Evidence for the Thermal Sunyaev-Zel'dovich Effect Associated with Quasar Feedback
Bibliographic record
Abstract
Using a radio-quiet subsample of the Sloan Digital Sky Survey spectroscopic quasar catalogue, spanning redshifts 0.5–3.5, we derive the mean millimetre and far-infrared quasar spectral energy distributions (SEDs) via a stacking analysis of Atacama Cosmology Telescope and Herschel-Spectral and Photometric Imaging REceiver data. We constrain the form of the far-infrared emission and find 3σ–4σ evidence for the thermal Sunyaev–Zel'dovich (SZ) effect, characteristic of a hot ionized gas component with thermal energy (6.2 ± 1.7) × 1060 erg. This amount of thermal energy is greater than expected assuming only hot gas in virial equilibrium with the dark matter haloes of (1–5) × 1012 h−1 M⊙ that these systems are expected to occupy, though the highest quasar mass estimates found in the literature could explain a large fraction of this energy. Our measurements are consistent with quasars depositing up to |$(14.5 \pm 3.3)\tau _8^{-1}$| per cent of their radiative energy into their circumgalactic environment if their typical period of quasar activity is τ8 × 108 yr. For high quasar host masses, ∼1013 h−1 M⊙, this percentage will be reduced. Furthermore, the uncertainty on this percentage is only statistical and additional systematic uncertainties enter at the 40 per cent level. The SEDs are dust dominated in all bands and we consider various models for dust emission. While sufficiently complex dust models can obviate the SZ effect, the SZ interpretation remains favoured at the 3σ–4σ level for most models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".