A POPULATION OF INTERMEDIATE-MASS BLACK HOLES IN DWARF STARBURST GALAXIES UP TO REDSHIFT = 1.5
Bibliographic record
Abstract
ABSTRACT We study a sample of ∼50,000 dwarf starburst and late-type galaxies drawn from the COSMOS survey with the aim of investigating the presence of nuclear accreting black holes (BHs) as those seed BHs from which supermassive BHs could grow in the early universe. We divide the sample into five complete redshift bins up to z = 1.5 and perform an X-ray stacking analysis using the Chandra COSMOS-Legacy survey data. After removing the contribution from X-ray binaries and hot gas to the stacked X-ray emission, we still find an X-ray excess in the five redshift bins that can be explained by nuclear accreting BHs. This X-ray excess is more significant for . At higher redshifts, these active galactic nuclei could suffer mild obscuration, as indicated by the analysis of their hardness ratios. The average nuclear X-ray luminosities in the soft band are in the range 10 39 –10 40 erg s −1 . Assuming that the sources accrete at ≥1% the Eddington rate, their BH masses would be ≤10 5 , thus in the intermediate-mass BH regime, but their mass would be smaller than the one predicted by the BH-stellar mass relation. If instead the sources follow the correlation between BH mass and stellar mass, they would have sub-Eddington accreting rates of ∼10 −3 and BH masses 1–9 × 10 5 . We thus conclude that a population of intermediate-mass BHs exists in dwarf starburst galaxies, at least up to z = 1.5, though their detection beyond the local universe is challenging due to their low luminosity and mild obscuration unless deep surveys are employed.
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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 | 0.001 | 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".