Precise cosmological parameter estimation using CosmoRec
Bibliographic record
Abstract
We use the new cosmological recombination code, CosmoRec, for parameter estimation in the context of (future) precise measurements of the cosmic microwave background (CMB) temperature and polarization anisotropies. We address the question of how previously neglected physical processes in the recombination model of Recfast affect the determination of key cosmological parameters, for the first time performing a model-by-model computation of the recombination problem. In particular, we ask how the biases depend on different combinations of parameters, e.g. when varying the helium abundance or the effective number of neutrino species in addition to the standard six parameters. We forecast how important the recombination corrections are for Planck, and a survey cosmic-variance limited up to l = 2000. For example, we find that for a Λ cold dark matter (ΛCDM) analysis, ns may be biased by over 3σ and 7σ in each case, whereas when Yp is allowed to vary its value can be biased by over 2σ and 7σ. We find similar shifts for other parameter combinations. Furthermore, we ask which recombination corrections are really crucial for CMB parameter estimation, and ask whether an approach based on a redshift-dependent correction function to Recfast is sufficient.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".