Estimating the impact of recombination uncertainties on the cosmological parameter constraints from cosmic microwave background experiments
Bibliographic record
Abstract
We use our most recent training set for the rico code to estimate the impact of recombination uncertainties on the posterior probability distributions which will be obtained from future cosmic microwave background experiments, and in particular the Planck satellite. Using a Monte Carlo Markov Chain (MCMC) analysis to sample the posterior distribution of the cosmological parameters, we find that Planck will have biases of −0.7, −0.3 and −0.4σ for nS, Ωbh2 and log(1010AS), respectively, in the minimal six-parameter Λ cold dark matter model, if the description of the recombination history given by rico is not used. The remaining parameters (e.g. τ or Ωdmh2) are not significantly affected. We also show that the cosmology dependence of the corrections to the recombination history modelled with rico has a negligible impact on the posterior distributions obtained for the case of the Planck satellite. In practice, this implies that the inclusion of additional corrections to existing recombination codes can be achieved using simple cosmology-independent ‘fudge functions’. Finally, we also investigated the impact of some recent improvements in the treatment of hydrogen recombination which are still not included in the current version of our training set for rico, by assuming that the cosmology dependence of those corrections can be neglected. In summary, with our current understanding of the complete recombination process, the expected biases in the cosmological parameters inferred from Planck might be as large as −2.3, −1.7 and −1σ for nS, Ωbh2 and log(1010AS), respectively, if all those corrections are not taken into account. We note that although the list of physical processes that could be of importance for Planck seems to be nearly complete, still some effort has to be put into the validation of the results obtained by the different groups. The new rico training set as well as the fudge functions used for this paper are publicly available on the rico webpage.
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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 | 0.007 | 0.033 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".