Weak lensing mass map and peak statistics in CFHT/Stripe82 survey
Bibliographic record
Abstract
ABSTRACT We present the weak lensing mass map of the 173 tiles Canada-France-Hawaii TelescopeStripe82 Survey (CS82) with the effective area ∼ 124 deg 2 and study the peak statistics, in-cluding peak abundance, correlation functions and tangential-shear profile of peaks with themass map. We find that (1) peak abundance detected in CS82 are c onsistent with predictionsfrom a ΛCDM cosmological model, once noise effects are properly included; (2) correlationfunction of peaks with different signal-to-noise ratio (SNR) can be well fitted with powerlaws. Combining with the SDSS-III/Constant Mass (CMASS) galaxies, the cross-correlationbetween CMASS galaxies and high SNR peaks can be well-fitted with a power law; (3) thetangential shear profiles of the peaks increase with SNR. We c oncentrate on fitting spheri-cal models to the tangential profiles with both singular isot hermal sphere (SIS) and NavarroFrenk & White (NFW) models. For the high SNR peaks, the SIS model is rejected at ∼ 3σ.Comparing the Dark and matched clumps to the optically selected redMaPPer clusters, a dif-ference in lensing signal of a factor of 2 can be found, reflect ing the fact that likely about halfof the dark clumps are false detection.Key words: large-scale structure of Universe-gravitational lensing: weak
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".