Emulating Power Spectra for Prereconstructed and Postreconstructed Galaxy Samples
Bibliographic record
Abstract
Abstract The small-scale linear information in galaxy samples typically lost during nonlinear growth can be restored to a certain level by the density field reconstruction, which has been demonstrated for improving the precision of the baryon acoustic oscillation (BAO) measurements. As proposed in the literature, a joint analysis of the power spectrum before and after the reconstruction enables an efficient extraction of information carried by high-order statistics. However, the statistics of the postreconstruction density field are difficult to model. In this work, we circumvent this issue by developing an accurate emulator for the prereconstructed, postreconstructed, and cross-power spectra ( P pre , P post, P cross) up to k = 0.5 h Mpc−1 based on the Dark Quest N-body simulations. The accuracy of the emulator is at the percent level; namely, the error of the emulated monopole and quadrupole of the power spectra is less than 1% and 10% of the ground truth, respectively. A fit to an example power spectrum using the emulator shows that the constraints on cosmological parameters get largely improved using P pre +P post+P cross with k max = 0.25 h Mpc − 1 , compared to that derived from P pre alone; namely, the constraints on (Ω m , H 0, σ 8) are tightened by ∼41%–55%, and the uncertainties of the derived BAO and RSD parameters (α ⊥, α ∣∣, f σ 8) shrink by ∼28%–54%, respectively. This highlights the complementarity among P pre , P post, and P cross, which demonstrates the efficiency and practicability of a joint P pre , P post, and P cross analysis for cosmological implications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".