The structure of the interstellar medium of star-forming galaxies
Bibliographic record
Abstract
We develop and implement numerical methods for including stellar feedback in galaxy‐scale numerical simulations. Our models include simplified treatments of heating by Type I and Type II supernovae, gas recycling from young stars and asymptotic giant branch winds, heating from the shocked stellar winds, H ii photoionization heating and radiation pressure from stellar photons. The energetics and time dependence associated with the feedback are taken directly from stellar evolution models. We implement these stellar feedback models in smoothed particle hydrodynamic simulations with pc‐scale resolution, modelling galaxies from Small Magellanic Cloud (SMC) like dwarfs and Milky Way (MW) analogues to massive z∼ 2 star‐forming discs. In the absence of stellar feedback, gas cools rapidly and collapses without limit into dense sub‐units, inconsistent with observations. By contrast, in all cases with feedback, the interstellar medium (ISM) quickly approaches a statistical steady state in which giant molecular clouds (GMCs) continuously form, disperse and re‐form, leading to a multiphase ISM. In this paper, we quantify the properties of the ISM and GMCs in this self‐regulated state. In a companion paper we study the galactic winds driven by stellar feedback. Our primary results on the structure of the ISM in star‐forming galaxies include the following. Star‐forming galaxies generically self‐regulate so that the cool, dense gas maintains Toomre’s Q∼ 1. Most of the volume is occupied by relatively diffuse hot gas, while most of the mass is in dense GMC complexes created by self‐gravity. The phase structure of the gas and the gas mass fraction at high densities are much more sensitive probes of the physics of stellar feedback than integrated quantities such as the Toomre Q or gas velocity dispersion. Different stellar feedback mechanisms act on different spatial (and density) scales. Radiation pressure and H ii gas pressure are critical for preventing runaway collapse of dense gas in GMCs. Shocked supernova ejecta and stellar winds dominate the dynamics of the volume‐filling hot gas. However, this gas primarily vents out of the star‐forming disc and contributes only modestly to the mid‐plane ISM pressure. The galaxy‐averaged star formation rate is determined by feedback, with different mechanisms dominating in different galaxy types. For a given feedback efficiency, restricting star formation to molecular gas or modifying the cooling function has little effect on the star formation rate in the galaxies we model (including an SMC‐mass dwarf). By contrast, changing the feedback mechanisms or assumed feedback efficiencies directly translates to shifts off of the observed Kennicutt–Schmidt relation. Self‐gravity leads to GMCs with an approximately self‐similar mass function ∝M−2, with a high‐mass cut‐off determined by the characteristic Jeans/Toomre mass of the system. In all of our galaxy models, GMCs live for a few dynamical times before they are disrupted by stellar feedback. The net star formation efficiency in GMCs ranges from ∼1 per cent in dwarfs and MW‐like spirals to nearly ∼10 per cent in gas‐rich rapidly star‐forming galaxies. GMCs are approximately virialized, but there is a large dispersion in the virial parameter for a given GMC mass, and lower mass GMCs tend to be preferentially unbound.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".