Kinemetry of SINS High‐Redshift Star‐Forming Galaxies: Distinguishing Rotating Disks from Major Mergers
Bibliographic record
Abstract
We present a simple set of kinematic criteria that can distinguish between galaxies dominated by ordered rotational motion and those involved in major merger events. Our criteria are based on the dynamics of the warm ionized gas (as traced by Hα) within galaxies, making this analysis accessible to high-redshift systems, whose kinematics are primarily traceable through emission features. Using the method of kinemetry (developed by Krajnović and coworkers), we quantify asymmetries in both the velocity and velocity dispersion maps of the warm gas, and the resulting criteria enable us to empirically differentiate between nonmerging and merging systems at high redshift. We apply these criteria to 11 of our best-studied rest-frame UV/optical-selected z ∼ 2 galaxies for which we have near-infrared integral-field spectroscopic data from SINFONI on the VLT. Of these 11 systems, we find that >50% have kinematics consistent with a single rotating disk interpretation, while the remaining systems are more likely undergoing major mergers. This result, combined with the short formation timescales of these systems, provides evidence that rapid, smooth accretion of gas plays a significant role in galaxy formation at high redshift.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".