Co-evolution of black hole growth and star formation from a cross-correlation analysis between quasars and the cosmic infrared background
Bibliographic record
Abstract
We present the first cross-correlation measurement between Sloan Digital Sky Survey type 1 quasars and the cosmic infrared background (CIB) measured by Herschel. The quasars cover the redshift range 0.15 < z < 3.5 where most of the CIB originates. We detect the sub-millimetre emission of the quasars, which dominates on small scales, and correlated emission from dusty star-forming galaxies (DSFGs) dominant on larger scales. The mean flux of the Data Release 7 (DR7) quasars (median redshift 〈z〉 = 1.4) is 11.1, 7.1 and 3.6 mJy at 250, 350 and 500 μm, respectively, while the mean flux of the DR9 quasars (〈z〉 = 2.5) is 5.7, 5.0 and 1.8 mJy at 250, 350 and 500 μm, respectively. Assuming a modified blackbody spectral energy distribution with a power law in the mid-infrared, we infer that the mean infrared luminosity of the DR7 and DR9 quasars is 1012.4 and 1012.8 L⊙, respectively. The correlated emission arises from DSFGs in the same halo as the quasar (the one-halo term) and DSFGs in separate haloes correlated with the quasar-hosting halo (the two-halo term). Using a simple halo model, we find that most quasars are hosted by central galaxies. The host halo mass scale of the DR7 central and satellite quasars is 1012.4 ± 0.9 and 1013.6 ± 0.4 M⊙, respectively. The host halo mass scale of the DR9 central and satellite quasars is 1012.3 ± 0.6 and 1012.8 ± 0.4 M⊙, respectively. Thus, the halo environment of the central quasars is similar to that of the most actively star-forming galaxies, which supports the view that dusty starburst and quasar activity are evolutionarily linked.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".