Non-Gaussianity and Minkowski functionals: forecasts for Planck
Bibliographic record
Abstract
We study Minkowski functionals as probes of primordial non-Gaussianity in the cosmic microwave background, specifically for the estimate of the primordial ‘local’ bi-spectrum parameter |$f_{_{\rm NL}}$|, with instrumental parameters which should be appropriate for the Planck experiment. We use a maximum likelihood approach, which we couple with various filtering methods and test thoroughly for convergence. We included the effect of inhomogeneous noise as well as astrophysical biases induced by point sources and by the contamination from the Galaxy. We find that when Wiener filtered maps are used (rather than simply smoothed with Gaussian), the expected error on the measurement of |$f_{_{\rm NL}}$| should be as small as |$\Delta f_{_{\rm NL}} \simeq 10$| when combining the three channels at 100, 143 and 217 GHz in the Planck extended mission setup. This result is fairly insensitive to the non-homogeneous nature of the noise, at least for realistic hitmaps expected from Planck. We then estimate the bias induced on the measurement of |$f_{_{\rm NL}}$| by point sources in those three channels. With the appropriate masking of the bright sources, this bias can be reduced to a negligible level in the 100 and 143 GHz channels. It remains significant in the 217 GHz channel, but can be corrected for. The Galactic foreground biases are quite important and present a complex dependence on sky coverage: making them negligible will depend strongly on the quality of the component separation methods.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".