Disentangling the AGN and star formation connection using
Bibliographic record
Abstract
There is growing evidence supporting the coeval growth of galaxies and their resident super-massive black hole (SMBH). Most studies also claim a correlation between the activity of the SMBH and the star formation of the host galaxy. It is unclear, however, whether this correlation extends to all redshifts and X-ray luminosities. Some studies find a weaker dependence at lower luminosities and/or a suppression of the star formation at high luminosities. We here use data from the X-ATLAS and XMM-XXL North fields and compile the largest X-ray sample up to date to investigate how X-ray selected AGN affect the star formation of their host galaxies in a wide redshift and luminosity baseline of 0.03 < z < 3 and log LX(2−10 keV) = (41−45.5) erg s−1. Our sample consists of 3336 AGN. 1872 of our sources have spectroscopic redshifts. For the remaining sources we calculate photometric redshifts using TPZ, a machine-learning algorithm. We estimate stellar masses (M⋆) and star formation rates (SFRs) by applying spectral energy distribution fitting through the CIGALE code, using optical, near-IR, and mid-IR photometry (SDSS, VISTA, and WISE). Of our sources, 608 also have far-IR photometry (Herschel). We use these sources to calibrate the SFR calculations of our remaining X-ray sample. Our results show a correlation between the X-ray luminosity (LX) and the SFR of the host galaxy at all redshifts and luminosities spanned by our sample. We also find a dependence of the specific SFR (sSFR) on redshift, while there are indications that the X-ray luminosity enhances the sSFR even at low redshifts. We then disentangle the effects of stellar mass and redshift on the SFR and again study its dependence on the X-ray luminosity. Towards this end, we estimate the SFR of main-sequence galaxies that have the same stellar mass and redshift as our X-ray AGN and compare them with the SFR of our X-ray AGN. Our analysis reveals that the AGN enhances the star formation of its host galaxy when the galaxy lies below the main sequence and quenches the star formation of the galaxy it lives in when the host lies above the main sequence. Therefore, the effect of AGN on the SFR of the host galaxy depends on the location of the galaxy relative to the main sequence.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".