How to Reconcile the Observed Velocity Function of Galaxies with Theory
Bibliographic record
Abstract
Abstract Within a Λ cold dark matter (ΛCDM) scenario, we use high-resolution cosmological simulations spanning over four orders of magnitude in galaxy mass to understand the deficit of dwarf galaxies in observed velocity functions (VFs). We measure velocities in as similar a way as possible to observations, including generating mock H i data cubes for our simulated galaxies. We demonstrate that this apples-to-apples comparison yields an “observed” VF in agreement with observations, reconciling the large number of low-mass halos expected in a ΛCDM cosmological model with the low number of observed dwarfs at a given velocity. We then explore the source of the discrepancy between observations and theory and conclude that the dearth of observed dwarf galaxies is primarily explained by two effects. The first effect is that galactic rotational velocities derived from the H i linewidth severely underestimate the maximum halo velocity. The second effect is that a large fraction of halos at the lowest masses are too faint to be detected by current galaxy surveys. We find that cored DM density profiles can contribute to the lower observed velocity of galaxies but only for galaxies in which the velocity is measured interior to the size of the core (∼3 kpc).
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".