Refined modelling of the radio SZ signal: kinematic terms, relativistic temperature corrections, and anisotropies in the radio background
Bibliographic record
Abstract
ABSTRACT A significant cosmological radio background will inevitably lead to a radio Sunyaev–Zeldovich (SZ) effect. In the simplest limit, the combined signal from the scattered radio and cosmic microwave background exhibits a null at around ν ≃ 735 MHz. Here, we show that kinematic and relativistic temperature corrections to this radio SZ signal are easily calculable. We treat both the cluster and observer motion, and the scattering of anisotropies in the radio background, highlighting how the spectrum of the radio SZ effect is affected in each case. Although relativistic temperature corrections only enter at the level of a few per cent, our expressions allow high-precision modelling of these terms. By measuring the SZ signal around the radio null, one is in principle able to place constraints on the properties of a cosmological radio background. A combination with standard SZ measurements from large cluster samples could provide a promising avenue towards breaking degeneracies between different contributions. Stacking analyses can reduce the effect of kinematic corrections and dipolar anisotropies in the radio background, thereby providing a way to constrain the redshift dependence of the average radio background. Our qualitative discussion is meant to give an analytic understanding of the various effects and also motivate further studies with the aim to obtain quantitative forecasts of their observability. At this stage, a detection of the corrections seems rather futuristic, but the advent of large SZ and X-ray cluster samples could drastically improve our ability to disentangle various effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".