Towards Robust Quantification of Cosmological Errors
Bibliographic record
Abstract
The method of baryon acoustic oscillation (BAO) is among the best probes of the dark energy equation of state,\nand worldwide efforts are being invested in order to perform measurements that are accurate at the percent level.\nIn current data analyses, however, estimates of the error about the BAO are based on the assumption\nthat the density field can be treated as Gaussian, an assumption that becomes less accurate as smaller scales are included in the measurement.\nIt was recently shown from large samples of N-body simulations that the error bars about the BAO obtained this way are in fact up to 15-20 per cent too small.\nThis important bias has shaken the confidence in the way error bars are calculated, and is motivating developments of analyses pipelines that include non-Gaussian features in the matter density fields.\n\nIn this thesis, we propose general strategies to incorporate non-Gaussian effects in the context of a survey. \nAfter describing the high performance N-body code that we used, we present novel properties of the non-Gaussian uncertainty about\nthe matter power spectrum, and explain how these combine with a general survey selection function.\nAssuming that the non-Gaussian features that are observed in the simulations correspond to those of Nature, \nthis approach is the first unbiased measurement of the error bar about the power spectrum, which simultaneously removes the undesired bias on the BAO error.\nWe then relax this assumption about the similitude of the non-Gaussian natures in simulations and data, \nand develop tools that aim at measuring the non-Gaussian error bars exclusively from the data.\n\nIt is possible to improve the constraining power of non-Gaussian analyses \nwith `Gaussianizations' techniques, which map the observed fields into something more Gaussian.\nWe show that two of such techniques maximally recover degrees of freedom that were lost in the gravitational collapse.\nFinally, from a large sample of high resolution N-body realizations, we construct a series of weak gravitational lensing distortion maps\n and provide high resolution halo catalogues that are used by the CFTHLenS community to calibrate their estimators and study many secondary effects with unprecedented\n accuracy.
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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 | 0.015 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".