. We present models of massive star cluster formation using the TORCH framework, which uses the Astrophysical MUltipurpose Software Environment (AMUSE) to couple distinct multi-physics codes that handle star formation, stellar evolution and dynamics, radiative transfer, and magnetohydrodynamics. We upgraded TORCH by implementing the N-body code PETAR, thereby enabling TORCH to handle massive clusters forming from 106 M⊙ clouds with ≥105 individual stars. We present results from TORCH simulations of star clusters forming from 104, 105, and 106 M⊙ turbulent spherical gas clouds (named M4, M5, M6) of radius R = 11.7 pc. We find that star formation is highly efficient and becomes more so at a higher cloud mass and surface density. For M4, M5, and M6 with initial surface densities 2.325 × 101,2,3 M⊙ pc−2, after a free-fall time of tff = 6.7,2.1,0.67 Myr, we find that ∼30%, 40%, and 60% of the cloud mass has formed into stars, respectively. The end of simulation-integrated star formation efficiencies for M4, M5, and M6 are ϵ⋆ = M⋆/Mcloud = 36%, 65%, and 85%. Observations of nearby clusters similar in mass and size to M4 have instantaneous star formation efficiencies of ϵinst ≤ 30%, which is slightly lower than the integrated star formation efficiency of M4. The M5 and M6 models represent a different regime of cluster formation that is more appropriate for the conditions in starburst galaxies and gas-rich galaxies at high redshift, and that leads to a significantly higher efficiency of star formation. We argue that young massive clusters build up through short efficient bursts of star formation in regions that are sufficiently dense (Σ ≥ 102 M⊙ pc−2) and massive (Mcloud ≥ 105 M⊙). In such environments, stellar feedback from winds and radiation is not strong enough to counteract the gravity from gas and stars until a majority of the gas has formed into stars.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.217
Teacher spread0.210 · 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

Citations30
Published2024
Admission routes2
Has abstractyes

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