The kinematics of massive high-redshift dusty star-forming galaxies
Bibliographic record
Abstract
ABSTRACT We present a new method for modelling the kinematics of galaxies from interferometric observations by performing the optimization of the kinematic model parameters directly in visibility space instead of the conventional approach of fitting velocity fields produced with the clean algorithm in real-space. We demonstrate our method on Atacama Large Millimeter/submillimeter Array (ALMA) observations of $^{12}$CO (2–1), (3–2), or (4–3) emission lines from an initial sample of 30 massive 850 $\mu$m-selected dusty star-forming galaxies with far-infrared luminosities $\gtrsim$$\, 10^{12}$ L$_{\odot }$ in the redshift range $z \sim$ 1.2–4.7. Using the results from our modelling analysis for the 12 of the 20 sources with the highest signal-to-noise emission lines that show disc-like kinematics, we conclude the following: (i) our sample prefers a CO-to-$H_2$ conversion factor, of $\alpha _{\rm CO} = 0.74 \pm 0.37$; (ii) these far-infrared luminous galaxies follow a similar Tully–Fisher relation between the circular velocity, $V_{\rm circ}$, and baryonic mass, $M_{\rm b}$, as less strongly star-forming samples at high redshift, but extend this relation to much higher masses – showing that these are some of the most massive disc-like galaxies in the Universe; (iii) finally, we demonstrate support for an evolutionary link between massive high-redshift dusty star-forming galaxies and the formation of local early-type galaxies using the both the distributions of the baryonic and kinematic masses of these two populations on the $M_{\rm b}$ – $\sigma$ plane and their relative space densities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".