Bibliographic record
Abstract
Halo models of large scale structure provide powerful and indispensable tools for phenomenological understanding of the clustering of matter in the Universe. While the halo model builds structures out of the superposition of haloes, defining halo profiles in their outskirts---beyond their virial radii---becomes increasingly ambiguous, as one cannot assign matter to individual haloes in a clear way. In this paper, we address this issue by finding a systematic definition of mean halo profile that can be extended to large distances---beyond the virial radius of the halo---and matched to simulation results. These halo profiles are compensated and are the key ingredients for the computation of cosmological correlation functions in an amended halo model. The latter, introduced in our earlier work [A. Y. Chen and N. Afshordi, Phys. Rev. D 101, 103522 (2020)], provides a more physically accurate phenomenological description of nonlinear structure formation, which respects conservation laws on large scales. Here, we show that this model can be extended from the matter auto-power spectrum to the halo-matter cross-power spectra by using data from $N$-body simulations. Furthermore, we find that this (dimensionless) definition of the compensated halo profile, ${r}^{3}\ifmmode\times\else\texttimes\fi{}\ensuremath{\rho}(r)/{M}_{200c}$, has a near-universal maximum in the small range of 0.03--0.04 around the virial radius, $r\ensuremath{\simeq}{r}_{200\mathrm{c}}$, nearly independent of the halo mass. The profiles cross zero into negative values in the halo outskirts---beyond $2--3\ifmmode\times\else\texttimes\fi{}{r}_{200\mathrm{c}}$---consistent with our previous results. We provide a preliminary fitting function for the compensated halo profiles (extensions of Navarro-Frenk-White profiles), which can be used to compute more physical observables in large scale structure.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".