The connection between star formation and supermassive black hole activity in the local Universe
Bibliographic record
Abstract
ABSTRACT We study the nuclear (AGN) activity in the local Universe (z < 0.33) and its correlation with the host galaxy properties, derived from a Sloan Digital Sky Survey sample with spectroscopic star-formation rate (SFR) and stellar mass determination. To quantify the level of AGN activity we used the XMM-Newton Serendipitous Source Catalogue. Applying multiwavelength selection criteria (optical BPT-diagrams, X-ray/optical ratio etc), we found that 24 per cent of the detected sources are efficiently-accreting AGN with moderate-to-high X-ray luminosity, twice as likely to be hosted by star-forming galaxies than by quiescent ones. The distribution of the specific Black Hole accretion rate (λsBHAR) shows that nuclear activity in local, non-AGN dominated galaxies peaks at very low accretion rates (−4 ≲ log λsBHAR ≲ −3) in all stellar mass ranges. We observe systematically larger values of λsBHAR for galaxies with active star formation than for quiescent ones, and an increase of the mean λsBHAR with SFR for both star-forming and quiescent galaxies. These finding confirm the decrease in AGN activity with cosmic time and are consistent with a scenario where both star-formation and AGN activity are fuelled by a common gas reservoir.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".