Accurate Determination of Halo Velocity Bias in Simulations and Its Cosmological Implications
Bibliographic record
Abstract
Abstract A long-standing issue in peculiar velocity cosmology is whether the halo/galaxy velocity bias b v = 1 at large scale. The resolution of this important issue must resort to high-precision cosmological simulations. However, this is hampered by another long-standing “sampling artifact” problem in volume-weighted velocity measurement. We circumvent this problem with a hybrid approach. We first measure the statistics free of sampling artifacts, then link them to volume-weighted statistics in theory, and finally solve for the velocity bias. b v (determined by our method) is not only free of sampling artifacts but also free of cosmic variance. We apply this method to a ΛCDM N-body simulation of 30723 particles and box size. For the first time, we determine the halo velocity bias to 0.1%–1% accuracy. Our major findings are as follows: (1) at . The deviation from unity ( ) increases with k. Depending on halo mass and redshift, it may reach at and at . The discovered has a statistically significant impact on the structure growth rate measurement by spectroscopic redshift surveys, including DESI, Euclid, and SKA. (2) Both the sign and the amplitude of b v − 1 depend on mass and redshift. These results disagree with the peak model prediction in that b v has much weaker deviation from unity, varies with redshift, and can be bigger than unity. (3) Most of the mass and redshift dependences can be compressed into a single dependence on the halo density bias. Based on this finding, we provide an approximate two-parameter fitting formula.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".