GAMA/WiggleZ: the 1.4 GHz radio luminosity functions of high- and low-excitation radio galaxies and their redshift evolution to<i>z</i>= 0.75
Bibliographic record
Abstract
We present radio active galactic nuclei (AGN) luminosity functions over the redshift range 0.005 < z < 0.75. The sample from which the luminosity functions are constructed is an optical spectroscopic survey of radio galaxies, identified from matched Faint Images of the Radio Sky at Twenty-cm survey (FIRST) sources and Sloan Digital Sky Survey images. The radio AGN are separated into low-excitation radio galaxies (LERGs) and high-excitation radio galaxies (HERGs) using the optical spectra. We derive radio luminosity functions for LERGs and HERGs separately in the three redshift bins (0.005 < z < 0.3, 0.3 < z < 0.5 and 0.5 < z < 0.75). The radio luminosity functions can be well described by a double power law. Assuming this double power-law shape the LERG population displays little or no evolution over this redshift range evolving as |${\sim } (1+z)^{0.06^{+0.17}_{-0.18}}$| assuming pure density evolution or |${\sim } (1+z)^{0.46^{+0.22}_{-0.24}}$| assuming pure luminosity evolution. In contrast, the HERG population evolves more rapidly, best fitted by |${\sim } (1+z)^{2.93^{+0.46}_{-0.47}}$| assuming a double power-law shape and pure density evolution. If a pure luminosity model is assumed, the best-fitting HERG evolution is parametrized by |${\sim } (1+z)^{7.41^{+0.79}_{-1.33}}$|. The characteristic break in the radio luminosity function occurs at a significantly higher power (≳1 dex) for the HERG population in comparison to the LERGs. This is consistent with the two populations representing fundamentally different accretion modes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".