MétaCan
Menu
Back to cohort
Record W3104923308 · doi:10.5167/uzh-182530

Maximally accreting supermassive stars: a fundamental limit imposed by hydrostatic equilibrium

2019· article· en· W3104923308 on OpenAlexaff
L. Haemmerlé, G. Meynet, Lucio Mayer, Ralf S. Klessen, Tyrone E. Woods, Alexander Heger

Bibliographic record

VenueSpringer Link (Chiba Institute of Technology) · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsHerzberg Institute of Astrophysics
FundersDeutsche ForschungsgemeinschaftSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsPhysicsAstrophysicsSupermassive black holeHydrostatic equilibriumStarsGalaxyAccretion (finance)Astronomy

Abstract

fetched live from OpenAlex

Context. Major mergers of gas-rich galaxies provide promising conditions for the formation of supermassive black holes (SMBHs; ≳105 M⊙) by direct collapse because they can trigger mass inflows as high as 104 − 105 M⊙ yr−1 on sub-parsec scales. However, the channel of SMBH formation in this case, either dark collapse (direct collapse without prior stellar phase) or supermassive star (SMS; ≳104 M⊙), remains unknown. Aims. Here, we investigate the limit in accretion rate up to which stars can maintain hydrostatic equilibrium. Methods. We compute hydrostatic models of SMSs accreting at 1–1000 M⊙ yr−1, and estimate the departures from equilibrium a posteriori by taking into account the finite speed of sound. Results. We find that stars accreting above the atomic cooling limit (≳10 M⊙ yr−1) can only maintain hydrostatic equilibrium once they are supermassive. In this case, they evolve adiabatically with a hylotropic structure, that is, entropy is locally conserved and scales with the square root of the mass coordinate. Conclusions. Our results imply that stars can only become supermassive by accretion at the rates of atomically cooled haloes (∼0.1 − 10 M⊙ yr−1). Once they are supermassive, larger rates are possible.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.001
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 head0.007
GPT teacher head0.208
Teacher spread0.201 · 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.

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

Citations28
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueSpringer Link (Chiba Institute of Technology)Same topicGalaxies: Formation, Evolution, PhenomenaFrench-language works237,207