Primordial non-Gaussianity, statistics of collapsed objects, and the integrated Sachs-Wolfe effect
Bibliographic record
Abstract
Any hint of non-Gaussianity in the cosmological initial conditions will provide us with a unique window into the physics of the early Universe. We show that the impact of a small local primordial non-Gaussianity (generated on superhorizon scales) on the statistics of collapsed objects (such as galaxies or clusters) can be approximated by using slightly modified, but Gaussian, initial conditions, which we describe through simple analytic expressions. Given that numerical simulations with Gaussian initial conditions are relatively well studied, this equivalence provides us with a simple tool to predict signatures of primordial non-Gaussianity in the statistics of collapsed objects. In particular, we describe the predictions for non-Gaussian mass function, and also confirm the recent discovery of a nonlocal bias on large scales [N. Dalal, O. Dore, D. Huterer, and A. Shirokov, Phys. Rev. D 77, 123514 (2008).][S. Matarrese and L. Verde, Astrophys. J. 677, L77 (2008).], as a signature of primordial non-Gaussianity. We then study the potential of galaxy surveys to constrain non-Gaussianity using their autocorrelation and cross correlation with the cosmic microwave background (CMB) (due to the integrated Sachs-Wolfe effect), as a function of survey characteristics, and predict that they will eventually yield an accuracy of $\ensuremath{\Delta}{f}_{NL}\ensuremath{\sim}0.1$ and 3, respectively, which will be better than or competitive with (but independent of) the best predicted constraints from the CMB. Interestingly, the cross correlation of the CMB and the NRAO VLA Sky Survey galaxy survey already shows a hint of a large local primordial non-Gaussianity: ${f}_{NL}=236\ifmmode\pm\else\textpm\fi{}127$.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".