Independent constraints on local non-Gaussianity from the peculiar velocity and density fields
Bibliographic record
Abstract
Primordial, non-Gaussian perturbations can generate scale-dependent bias in the galaxy distribution. This in turn will modify correlations between galaxy positions and peculiar velocities at late times, since peculiar velocities reflect the underlying matter distribution, whereas galaxies are a biased tracer of the same. We study this effect, and show that non-Gaussianity can be constrained by comparing the observed peculiar velocity field to a model velocity field reconstructed from the galaxy density field assuming linear bias. The amplitude of the spatial correlations in the residual map obtained after subtracting one velocity field from the other is directly proportional to the strength of the primordial non-Gaussianity. We construct the corresponding likelihood function and use it to constrain the amplitude of the linear flow β and the amplitude of local non-Gaussianity |$f^{\rm {local}}_{\rm {NL}}$|. Applying our method to two observational data sets, the Type-Ia supernovae (A1SN) and Spiral Field I-band (SFI++) catalogues, we obtain constraints on the linear flow parameter consistent with the values derived previously assuming Gaussianity. The marginalized 1D distribution of |$\,|f^{\rm {local}}_{\rm {NL}}|$| does not show strong evidence for non-zero |$f^{\rm {local}}_{\rm {NL}}$|, and we set 95 per cent upper limits |$\,|f^{\rm {local}}_{\rm {NL}}|<51.4$| from A1SN and |$\,|f^{\rm {local}}_{\rm {NL}}|<92.6$| from SFI++. These limits on |$f^{\rm {local}}_{\rm {NL}}$| are as tight as any set by previous large-scale structure measurements. Our method can be applied to any survey with radial velocities and density field data, and provides an independent check of recent CMB constraints on |$f^{\rm {local}}_{\rm {NL}}$|, extending these to smaller spatial scales.
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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 | 0.003 | 0.018 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".