A hydrodynamical study of outflows in starburst galaxies with different driving mechanisms
Bibliographic record
Abstract
ABSTRACT Outflows from starburst galaxies can be driven by thermal pressure, radiation, and cosmic rays. We present an analytic phenomenological model that accounts for these contributions simultaneously to investigate their effects on the hydrodynamical properties of outflows. We assess the impact of energy injection, wind opacity, magnetic field strength, and the mass of the host galaxy on flow velocity, temperature, density, and pressure profiles. For an M82-like wind, a thermally dominated driving mechanism is found to deliver the fastest and hottest wind. Radiation-driven winds in typical starburst-galaxy configurations are unable to attain the higher flow velocities and temperatures associated with thermal and cosmic ray-driven systems, leading to higher wind densities which would be more susceptible to cooling and fragmentation at lower altitudes. High opacity winds are more sensitive to radiative driving, but terminal flow velocities are still lower than those achieved by other driving mechanisms at realistic opacities. We demonstrate that variations in the outflow magnetic field can influence its coupling with cosmic rays, where stronger fields enable greater streaming but less driving near the base of the flow, instead with cosmic rays redirecting their driving impact to higher altitudes. The gravitational potential is less important in M82-like wind configurations, and substantial variations in the flow profiles only emerge at high altitude in massive haloes. This model offers a more generalized approach to examine the large-scale hydrodynamical properties for a wide variety of starburst galaxies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".