Redshift space distortions in Lagrangian space and the linear large scale velocity field of dark matter
Bibliographic record
Abstract
Untangling the connection between redshift space coordinates, a velocity measurement, and three dimensional real space coordinates is a cosmological problem that is often modeled through a linear understanding of the velocity-position coupling. This linear information is better preserved in the Lagrangian space picture of the matter density field. Through Lagrangian space measurements, we can extract more information and make more accurate estimates of the linear growth rate of the Universe. In this paper, we address the linear modeling of matter particle velocities through transfer functions, and in doing so examine to what degree the decrease in correlation with initial conditions may be contaminated by velocity-based nonlinearities. With a thorough analysis of the monopole-quadrupole ratio, we find the best-fitting values for the Eulerian velocity dispersion, ${\ensuremath{\sigma}}_{p}=378.3\text{ }\text{ }\mathrm{km}/\mathrm{s}$ for a Lorentzian finger-of-God damping factor and ${\ensuremath{\sigma}}_{p}=254.6\text{ }\text{ }\mathrm{km}/\mathrm{s}$ for a Gaussian one. The covariance of the cosmological linear growth rate $f$ is estimated in the Eulerian and Lagrangian cases. Comparing Lagrangian and Eulerian, we find that the error in $f$ improves by a factor of 3, without the need for nonlinear velocity dispersion modeling.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| 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".