Estimates of Cosmological Parameters Using the Cosmic Microwave Background Angular Power Spectrum of ACBAR
Bibliographic record
Abstract
We report an investigation of cosmological parameters based on the measurements of anisotropy in the cosmic microwave background radiation (CMB) made by the Arcminute Cosmology Bolometer Array Receiver (ACBAR). We use the ACBAR data in concert with other recent CMB measurements to derive Bayesian estimates of parameters in inflation-motivated adiabatic cold dark matter models. We apply a series of additional cosmological constraints on the shape and amplitude of the density power spectrum and the Hubble parameter and from supernovae to further refine our parameter estimates. Previous estimates of parameters are confirmed, with sensitive measurements of the power spectrum now ranging from l ~ 3 to 2800. Comparing individual best-fit models, we find that the addition of Ω Λ as a parameter dramatically improves the fits. We also use the high- l data of ACBAR, along with similar data from the Cosmic Background Imager and Berkeley-Illinois-Maryland Association array, to investigate potential secondary anisotropies from the Sunyaev-Zel'dovich effect. We show that the results from the three experiments are consistent under this interpretation and use the data, combined and individually, to estimate σ 8 from the Sunyaev-Zel'dovich component.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".