How Superbubble-Driven Outflows Shape Galaxies And Their Cgm
Bibliographic record
Abstract
M* galaxies, with halo masses ~10^12 Msun, live in an interesting part of parameter space. Not only are they the "turnover" in the galaxy mass Schecter function, they also have the highest stellar mass (and baryon) fraction, very low bulge-to-disk ratios, and dominate the star formation of the epoch they live in. In this talk I will present the results of a sample of 18 cosmological M* galaxies, simulated using the state-of-the-art superbubble method for handling feedback from Type II Supernovae. I will show that the key to obtaining a realistic stellar mass to halo mass relation (SMHMR) is preventing the runaway growth of a massive bulge by driving outflows with large mass-loadings. If this happens, SN feedback alone can no longer effectively drive outflows from the galaxy, and star formation becomes unregulated. This is a key piece of evidence that the peak of the SMHMR is due to the shut down of SN regulation and the beginning of AGN regulation in more massive halos. I will also show how the interaction between hot outflows and the disk ISM, together with the potential well they live within, sets how much mass is entrained in a galactic wind/fountain, and how this can halt SN-driven winds in high- mass galaxies. Finally, I will detail some issues dealing with the chaotic nature of the dynamics in 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".