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Record W2808606823 · doi:10.3847/1538-4357/aacce2

Effect of the Heating Rate on the Stability of the Three-phase Interstellar Medium

2018· article· en· W2808606823 on OpenAlexaff
Alex S. Hill, Mordecai‐Mark Mac Low, Andrea Gatto, Juan C. Ibáñez-Mejía

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsNational Research Council CanadaUniversity of British Columbia
Fundersnot available
KeywordsPhysicsInterstellar mediumAstrophysicsPhase (matter)AstronomyInterstellar cloudAstrochemistryAstrobiologyGalaxy

Abstract

fetched live from OpenAlex

Abstract We investigate the impact of the far-UV (FUV) heating rate on the stability of the three-phase interstellar medium using three-dimensional simulations of a 1 kpc 2 , vertically extended domain. The FUV heating rate sets the range of thermal pressures across which the cold ( ∼ 10 2 K ) and warm ( ∼ 10 4 K ) neutral media (CNM and WNM) can coexist in equilibrium. Even absent a variable star formation rate regulating the FUV heating rate, the gas physics keeps the pressure in the two-phase regime: because radiative heating and cooling processes happen on shorter timescales than sound wave propagation, turbulent compressions tend to keep the interstellar medium within the CNM–WNM pressure regime over a wide range of heating rates. The thermal pressure is set primarily by the heating rate with little influence from the hydrostatics. The vertical velocity dispersion adjusts as needed to provide hydrostatic support given the thermal pressure: when the turbulent pressure 〈 ρ 〉 σ z 2 is calculated over scales ≳ 500 pc , the thermal plus turbulent pressure approximately equals the weight of the gas. The warm gas volume filling fraction is 0.2 < f w < 0.8 over a factor of less than three in heating rate, with f w near unity at higher heating rates and near zero at lower heating rates. We suggest that cosmological simulations that do not resolve the CNM should maintain an interstellar thermal pressure within the two-phase regime.

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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
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)0.0020.000

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 head0.013
GPT teacher head0.266
Teacher spread0.254 · 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

Citations34
Published2018
Admission routes1
Has abstractyes

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