HOT GALACTIC WINDS CONSTRAINED BY THE X-RAY LUMINOSITIES OF GALAXIES
Bibliographic record
Abstract
Galactic superwinds may be driven by very hot outflows generated by overlapping supernovae within the host galaxy. We use the Chevalier & Clegg (CC85) wind model and the observed correlation between X-ray luminosities of galaxies and their star formation rates (SFRs) to constrain the mass-loss rates ( ) across a wide range of SFRs, from dwarf starbursts to ultraluminous infrared galaxies. We show that for fixed thermalization and mass-loading efficiencies, the X-ray luminosity of the hot wind scales as L X ∝SFR 2 , significantly steeper than is observed for star-forming galaxies: L X ∝SFR. Using this difference, we constrain the mass-loading and thermalization efficiency of hot galactic winds. For reasonable values of the thermalization efficiency (≲ 1) and for SFR ≳ 10 M ☉ yr −1 we find that , which is significantly lower than required by integrated constraints on the efficiency of stellar feedback in galaxies and potentially too low to explain observations of winds from rapidly star-forming galaxies. In addition, we highlight the fact that heavily mass-loaded winds cannot be described by the adiabatic CC85 model because they become strongly radiative.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".