Effects of galactic disc inclination and resolution on observed GMC properties and Larson's scaling relations
Bibliographic record
Abstract
With ALMA (Atacama Large Millimeter/submillimeter Array) making it possible to resolve giant molecular clouds (GMCs) in other galaxies, it is becoming necessary to quantify the observational bias on measured GMC properties. Using a hydrodynamical simulation of a barred spiral galaxy, we compared the physical properties of GMCs formed in position–position–position (PPP) space to the observational position–position–velocity (PPV) space. We assessed the effect of disc inclination: face-on (PPVface) and edge-on (PPVedge), and resolution: 1.5 pc versus 24 pc, on GMC properties and the further implications of using Larson's scaling relations for mass–radius and velocity dispersion–radius. The low-resolution PPV data are generated by simulating ALMA Cycle 3 observations using the casa package. Results show that the median properties do not differ strongly between PPP and PPVface under both resolutions, but PPVedge clouds deviate from these two. The differences become magnified when switching to the lower, but more realistic resolution. The discrepancy can lead to opposite results for the virial parameter's measure of gravitational binding, and therefore the dynamical state of the clouds. The power-law indices for the two Larson's scaling relations decrease going from PPP, PPVedge to PPVface and decrease from high to low resolutions. We conclude that the relations are not entirely driven by the underlying physical origin and therefore have to be used with caution when considering the environmental dependence, dynamical state, and the extragalactic CO-to-H2 conversion factor of GMCs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".