The properties of radio and mid-infrared detected galaxies and the effect of environment on the co-evolution of AGN and star formation at <i>z</i> ∼ 1
Bibliographic record
Abstract
ABSTRACT In this study, we investigate 179 radio-infrared (IR) galaxies drawn from a sample of spectroscopically confirmed galaxies, which are detected in radio and mid-IR (MIR) in the redshift range of 0.55 ≤ z ≤ 1.30 in the Observations of Redshift Evolution in Large Scale Environments (ORELSE) survey. We constrain the active galactic nuclei (AGN) contribution to the total IR luminosity (fAGN), and estimate the AGN luminosity (LAGN) and the star formation rate (SFR). Based on the fAGN and radio luminosity, radio–IR galaxies are split into galaxies that host either high- or low-fAGN AGN (high-/low-fAGN), and star-forming galaxies (SFGs) with little to no AGN activity. We study the properties of the three radio–IR sub-samples comparing to an underlying parent sample. In the comparison of radio luminosity of three sub-samples, no significant difference was found, which could be due to the combined contribution of radio emission from AGN and star formation. We find a positive relationship between LAGN and specific SFR (sSFR) for both AGN sub-samples, strongly suggesting a co-evolution scenario of AGN and SF in these galaxies. A toy model is designed to demonstrate this co-evolution scenario, where we find that, in almost all cases, a rapid quenching time-scale is required, which we argue is a signature of AGN quenching. The environmental preference for intermediate/infall regions of clusters/groups remains across the co-evolution scenario, which suggests that galaxies might be in an orbital motion around the cluster/group during the scenario.
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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 | 0.001 | 0.000 |
| 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) | 0.001 | 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 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".