A hydrodynamical halo model for weak-lensing cross correlations
Bibliographic record
Abstract
On the scale of galactic haloes, the distribution of matter in the cosmos is affected by energetic, non-gravitational processes, the so-called baryonic feedback. A lack of knowledge about the details of how feedback processes redistribute matter is a source of uncertainty for weak-lensing surveys, which accurately probe the clustering of matter in the Universe over a wide range of scales. We developed a cosmology-dependent model for the matter distribution that simultaneously accounts for the clustering of dark matter, gas, and stars. We informed our model by comparing it to power spectra measured from the BAHAMAS suite of hydrodynamical simulations. In addition to considering matter power spectra, we also considered spectra involving the electron-pressure field, which directly relates to the thermal Sunyaev-Zel’dovich (tSZ) effect. We fitted parameters in our model so that it can simultaneously model both matter and pressure data and such that the distribution of gas as inferred from tSZ has an influence on the matter spectrum predicted by our model. We present two variants, one that matches the feedback-induced suppression seen in the matter–matter power spectrum at the percent level and a second that matches the matter–matter data to a slightly lesser degree (≃2%). However, the latter is able to simultaneously model the matter–electron pressure spectrum at the ≃15% level. We envisage our models being used to simultaneously learn about cosmological parameters and the strength of baryonic feedback using a combination of tSZ and lensing auto- and cross-correlation data.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".