Optimizing the recovery of Fisher information in the dark matter power spectrum
Bibliographic record
Abstract
We examine and combine different techniques that are known to increase the Fisher information content about the amplitude of the matter power spectrum, and propagate their impact on a baryonic acoustic oscillation (BAO) measurement. We compare a density reconstruction algorithm based on Zel'dovich displacement fields, a wavelet non-linear Wiener filter, a direct Gaussianization of the probability distribution function of the wavelet function coefficients and the action of the last two techniques on the first one. From a series of 200 N-body simulations, we compute the Fisher information and quantify the recovery performance, both using dark matter particles and haloes. We find that the height of the Fisher information trans-linear plateau is generally increased by the various techniques, by up to an order of magnitude at k = 0.6 h Mpc−1; however, shot noise subtracted halo measurements exhibit a milder information recovery. When we perform a BAO measurement from these altered density fields, we find that the reconstruction technique is the only one that sharpens the peak; the two wavelet-based techniques in fact smear out the features, thus reducing the overall precision of the cosmological ladder. We examine in detail why this occurs even though their Fisher information increased.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".