Probing hot gas around luminous red galaxies through the Sunyaev–Zel’dovich effect
Bibliographic record
Abstract
ABSTRACT We construct the mean thermal Sunyaev–Zel’dovich (tSZ) Comptonization y-profile around luminous red galaxies (LRGs) in the redshift range 0.16 < z < 0.47 from the Sloan Digital Sky Survey Data Release 7 using the Planck y-map. We detect a significant tSZ signal out to ∼30 arcmin, which is well beyond the 10 arcmin angular resolution of the y-map and well beyond the virial radii of the LRGs. We compare the measured profile with predictions from the cosmo-OWLS suite of cosmological hydrodynamical simulations. The best agreement is obtained for models that include efficient feedback from active galactic nuclei, over and above feedback associated with star formation. We also compare our results with predictions based on the halo model with a universal pressure profile giving the y-signal. The predicted profile is consistent with the data when using stacked weak lensing measurements to estimate the halo masses of the LRGs, but only if we account for the clustering of neighbouring haloes via a two-halo term.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".