The OTELO survey
Bibliographic record
Abstract
Aims. We take advantage of the capabilities of the OSIRIS Tunable Emission Line Object (OTELO) survey to select and study the AGN population in the field. In particular, we aim to perform an analysis of the properties of these objects, including their demography, morphology, and IR luminosity. Focusing on the population of Hαemitters atz ∼ 0.4, we also aim to study the environments of AGN and non-AGN galaxies at that redshift. methods. We make use of the multiwavelength catalogue of objects in the field compiled by the OTELO survey, unique in terms of minimum flux and equivalent width. We also take advantage of the pseudo-spectra built for each source, which allow the identification of emission lines and the discrimination of different types of objects. Results. We obtained a sample of 72 AGNs in the field of OTELO, selected with four different methods in the optical, X-rays, and mid-infrared bands. We find that using X-rays is the most efficient way to select AGNs. An analysis was performed on the AGN population of OTELO in order to characterise its members. Atz ∼ 0.4, we find that up to 26% of our Hαemitters are AGNs. At that redshift, AGNs are found in identical environments to non-AGNs, although they represent the most clustered group when compared to passive and star-forming galaxies. The majority of our AGNs at any redshift were classified as late-type galaxies, including a 16% proportion of irregulars. Another 16% of AGNs show signs of interactions or mergers. Regarding the infrared luminosity, we are able to recover all the luminous infrared galaxies (LIRGs) in the field of OTELO up toz ∼ 1.6. We find that the proportion of LIRGs and ultra-luminous infraed galaxies (ULIRGs) is higher among the AGN population, and that ULIRGs show a higher fraction of AGNs than LIRGs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.033 | 0.007 |
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".