Weak lensing and CMB: Parameter forecasts including a running spectral index
Bibliographic record
Abstract
We use statistical inference theory to explore the constraints from future galaxy weak lensing (cosmic shear) surveys combined with the current CMB constraints on cosmological parameters, focusing particularly on the running of the spectral index of the primordial scalar power spectrum, ${\ensuremath{\alpha}}_{s}.$ Recent papers have drawn attention to the possibility of measuring ${\ensuremath{\alpha}}_{s}$ by combining the CMB with galaxy clustering and/or the Lyman-$\ensuremath{\alpha}$ forest. Weak lensing combined with the CMB provides an alternative probe of the primordial power spectrum. We run a series of simulations with variable runnings and compare them to semianalytic nonlinear mappings to test their validity for our calculations. We find that a ``reference'' cosmic shear survey with ${f}_{\mathrm{sky}}=0.01$ and $6.6\ifmmode\times\else\texttimes\fi{}{10}^{8}$ galaxies per steradian can reduce the uncertainty on ${n}_{s}$ and ${\ensuremath{\alpha}}_{s}$ by roughly a factor of 2 relative to the CMB alone. We investigate the effect of shear calibration biases on lensing by including the calibration factor as a parameter, and show that for our reference survey, the precision of cosmological parameter determination is only slightly degraded even if the amplitude calibration is uncertain by as much as 5%. We conclude that in the near future weak lensing surveys can supplement the CMB observations to constrain the primordial power spectrum.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".