The star formation rates of active galactic nuclei host galaxies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it