The star formation rates of active galactic nuclei host galaxies
Bibliographic record
Abstract
Abstract Using artificial neural network predictions of total infrared luminosities (LIR), we compare the host galaxy star formation rates (SFRs) of ∼21 000 optically selected active galactic nuclei (AGN), 466 low-excitation radio galaxies (LERGs) and 721 mid-IR-selected AGN. SFR offsets (ΔSFR) relative to a sample of star-forming ‘main-sequence’ galaxies (matched in M⋆, z and local environment) are computed for the AGN hosts. Optically selected AGN exhibit a wide range of ΔSFR, with a distribution skewed to low SFRs and a median ΔSFR = −0.06 dex. The LERGs have SFRs that are shifted to even lower values with a median ΔSFR = −0.5 dex. In contrast, mid-IR-selected AGN have, on average, SFRs enhanced by a factor of ∼1.5. We interpret the different distributions of ΔSFR amongst the different AGN classes in the context of the relative contribution of triggering by galaxy mergers. Whereas the LERGs are predominantly fuelled through low accretion rate secular processes which are not accompanied by enhancements in SFR, mergers, which can simultaneously boost SFRs, most frequently lead to powerful, obscured AGN.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 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".