Quantifying the thermal Sunyaev–Zel’dovich effect and excess millimetre emission in quasar environments
Bibliographic record
Abstract
ABSTRACT In this paper, we probe the hot, post-shock gas component of quasar-driven winds through the thermal Sunyaev–Zel’dovich (tSZ) effect. Combining data sets from the Atacama Cosmology Telescope, the Herschel Space Observatory, and the Very Large Array, we measure average spectral energy distributions of 109 829 optically selected, radio quiet quasars from 1.4 to 3000 GHz in six redshift bins between 0.3 < z < 3.5. We model the emission components in the radio and far-infrared, plus a spectral distortion from the tSZ effect. At z > 1.91, we measure the tSZ effect at 3.8σ significance with an amplitude corresponding to a total thermal energy of 3.1 × 1060 erg. If this energy is due to virialized gas, then our measurement implies quasar host halo masses are ∼6 × 1012 h−1 M⊙. Alternatively, if the host dark matter halo masses are ∼2 × 1012 h−1 M⊙ as some measurements suggest, then we measure a >90 per cent excess in the thermal energy over that expected due to virialization. If the measured SZ effect is primarily due to hot bubbles from quasar-driven winds, we find that $(5^{+1.2}_{-1.3}$) per cent of the quasar bolometric luminosity couples to the intergalactic medium over a fiducial quasar lifetime of 100 Myr. An additional source of tSZ may be correlated structure, and further work is required to separate the contributions. At z ≤ 1.91, we detect emission at 95 and 148 GHz that is in excess of thermal dust and optically thin synchrotron emission. We investigate potential sources of this excess emission, finding that CO line emission and an additional optically thick synchrotron component are the most viable candidates.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".