Herschel-ATLAS: far-infrared properties of radio-selected galaxies★
Bibliographic record
Abstract
We use the Herschel-Astrophysical Terahertz Large Area Survey (ATLAS) science demonstration data to investigate the star formation properties of radio-selected galaxies in the GAMA-9h field as a function of radio luminosity and redshift. Radio selection at the lowest radio luminosities, as expected, selects mostly starburst galaxies. At higher radio luminosities, where the population is dominated by active galactic nuclei (AGN), we find that some individual objects are associated with high far-infrared luminosities. However, the far-infrared properties of the radio-loud population are statistically indistinguishable from those of a comparison population of radio-quiet galaxies matched in redshift and K-band absolute magnitude. There is thus no evidence that the host galaxies of these largely low-luminosity (Fanaroff–Riley class I), and presumably low-excitation, AGN, as a population, have particularly unusual star formation histories. Models in which the AGN activity in higher luminosity, high-excitation radio galaxies is triggered by major mergers would predict a luminosity-dependent effect that is not seen in our data (which only span a limited range in radio luminosity) but which may well be detectable with the full Herschel-ATLAS data set.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.012 |
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".