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Stellar feedback and bulge formation in clumpy discs

2012· article· en· W1892652016 on OpenAlexafffund
Philip F. Hopkins, Dušan Kereš, Norman Murray, Eliot Quataert, Lars Hernquist

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaSmithsonian Astrophysical ObservatoryNational Aeronautics and Space AdministrationCanada Research ChairsHarvard UniversitySmithsonian InstitutionDavid and Lucile Packard Foundation
KeywordsPhysicsAstrophysicsBulgeStar formationStarsGalaxySpiral galaxyStellar massGalaxy formation and evolutionAstronomy

Abstract

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We use numerical simulations of isolated galaxies to study the effects of stellar feedback on the formation and evolution of giant star-forming gas ‘clumps’ in high-redshift, gas-rich galaxies. Such galactic discs are unstable to the formation of bound gas-rich clumps whose properties initially depend only on global disc properties, not the microphysics of feedback. In simulations without stellar feedback, clumps turn an order-unity fraction of their mass into stars and sink to the centre, forming a large bulge and kicking most of the stars out into a much more extended stellar envelope. By contrast, strong radiative stellar feedback disrupts even the most massive clumps after they turn ∼10–20 per cent of their mass into stars, in a time-scale of ∼10–100 Myr, ejecting some material into a superwind and recycling the rest of the gas into the diffuse interstellar medium (ISM). This suppresses the bulge formation rate by direct ‘clump coalescence’ by a factor of several. However, the galactic discs do undergo significant internal evolution in the absence of mergers: clumps form and disrupt continuously and torque gas to the galactic centre. The resulting evolution is qualitatively similar to bar/spiral evolution in simulations with a more homogeneous ISM.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.193
Teacher spread0.186 · 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

Citations150
Published2012
Admission routes2
Has abstractyes

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