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Record W2792300856 · doi:10.1093/mnras/sty681

Dynamical evolution of stars and gas of young embedded stellar sub-clusters

2018· article· en· W2792300856 on OpenAlexaff
Alison Sills, Steven Rieder, Jennifer Scora, Jessica McCloskey, Sarah Jaffa

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhysicsAstrophysicsStarsStar clusterStar formationStellar dynamicsStellar evolutionStellar populationCluster (spacecraft)Astronomy

Abstract

fetched live from OpenAlex

We present simulations of the dynamical evolution of young embedded star clusters. Our initial conditions are directly derived from X-ray, infrared, and radio observations of local systems, and our models evolve both gas and stars simultaneously. Our regions begin with both clustered and extended distributions of stars, and a gas distribution that can include a filamentary structure in addition to gas surrounding the stellar sub-clusters. We find that the regions become spherical, monolithic, and smooth quite quickly, and that the dynamical evolution is dominated by the gravitational interactions between the stars. In the absence of stellar feedback, the gas moves gently out of the centre of our regions but does not have a significant impact on the motions of the stars at the earliest stages of cluster formation. Our models at later times are consistent with observations of similar regions in the local neighbourhood. We conclude that the evolution of young protostar clusters is relatively insensitive to reasonable choices of initial conditions. Models with more realism, such as an initial population of binary and multiple stars and ongoing star formation, are the next step needed to confirm these findings.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.200
Teacher spread0.195 · 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 designSimulation or modeling
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

Citations49
Published2018
Admission routes1
Has abstractyes

Explore more

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