All about baryons: revisiting SIDM predictions at small halo masses
Bibliographic record
Abstract
We use cosmological hydrodynamic simulations to consistently compare the assembly of dwarf galaxies in both Λ dominated, cold dark matter (CDM) and self-interacting dark matter (SIDM) models. The SIDM model adopts a constant cross-section of 2 cm2 g−1, a relatively large value to maximize its effects. These are the first SIDM simulations that are combined with a description of stellar feedback that naturally drives potential fluctuations able to create dark matter (DM) cores. Remarkably, SIDM fails to significantly lower the central DM density within the central 500 pc at halo peak velocities Vmax < 30 km s−1. This is due to the fact that the central regions of very low mass field haloes have relatively low central velocity dispersion and densities, leading to time-scales for SIDM collisions greater than a Hubble time. CDM haloes with Vmax < 30 km s−1 have inefficient star formation, and hence weak supernova feedback. At a fixed 2 cm2 g−1 SIDM cross-section, the DM content of very low mass CDM and SIDM haloes differs by no more than a factor of 2 within 100–200 pc. At larger halo masses (∼1010 M⊙), the introduction of baryonic processes creates field dwarf galaxies with DM cores and central DM+baryon distributions that are effectively indistinguishable between CDM and SIDM. Both models are in broad agreement with observed Local Group field galaxies across the range of masses explored. To significantly differentiate SIDM from CDM at the scale of faint dwarf galaxies, a velocity-dependent cross-section that rapidly increases to values larger than 2 cm2 g−1 for haloes with Vmax < 25–30 km s−1 needs to be introduced.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".