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Cosmological galaxy formation simulations using smoothed particle hydrodynamics

2010· article· en· W2016776564 on OpenAlexaff
G. S. Stinson, Jeremy Bailin, H. M. P. Couchman, James Wadsley, Sijing Shen, Sarah Nickerson, Chris B. Brook, Thomas Quinn

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsMcMaster University
FundersScience and Technology Facilities Council
KeywordsPhysicsAstrophysicsGalaxyGalaxy formation and evolutionStar formationSmoothed-particle hydrodynamicsBulgeHaloSurface brightnessGalaxy rotation curveAstronomy

Abstract

fetched live from OpenAlex

We present the McMaster Unbiased Galaxy Simulations (MUGS), the first nine galaxies of an unbiased selection ranging in total mass from 5 × 1011 M⊙ to 2 × 1012 M⊙ simulated using N-body smoothed particle hydrodynamics at high resolution. The simulations include a treatment of low-temperature metal cooling, UV background radiation, star formation and physically motivated stellar feedback. Mock images of the simulations show that the simulations lie within the observed range of relations such as that between colour and magnitude and that between brightness and circular velocity (Tully–Fisher). The greatest discrepancy between the simulated galaxies and observed galaxies is the high concentration of material at the centre of the galaxies as represented by the centrally peaked rotation curves and the high bulge-to-total ratios of the simulations determined both kinematically and photometrically. This central concentration represents the excess of low angular momentum material that long has plagued morphological studies of simulated galaxies and suggests that higher resolutions and a more accurate description of feedback will be required to simulate more realistic galaxies. Even with the excess central mass concentrations, the simulations suggest the important role merger history and halo spin play in the formation of discs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.555

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 communication0.0000.000
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 head0.010
GPT teacher head0.215
Teacher spread0.205 · 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

Citations165
Published2010
Admission routes1
Has abstractyes

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