Cosmic bulk flows on 50 h−1 Mpc scales: a Bayesian hyper-parameter method and multishell likelihood analysis
Bibliographic record
Abstract
It has been argued recently that the galaxy peculiar velocity field provides evidence of excessive power on scales of 50 h−1 Mpc, which seems to be inconsistent with the standard Λ cold dark matter (ΛCDM) cosmological model. We discuss several assumptions and conventions used in studies of the large-scale bulk flow to check whether this claim is robust under a variety of conditions. Rather than using a composite catalogue we select samples from the SN, ENEAR, Spiral Field I-band Survey (SFI++) and First Amendment Supernovae (A1SN) catalogues, and correct for Malmquist bias in each according to the IRAS PSCz density field. We also use slightly different assumptions about the small-scale velocity dispersion and the parametrization of the matter power spectrum when calculating the variance of the bulk flow. By combining the likelihood of individual catalogues using a Bayesian hyper-parameter method, we find that the joint likelihood of the amplitude parameter gives σ8 = 0.65+ 0.47− 0.35 (68 per cent confidence region), which is entirely consistent with the ΛCDM model. In addition, the bulk flow magnitude, v ∼ 310 km s−1, and direction, (l, b) ∼ (280° ± 8°, 5| ${.\!\!\!\!\!\!^{\circ}}$|1 ± 6°), found by each of the catalogues are all consistent with each other, and with the bulk flow results from most previous studies. Furthermore, the bulk flow velocities in different shells of the surveys constrain (σ8, Ωm) to be (1.01+ 0.26− 0.20, 0.31+ 0.28− 0.14) for SFI++ and (1.04+ 0.32− 0.24, 0.28+ 0.30− 0.14) for ENEAR, which are consistent with the 7-year Wilkinson and Microwave Anisotropy Probe (WMAP7) best-fitting values. We finally discuss the differences between our conclusions and those of the studies claiming the largest bulk flows.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".