Constraining primordial and gravitational mode coupling with the position-dependent bispectrum of the large-scale structure
Bibliographic record
Abstract
We develop and study the position-dependent bispectrum. It is a generalization of the recently proposed position-dependent power spectrum method of measuring the squeezed-limit bispectrum. The position-dependent bispectrum can similarly be used to measure the squeezed-limit trispectrum in which one of the wavelengths is much longer than the other three. In this work, we will mainly consider the case in which the three smaller wavelengths are nearly the same (the equilateral configuration). We use the Fisher information matrix to forecast constraints on bias parameters and the amplitude of primordial trispectra from the position-dependent bispectrum method. We find that the method can constrain the local-type ${g}_{\mathrm{NL}}$ at a level of $\ensuremath{\sigma}({g}_{\mathrm{NL}}^{\text{local}})\ensuremath{\approx}3\ifmmode\times\else\texttimes\fi{}1{0}^{5}$ for a large volume SPHEREx-like survey; improvements can be expected by including all the triangular configurations of the bispectra rather than just the equilateral configuration. However, the same measurement would also constrain a much larger family of trispectra than local ${g}_{\mathrm{NL}}$ model. We discuss the implications of the forecasted reach of future surveys in terms of super cosmic variance uncertainties from primordial non-Gaussianities.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".