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Record W2972955431 · doi:10.1093/pasj/psaa033

The formation of young massive clusters triggered by cloud–cloud collisions in the Antennae galaxies NGC 4038/NGC 4039

2020· article· en· W2972955431 on OpenAlexfundno aff

Bibliographic record

VenuePublications of the Astronomical Society of Japan · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceNational Astronomical Observatory of JapanAlberta Livestock and Meat AgencyNational Aeronautics and Space AdministrationNational Science Foundation
KeywordsGalaxyStar clusterMilky WayCluster (spacecraft)Star formationRam pressureCollisionGalaxy cluster

Abstract

fetched live from OpenAlex

Abstract The formation mechanism of super star clusters (SSCs), present-day analogs of the ancient globulars, still remains elusive. The major merger that is the Antennae galaxies is forming SSCs and is one of the primary targets to test the cluster formation mechanism. We reanalyzed the archival ALMA CO data of the Antennae and found three typical observational signatures of a cloud–cloud collision toward SSC B1 and other SSCs in the overlap region: (i) two velocity components with ∼100 km s−1 velocity separation, (ii) bridge features connecting the two components, and (iii) a complementary spatial distribution between them, lending support to collisions of the two components as a cluster formation mechanism. We present a scenario that two clouds with 100 km s−1 velocity separation collided, and SSCs having ∼106–107 M⊙ were formed rapidly during that time scale. We compared the present results with the recent studies of star-forming regions in the Milky Way and the Large Magellanic Cloud, where the SSCs having ∼104–105 M⊙ are located. As a result, we found that there is a positive correlation between the compressed gas pressure generated by collisions and the total stellar mass of an SSC, suggesting that the pressure may be a key parameter in SSC formation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.033
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.212
Teacher spread0.202 · 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 teacher head, 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

Citations22
Published2020
Admission routes1
Has abstractyes

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