KMOS LENsing Survey (KLENS): Morpho-kinematic analysis of star-forming galaxies at z ~ 2
Bibliographic record
Abstract
We present results from the KMOS LENsing Survey (KLENS), which is exploiting gravitational lensing to study the kinematics of 24 star-forming galaxies at 1.4 < z < 3.5 with a median mass of log(M⋆∕M⊙) = 9.6 and a median star formation rate (SFR) of 7.5 M⊙ yr−1. We find that 25% of these low mass/low SFR galaxies are rotation-dominated, while the majority of our sample shows no velocity gradient. When combining our data with other surveys, we find that the fraction of rotation-dominated galaxies increases with the stellar mass, and decreases for galaxies with a positive offset from the main sequence (higher specific star formation rate). We also investigate the evolution of the intrinsic velocity dispersion, σ0, as a function of the redshift, z, and stellar mass, M⋆, assuming galaxies in quasi-equilibrium (Toomre Q parameter equal to 1). From the z − σ0 relation, we find that the redshift evolution of the velocity dispersion is mostly expected for massive galaxies (log(M⋆∕M⊙) > 10). We derive a M⋆ − σ0 relation, using the Tully–Fisher relation, which highlights that a different evolution of the velocity dispersion is expected depending on the stellar mass, with lower velocity dispersions for lower masses, and an increase for higher masses, stronger at higher redshift. The observed velocity dispersions from this work and from comparison samples spanning 0 < z < 3.5 appear to follow this relation, except at higher redshift (z > 2), where we observe higher velocity dispersions for low masses (log(M⋆∕M⊙) ~ 9.6) and lower velocity dispersions for high masses (log(M⋆∕M⊙) ~ 10.9) than expected. This discrepancy could, for instance, suggest that galaxies at high redshift do not satisfy the stability criterion, or that the adopted parametrization of the specific star formation rate and molecular properties fail 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".