Synthetic CO, H2 and H i surveys of the second galactic quadrant, and the properties of molecular gas
Bibliographic record
Abstract
We present CO, H2, H i and HISA (H i self-absorption) distributions from a set of simulations of grand design spirals including stellar feedback, self-gravity, heating and cooling. We replicate the emission of the second galactic quadrant by placing the observer inside the modelled galaxies and post-process the simulations using a radiative transfer code, so as to create synthetic observations. We compare the synthetic data cubes to observations of the second quadrant of the Milky Way to test the ability of the current models to reproduce the basic chemistry of the Galactic interstellar medium (ISM), as well as to test how sensitive such galaxy models are to different recipes of chemistry and/or feedback. We find that models which include feedback and self-gravity can reproduce the production of CO with respect to H2 as observed in our Galaxy, as well as the distribution of the material perpendicular to the Galactic plane. While changes in the chemistry/feedback recipes do not have a huge impact on the statistical properties of the chemistry in the simulated galaxies, we find that the inclusion of both feedback and self-gravity are crucial ingredients, as our test without feedback failed to reproduce all of the observables. Finally, even though the transition from H2 to CO seems to be robust, we find that all models seem to underproduce molecular gas, and have a lower molecular to atomic gas fraction than is observed. Nevertheless, our fiducial model with feedback and self-gravity has shown to be robust in reproducing the statistical properties of the basic molecular gas components of the ISM in our Galaxy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".