Modelling molecular clouds and CO excitation in AGN-host galaxies
Bibliographic record
Abstract
ABSTRACT We present a new physically motivated model for estimating the molecular line emission in active galaxies. The model takes into account (i) the internal density structure of giant molecular clouds (GMCs), (ii) the heating associated with both stars and the active galactic nuclei (AGNs), respectively, producing photodissociation regions (PDRs) and X-ray-dominated regions (XDRs) within the GMCs, and (iii) the mass distribution of GMCs within the galaxy volume. The model needs, as input parameters, the radial profiles of molecular mass, far-UV flux and X-ray flux for a given galaxy, and it has two free parameters: the CO-to-H2 conversion factor αCO, and the X-ray attenuation column density NH. We test this model on a sample of 24 local (z ≤ 0.06) AGN-host galaxies, simulating their carbon monoxide spectral-line energy distribution (CO SLED). We compare the results with the available observations and calculate, for each galaxy, the best (αCO, NH) with a Markov chain Monte Carlo algorithm, finding values consistent with those present in the literature. We find a median αCO = 4.8 M⊙ (K km s−1 pc2)−1 for our sample. In all the modelled galaxies, we find the XDR component of the CO SLED to dominate the CO luminosity from Jupp ≥ 4. We conclude that, once a detailed distribution of molecular gas density is taken into account, PDR emission at mid-/high-J becomes negligible with respect to XDR.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".