Constraints on warm dark matter models from the high redshift Universe
Bibliographic record
Abstract
Warm Dark Matter (WDM) models have recently been resurrected to resolve apparent conflicts of Cold Dark Matter (DM) models with observations.Endowing the DM particles with non-negligible velocities causes free-streaming, which suppresses the primordial power spectrum on small scales.The choice of a root-mean-square velocity dispersion v rms,0 ∼ 0.05 km/s at redshift z = 0 (corresponding to a particle mass m X ∼ 1 keV if the WDM particles are fermions decoupling while relativistic) helps alleviate most, but probably not all, of the small-scale problems faced by CDM.An important side-effect of the particle velocities is the severe decrease in the number of collapsed halos at high redshift.This is caused both by the loss of small-scale power, and by the delay in the collapse of the smallest individual halos (with masses near the effective Jeans mass of the DM).The presence of early halos is required in order (1) to host either early quasars or galaxies that can reionize the universe by redshift z = 5.8, and (2) to allow the growth of the supermassive black hole believed to power the recently discovered quasar SDSS 1044-1215 at this redshift.We quantify these constraints using a modified Press-Schechter formalism, and find v rms,0 ∼ < 0.04 km/s (or m X ∼ > 1 keV).If future observations uncover massive black holes at z ∼ > 10, or reveal that reionization occurred at z ∼ > 10, this could conclusively rule out WDM models as the solution to the small-scale crisis of the CDM paradigm.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".