Forged in fire: cusps, cores and baryons in low-mass dwarf galaxies
Bibliographic record
Abstract
We present multiple ultrahigh resolution cosmological hydrodynamic simulations of M⋆ ≃ 104–6.3 Mȯ dwarf galaxies that form within two Mvir = 109.5–10 Mȯ dark matter halo initial conditions. Our simulations rely on the Feedback in Realistic Environments (fire) implementation of star formation feedback and were run with high enough force and mass resolution to directly resolve structure on the ∼200 pc scales. The resultant galaxies sit on the M⋆ versus Mvir relation required to match the Local Group stellar mass function via abundance matching. They have bursty star formation histories and also form with half-light radii and metallicities that broadly match those observed for local dwarfs at the same stellar mass. We demonstrate that it is possible to create a large (∼1 kpc) constant-density dark matter core in a cosmological simulation of an M⋆ ≃ 106.3 Mȯ dwarf galaxy within a typical Mvir = 1010 Mȯ halo – precisely the scale of interest for resolving the ‘too big to fail’ problem. However, these large cores are not ubiquitous and appear to correlate closely with the star formation histories of the dwarfs: dark matter cores are largest in systems that form their stars late (z ≲ 2), after the early epoch of cusp building mergers has ended. Our M⋆ ≃ 104 Mȯ dwarf retains a cuspy dark matter halo density profile that matches that of a dark-matter-only run of the same system. Though ancient, most of the stars in our ultrafaint form after reionization; the ultraviolet field acts mainly to suppress fresh gas accretion, not to boil away gas that is already present in the protodwarf.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".