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Record W6948239151 · doi:10.48550/arxiv.1506.08785

NIHAO III: The constant disc gas mass conspiracy

2015· preprint· en· W6948239151 on OpenAlexaff

Bibliographic record

VenuearXiv (Cornell University) · 2015
Typepreprint
Languageen
FieldChemistry
TopicChromatography in Natural Products
Canadian institutionsMcMaster University
Fundersnot available
KeywordsHaloStar formationGalaxyVirial massSupernovaVirial theoremStellar massDisc galaxy

Abstract

fetched live from OpenAlex

We show that the cool gas masses of galactic discs reach a steady state that lasts many Gyr after their last major merger in cosmological hydrodynamic simulations. The mass of disc gas, M$_{\rm gas}$, depends upon a galaxy halo's spin and virial mass, but not upon stellar feedback. Halos with low spin have high star formation efficiency and lower disc gas mass. Similarly, lower stellar feedback leads to more star formation so the gas mass ends up nearly the same irregardless of stellar feedback strength. Even considering spin, the M$_{\rm gas}$ relation with halo mass, M$_{200}$ only shows a factor of 3 scatter. The M$_{\rm gas}$--M$_{200}$ relation show a break at M$_{200}$=$2\times10^{11}$ M$_\odot$ that corresponds to an observed break in the M$_{\rm gas}$--M$_\star$ relation. The constant disc mass stems from a shared halo gas density profile in all the simulated galaxies. In their outer regions, the profiles are isothermal. Where the profile rises above $n=10^{-3}$ cm$^{-3}$, the gas readily cools and the profile steepens. Inside the disc, rotation supports gas with a flatter density profile except where supernova explosions disrupt the disc. Energy injection from stellar feedback also provides pressure support to the halo gas to prevent runaway cooling flows. The resulting constant gas mass makes simpler models for galaxy formation possible, either using a "bathtub" model for star formation rates or when modeling chemical evolution.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.066
GPT teacher head0.192
Teacher spread0.126 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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