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Record W2106837094 · doi:10.1093/mnras/stv1789

Cosmological galaxy evolution with superbubble feedback – I. Realistic galaxies with moderate feedback

2015· article· en· W2106837094 on OpenAlexaff
Ben Keller, James Wadsley, H. M. P. Couchman

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhysicsSuperbubbleAstrophysicsGalaxySupernovaBulgeRedshiftGalaxy rotation curveStarsGalaxy formation and evolutionMilky WaySmoothed-particle hydrodynamicsAstronomyInterstellar medium

Abstract

fetched live from OpenAlex

We present the first cosmological galaxy evolved using the modern smoothed particle hydrodynamics (SPH) code gasoline2 with superbubble feedback. We show that superbubble-driven galactic outflows powered by Type II supernovae alone can produce L* galaxies with flat rotation curves with circular velocities ∼ 200 km s− 1, low bulge-to-disc ratios, and stellar mass fractions that match observed values from high redshift to the present. These features are made possible by the high mass loadings generated by the evaporative growth of superbubbles. Outflows are driven extremely effectively at high redshift, expelling gas at early times and preventing overproduction of stars before z = 2. Centrally concentrated gas in previous simulations has often lead to unrealistically high bulge to total ratios and strongly peaked rotation curves. We show that supernova-powered superbubbles alone can produce galaxies that agree well with observed properties without the need for additional feedback mechanisms or increased feedback energy. We present additional results arising from properly modelled hot feedback.

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 score0.023
Threshold uncertainty score0.045

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 studies0.0000.001
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 head0.011
GPT teacher head0.195
Teacher spread0.183 · 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

Citations71
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueMonthly Notices of the Royal Astronomical Society→Same topicGalaxies: Formation, Evolution, Phenomena→French-language works237,207→