On the road to percent accuracy: non-linear reaction of the matter power spectrum to dark energy and modified gravity
Bibliographic record
Abstract
ABSTRACT We present a general method to compute the non-linear matter power spectrum for dark energy (DE) and modified gravity scenarios with per cent-level accuracy. By adopting the halo model and non-linear perturbation theory, we predict the reaction of a lambda cold dark matter (ΛCDM) matter power spectrum to the physics of an extended cosmological parameter space. By comparing our predictions to N-body simulations we demonstrate that with no-free parameters we can recover the non-linear matter power spectrum for a wide range of different w0–wa DE models to better than 1 per cent accuracy out to k ≈ 1 $h \,{\rm Mpc}^{-1}$. We obtain a similar performance for both DGP and f(R) gravity, with the non-linear matter power spectrum predicted to better than 3 per cent accuracy over the same range of scales. When including direct measurements of the halo mass function from the simulations, this accuracy improves to 1 per cent. With a single suite of standard ΛCDM N-body simulations, our methodology provides a direct route to constrain a wide range of non-standard extensions to the concordance cosmology in the high signal-to-noise non-linear regime.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".