Sub-Eddington Supermassive Black Hole Activity in Fornax Early-type Galaxies
Bibliographic record
Abstract
Abstract We characterize the incidence and intensity of low-level supermassive black hole activity within the Fornax cluster, through X-ray observations of the nuclei of 29 quiescent early-type galaxies. Using the Chandra X-ray Telescope, we target 17 galaxies from the Hubble Space Telescope Fornax Cluster Survey, down to a uniform (3σ) limiting X-ray luminosity threshold of 5 · 1038 erg s−1, which we combine with deeper, archival observations for an additional 12 galaxies. A nuclear X-ray point source is detected in 11 out of 29 targets. After accounting for the low-mass X-ray binary contamination to the nuclear X-ray signal, the X-ray active fraction is measured at 26.6% ± 9.6%. The results from this analysis are compared to similar investigations targeting quiescent early types in the Virgo cluster, as well as the field. After correcting for the different mass distributions, the measured Fornax active fraction is less than the field fraction, at more than 3σ, confirming that the funneling of gas to the nuclear regions of cluster members is inhibited compared to those galaxies in the field. At the same time, we find no statistically significant difference between Fornax and Virgo galaxies, with only marginal evidence for a lower active fraction in Fornax (1σ); if real, owing to Fornax's higher galaxy number density, this could indicate that galaxy–galaxy interactions are more effective at gas removal than galaxy-gas effects.
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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) | 0.001 | 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".