A selection effect boosting the contribution from rapidly spinning black holes to the cosmic X-ray background
Bibliographic record
Abstract
The cosmic X-ray background (CXB) is the total emission from past accretion activity on to supermassive black holes in active galactic nuclei (AGN) and peaks in the hard X-ray band (30 keV). In this paper, we identify a significant selection effect operating on the CXB and flux-limited AGN surveys, and outline how they must depend heavily on the spin distribution of black holes. We show that, due to the higher radiative efficiency of rapidly spinning black holes, they will be over-represented in the X-ray background, and therefore could be a dominant contributor to the CXB. Using a simple bimodal spin distribution, we demonstrate that only 15 per cent maximally spinning AGN can produce 50 per cent of the CXB. We also illustrate that invoking a small population of maximally spinning black holes in CXB synthesis models can reproduce the CXB peak without requiring large numbers of Compton-thick AGN. The spin bias is even more pronounced for flux-limited surveys: 7 per cent of sources with maximally spinning black holes can produce half of the source counts. The detectability for maximum spin black holes can be further boosted in hard (>10 keV) X-rays by up to ∼60 per cent due to pronounced ionized reflection, reducing the percentage of maximally spinning black holes required to produce half of the CXB or survey number counts further. A host of observations are consistent with an over-representation of high-spin black holes. Future NuSTAR and ASTRO-H hard X-ray surveys will provide the best constraints on the role of spin within the AGN population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".