Voids in the SDSS DR9: observations, simulations, and the impact of the survey mask
Bibliographic record
Abstract
We present and study cosmic voids identified using the watershed void finder vide in the Sloan Digital Sky Survey Data Release 9, compare these voids to ones identified in mock catalogues, and assess the impact of the survey mask on void statistics such as number functions, ellipticity distributions, and radial density profiles. The nearly 1000 identified voids span three nearly volume-limited samples from redshift z = 0.43 to 0.7. For comparison, we use 98 of the publicly available second-order Lagrangian perturbation theory-based mock galaxy catalogues of Manera et al., and also generate our own mock catalogues by applying a Halo Occupation Distribution model to an N-body simulation. We find that the mask reduces the number density of voids at all scales by a factor of 3 and slightly skews the relative size distributions. This engenders an increase in the mean ellipticity by roughly 30 per cent. However, we find that radial density profiles are largely robust to the effects of the mask. We see excellent agreement between the data and both mock catalogues, and find no tension between the observed void properties and the properties derived from Λcolddarkmatter simulations. We have added the void catalogues from both data and mock galaxy populations discussed in this work to the Public Cosmic Void Catalog at http://www.cosmicvoids.net.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".