Environmental effects on halo abundance and weak lensing peak statistics towards large underdense regions
Bibliographic record
Abstract
ABSTRACT The cosmic microwave background (CMB) contains an anomalous cold spot with a surrounding hot ring, known as the Cold Spot. Inoue & Silk proposed that this feature could be explained by postulating a supervoid: if such a large underdense region exists, then the growth of matter perturbing around the spot might differ from the average value in the Universe and the differences might affect weak lensing analysis of peak statistics. To investigate environmental effects on halo number count and peak statistics, we used a publicly available ray-tracing simulation for a box size of 2250 h−1 Mpc on a side. We found that the number count for massive haloes towards the largest underdense region in the simulation decreases and the corresponding significance of the difference, based on a cosmic average, is ≥3σ. On the basis of the results of peak statistics analysis, the number of high peaks decreases with the decrement of massive haloes, but the number of low peaks increases with the lack of matter in the line of sight. The highest significance of the decrement in peak counts in large underdense regions is 5σ in the total signal-to-noise ratio. Our result implies that environmental effects on halo abundance and weak lensing peak statistic can be used to probe the presence and properties of supervoids.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".