On constraining cosmology and the halo mass function with weak gravitational lensing
Bibliographic record
Abstract
ABSTRACT The discrepancy between the weak lensing (WL) and the Planck measurements of S8 has been a subject of several studies. Assuming that residual systematics are not the cause, these studies tend to show that a strong suppression of the amplitude of the mass power spectrum P(k) in the late Universe at high k could resolve it. The WL signal at the small scale is sensitive to various effects not related to lensing, such as baryonic effects and intrinsic alignment. These effects are still poorly understood therefore the accuracy of P(k) depends on the modelling precision of these effects. A common approach for calculating P(k) relies on a halo model. Among the various components necessary for the construction of P(k) in the halo model framework, the halo mass function (HMF) is an important one. Traditionally, the HMF has been assumed to follow a fixed model, motivated by dark matter-only numerical simulations. Recent literature shows that baryonic physics, among several other factors, could affect the HMF. In this study, we investigate the impact of allowing the HMF to vary. This provides a way of testing the validity of the halo model-HMF calibration using data. In the context of the aforementioned S8 discrepancy, we find that the Planck cosmology is not compatible with the vanilla HMF for both the DES-y3 and the KiDS-1000 data. Moreover, when the cosmology and the HMF parameters are allowed to vary, the Planck cosmology is no longer in tension. The modified HMF predicts a matter power spectrum with a $\sim 25~{{\ \rm per\ cent}}$ power loss at k ∼ 1 h Mpc−1, in agreement with the recent studies that try to mitigate the S8 tension with modifications in P(k). We show that stage IV surveys will be able to measure the HMF parameters with a few per cent accuracy.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".