IMPACTS OF DARK STARS ON REIONIZATION AND SIGNATURES IN THE COSMIC MICROWAVE BACKGROUND
Bibliographic record
Abstract
We perform a detailed and systematic investigation of the possible impacts of dark stars on the reionization history of the universe, and its signatures in the cosmic microwave background (CMB). We compute hydrogen reionization histories, CMB optical depths, and anisotropy power spectra for a range of stellar populations including dark stars. If dark stars capture large amounts of dark matter (DM) via nuclear scattering, reionization can be substantially delayed, leading to decreases in the integrated optical depth to last scattering and large-scale power in the EE polarization power spectrum. Using the integrated optical depth observed by the Wilkinson Microwave Anistropy Probe seven-year mission, in our canonical reionization model we rule out the section of parameter space where dark stars with high scattering-induced capture rates tie up ≳ 90% of all the first star-forming baryons, and live for ≳ 250 Myr. When nuclear scattering delivers only moderate amounts of DM, reionization can instead be sped up slightly, modestly increasing the CMB optical depth. If dark stars do not obtain any DM via nuclear scattering, effects on reionization and the CMB are negligible. The effects of dark stars on reionization and its CMB markers can be largely mimicked or compensated for by changes in the existing parameters of reionization models, making dark stars difficult to disentangle from astrophysical uncertainties, but also widening the range of standard parameters in reionization models that can be made consistent with observations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".