The clustering of galaxies in the SDSS-III DR9 Baryon Oscillation Spectroscopic Survey: constraints on primordial non-Gaussianity
Bibliographic record
Abstract
We analyse the density field of 264 283 galaxies observed by the Sloan Digital Sky Survey (SDSS)-III Baryon Oscillation Spectroscopic Survey (BOSS) and included in the SDSS Data Release 9 (DR9). In total, the SDSS DR9 BOSS data include spectroscopic redshifts for over 400 000 galaxies spread over a footprint of more than 3000 deg2. We measure the power spectrum of these galaxies with redshifts 0.43 < z < 0.7 in order to constrain the amount of local non-Gaussianity, | $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$|, in the primordial density field, paying particular attention to the impact of systematic uncertainties. The BOSS galaxy density field is systematically affected by the local stellar density and this influences the ability to accurately measure | $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$|. In the absence of any correction, we find (erroneously) that the probability that | $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$| is greater than zero, P(| $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$| > 0), is 99.5 per cent. After quantifying and correcting for the systematic bias and including the added uncertainty, we find − 45 < | $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$| < 195 at 95 per cent confidence and P(| $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$| > 0) = 91.0 per cent. A more conservative approach assumes that we have only learnt the k dependence of the systematic bias and allows any amplitude for the systematic correction; we find that the systematic effect is not fully degenerate with that of | $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$|, and we determine that −82 < | $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$| < 178 (at 95 per cent confidence) and P(| $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$| > 0) = 68 per cent. This analysis demonstrates the importance of accounting for the impact of Galactic foregrounds on | $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$| measurements. We outline the methods that account for these systematic biases and uncertainties. We expect our methods to yield robust constraints on | $f{{\rm _N}{\rm _L}\!\!\!\!\!\!\!\!\!\!\!\!\!^{\rm local}}$| for both our own and future large-scale structure investigations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".