The Close AGN Reference Survey (CARS): a comparison between sub-mm and optical AGN diagnostic diagrams
Bibliographic record
Abstract
ABSTRACT The $L_{\rm IR}{\!-\!}L_{\rm HCN}$ relation suggests that there is a tight connection between dense gas and star formation. We use data from the Close AGN Reference Survey (CARS) to investigate the dense gas – star formation relation in active galactic nuclei (AGN) hosting galaxies, and the use of dense gas as an AGN diagnostic. Our sample contains five Type-1 (unobscured) AGN that were observed with the Atacama Large Millimetre/submillimetre Array with the aim to detect HCN(4-3), HCO$^+$(4-3), and CS(7-6). We detect the dense gas emission required for this analysis in three of the five targets. We find that despite the potential impact from the AGN on the line fluxes of these sources, they still follow the $L_{\rm IR}{\!-\!}L_{\rm HCN}$ relation. We then go on to test claims that the HCN/HCO+ and HCN/CS line ratios can be used as a tool to classify AGN in the sub-mm HCN diagram. We produce the classic ionized emission-line ratio diagnostics (the so-called BPT diagrams), using available CARS data from the Multi Unit Spectroscopic Explorer. We then compare the BPT classification with the sub-mm classification made using the dense gas tracers. Where it was possible to complete the analysis we find general agreement between optical and sub-mm classified gas excitation mechanisms. This suggests that AGN can contribute to the excitation of both the low-density gas in the warm ionized medium and the high-density gas in molecular clouds simultaneously, perhaps through X-ray, cosmic ray, or shock heating mechanisms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".