The XXL survey
Bibliographic record
Abstract
We model the evolution of active galactic nuclei (AGN) by constructing their radio LFs. We used a set of surveys of varying area and depth, namely, the deep COSMOS survey of 1916 AGN sources; the wide, shallow 3CRR, 7C, and 6CE surveys, together containing 356 AGN; and the intermediate XXL-North and South fields consisting of 899 and 1484 sources, respectively. We also used the CENSORS, BRL, Wall & Peacock, and Config surveys, respectively consisting of 150, 178, 233, and 230 sources. Together, these surveys account for 5446 AGN sources and constrained the LFs at high redshift and over a wide range of luminosities (up to z ≈ 3 and log( L /W Hz −1 )∈[22, 29]). We concentrated on parametric methods within the Bayesian framework, which allowed us to perform model selection between a set of different models. By comparing the marginalised likelihoods and both the Akaike information criterion and the Bayesian information criterion, we show that the luminosity-dependent density evolution (LDDE) model fits the data best, with evidence ratios varying from “strong” (> 10) to “decisive” (> 100), according to the Jeffreys’ interpretation. The best-fitting model gives insight into the physical picture of AGN evolution, where AGN evolve differently as a function of their radio luminosity. We determined the number density, luminosity density, and kinetic luminosity density as a function of redshift, and we observed a flattening of these functions at higher redshifts, which is not present in simpler models. We explain these trends by our use of the LDDE model. Finally, we divided our sample into subsets according to the stellar mass of the host galaxies in order to investigate a possible bimodality in evolution. We found a difference in LF shape and evolution between these subsets. All together, these findings point to a physical picture where the evolution and density of AGN cannot be explained well by simple models but require more complex models either via AGN sub-populations, where the total AGN sample is divided into sub-samples according to various properties, such as optical properties and stellar mass, or via luminosity-dependent functions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.041 |
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".