Unveiling the cosmological information beyond linear scales: forecasts for sufficient statistics
Bibliographic record
Abstract
Beyond the linear regime, Fourier modes of cosmological random fields become correlated, and the power spectrum of density fluctuations contains only a fraction of the available cosmological information. To unveil this formerly hidden information, the A* non-linear transform was introduced; it is optimized both for the nonlinearities induced by gravity and observational noise. Quantifying the resulting increase of our knowledge of cosmological parameters, we forecast the constraints from the angular power spectrum and that of A* from l ~ 200 to 3000 for upcoming galaxy surveys such as: the Wide-Field Infrared Survey Telescope (WFIRST), the Large Synoptic Survey Telescope (LSST), Euclid, the Hyper Suprime-Cam (HSC) and the Dark Energy Survey (DES). We find that at low redshifts this new data analysis strategy can double the extracted information, effectively doubling the survey area. To test the accuracy of our forecasting and the power of our data analysis methods, we apply the A* transformation to the latest release of the Canada-France-Hawaii-Telescope Legacy Survey (CFHTLS) Wide. While this data set is too sparse to allow for more than modest gains (~1.1-1.2), the realized gain from our method is in excellent agreement with our forecast, thus verifying the robustness of our analysis and prediction pipelines.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".