The diversity of rotation curves of galaxies in the <scp>NewHorizon</scp> cosmological simulation
Bibliographic record
Abstract
ABSTRACT We use the cosmological hydrodynamical simulation NewHorizon to study the effects of the baryonic component on the inner mass profile of dark matter haloes of isolated galaxies ($10^6< M_*/\mathrm{ M}_\odot < 10^{11.5}$). Dark matter deficits (‘cores’) develop only in galaxies in a narrow range of stellar mass, $5\times 10^7< M_*/\mathrm{ M}_\odot < 3\times 10^9$. The lower stellar mass limit arises because a minimum amount of star formation is required to drive the baryonic outflows that redistribute dark matter and create a core. The upper limit roughly coincides with the total amount of dark matter initially contained within the innermost 2 kpc ($1 \pm 0.5 \times 10^{9}\, \mathrm{ M}_\odot$), which roughly coincides with the stellar half-mass radius of these dwarfs. This enclosed mass is quite insensitive to the total virial mass of the system. The same upper limit applies to other simulations, like NIHAO and EAGLE-CHT10, despite their rather different galaxy formation efficiencies. This suggests that it is the galaxy total stellar mass that determines when a core is formed, and not the galaxy-to-dark halo mass ratio, as argued in earlier work. This is consistent with a back-of-the-envelope estimate for a SN-induced rate of orbital diffusion. Although NewHorizon dwarfs reproduce the observed diversity of rotation curves better than other simulations, there are significant differences in the gravitational importance of baryons in the inner regions of dwarfs compared to observations. These differences prevent us from concluding that cosmological simulations are currently fully able to account for the observed diversity of rotation curve shapes.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".