Measurement of the mean central optical depth of galaxy clusters via the pairwise kinematic Sunyaev-Zel’dovich effect with SPT-3G and DES
Bibliographic record
Abstract
We infer the mean optical depth of a sample of optically selected galaxy clusters from the Dark Energy Survey via the pairwise kinematic Sunyaev-Zel'dovich (KSZ) effect. The pairwise KSZ signal between pairs of clusters drawn from the Dark Energy Survey Year-3 cluster catalog is detected at $4.1\ensuremath{\sigma}$ in cosmic microwave background temperature maps from two years of observations with the SPT-3G camera on the South Pole Telescope. After cuts, there are 24,580 clusters in the $\ensuremath{\sim}1,400\text{ }\text{ }{\mathrm{deg}}^{2}$ of the southern sky observed by both experiments. We infer the mean optical depth of the cluster sample with two techniques. The optical depth inferred from the pairwise KSZ signal is ${\overline{\ensuremath{\tau}}}_{e}=(2.97\ifmmode\pm\else\textpm\fi{}0.73)\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$, while that inferred from the thermal SZ signal is ${\overline{\ensuremath{\tau}}}_{e}=(2.51\ifmmode\pm\else\textpm\fi{}0.5{5}^{\mathrm{stat}}\ifmmode\pm\else\textpm\fi{}0.1{5}^{\mathrm{syst}})\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$. The two measures agree at $0.6\ensuremath{\sigma}$. We perform a suite of systematic checks to test the robustness of the analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".