SIMULATIONS OF THE PAIRWISE KINEMATIC SUNYAEV–ZEL’DOVICH SIGNAL
Bibliographic record
Abstract
ABSTRACT The pairwise kinematic Sunyaev–Zel’dovich (kSZ) signal from galaxy clusters is a probe of their line of sight momenta, and thus a potentially valuable source of cosmological information. In addition to the momenta, the amplitude of the measured signal depends on the properties of the intracluster gas and observational limitations such as errors in determining cluster centers and redshifts. In this work, we simulate the pairwise kSZ signal of clusters at , using the output from a cosmological N-body simulation and including the properties of the intracluster gas via a model that can be varied in post-processing. We find that modifications to the gas profile due to star formation and feedback reduce the pairwise kSZ amplitude of clusters by , relative to the naive “gas traces mass” assumption. We demonstrate that miscentering can reduce the overall amplitude of the pairwise kSZ signal by up to 10%, while redshift errors can lead to an almost complete suppression of the signal at small separations. We confirm that a high-significance detection is expected from the combination of data from current generation, high-resolution cosmic microwave background experiments, such as the South Pole Telescope, and cluster samples from optical photometric surveys, such as the Dark Energy Survey. Furthermore, we forecast that future experiments such as Advanced ACTPol in conjunction with data from the Dark Energy Spectroscopic Instrument will yield detection significances of at least , and up to in an optimistic scenario. Our simulated maps are publicly available at http://www.hep.anl.gov/cosmology/ksz.html .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".