Cross-correlation of cosmic voids with thermal Sunyaev–Zel’dovich data
Bibliographic record
Abstract
ABSTRACT We provide a measurement of the deficit in the Sunyaev–Zel’dovich Compton-y signal towards cosmic voids, by stacking a catalogue of 97 090 voids constructed with BOSS-DR12 data, on the y-maps built on data from the Atacama Cosmology Telescope (ACT) DR4 and the Planck satellite. We detect the void signal with a significance of $7.3 \sigma$ with ACT and $9.7\sigma$ with Planck, obtaining agreements in the associated void radial y-profiles extracted from both maps. The inner-void profile (for angular separations within the void angular radius) is reconstructed with significances of 4.7σ and 6.1σ with ACT and Planck, respectively; we model such profile using a simple model that assumes uniform gas (under)density and temperature, which enables us to place constraints on the product (−δvTe) of the void density contrast (negative) and the electron temperature. The best-fitting values from the two data sets are $(-\delta _{\rm v}T_{\rm e})=(6.5\pm 2.3)\times 10^{5}\, \text{K}$ for ACT and $(8.6 \pm 2.1)\times 10^{5}\, \text{K}$ for Planck [68 per cent confidence level (CL)], which are in good agreement under uncertainty. The data allow us to place lower limits on the expected void electron temperature at $2.7\times 10^5\, \text{K}$ with ACT and $5.1\times 10^5\, \text{K}$ with Planck (95 per cent CL); these results can transform into upper limits for the ratio between the void electron density and the cosmic mean as $n^{\rm v}_{\rm e}/\bar{n}_{\rm e}\leqslant 0.73$ and 0.49 (95 per cent CL), respectively. Our findings prove the feasibility of using thermal Sunyaev–Zel’dovich observations to constrain the gas properties inside cosmic voids, and confirm that voids are under-pressured regions compared to their surroundings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".