Bursting with Feedback: The Relationship between Feedback Model and Bursty Star Formation Histories in Dwarf Galaxies
Bibliographic record
Abstract
Abstract Due to their inability to self-regulate, ultrafaint dwarfs are sensitive to prescriptions in subgrid physics models that converge and regulate at higher masses. We use high-resolution cosmological simulations to compare the effect of bursty star formation histories (SFHs) on dwarf galaxy structure for two different subgrid supernova (SN) feedback models, superbubble and blastwave, in dwarf galaxies with stellar masses from 5000 < M * / M ⊙ < 10 9 . We find that in the “MARVEL-ous Dwarfs” suite both feedback models produce cored galaxies and reproduce observed scaling relations for luminosity, mass, and size. Our sample accurately predicts the average stellar metallicity at higher masses, however low-mass dwarfs are metal poor relative to observed galaxies in the Local Group. We show that continuous bursty star formation and the resulting stellar feedback are able to create dark matter (DM) cores in the higher dwarf galaxy mass regime, while the majority of ultrafaint and classical dwarfs retain cuspy central DM density profiles. We find that the effective core formation peaks at M * / M halo ≃ 5 × 10 −3 for both feedback models. Both subgrid SN models yield bursty SFHs at higher masses; however, galaxies simulated with superbubble feedback reach maximum mean burstiness values at lower stellar mass fractions relative to blastwave feedback. As a result, core formation may be better predicted by stellar mass fraction than the burstiness of SFHs.
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 | 0.000 | 0.003 |
| 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 | 0.000 |
| 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".