Is the star-formation rate in <i>z</i> ∼ 6 quasars overestimated?
Bibliographic record
Abstract
ABSTRACT The large total infrared (TIR) luminosities (LTIR; ≳1012 L⊙) observed in z ∼ 6 quasars are generally converted into high star-formation rates (SFRs; $\gtrsim\!{10}^2~{\rm M}_{\odot }\, {\rm yr}^{-1}$) of their host galaxies. However, these estimates rely on the assumption that dust heating is dominated by stellar radiation, neglecting the contribution from the central active galactic nucleus (AGN). We test the validity of this assumption by combining cosmological hydrodynamic simulations with radiative transfer calculations. We find that, when AGN radiation is included in the simulations, the mass (luminosity)-weighted dust temperature in the host galaxies increases from T ≈ 50 K (T ≈ 70 K) to T ≈ 80 K (T ≈ 200 K), suggesting that AGN effectively heats the bulk of dust in the host galaxy. We compute the AGN-host galaxy SFR from the synthetic spectral energy distribution by using standard SFR – LTIR relations, and compare the results with the ‘true’ values in the simulations. We find that the SFR is overestimated by a factor of ≈3 (≳10) for AGN bolometric luminosities of Lbol ≈ 1012 L⊙ (≳1013 L⊙), implying that the SFRs of z ∼ 6 quasars can be overestimated by over an order of magnitude.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".