LEGA-C: Analysis of Dynamical Masses from Ionized Gas and Stellar Kinematics at z ∼ 0.8
Bibliographic record
Abstract
Abstract We compare dynamical mass estimates based on spatially extended stellar and ionized gas kinematics (M dyn,* and M dyn,eml, respectively) of 157 star-forming galaxies at 0.6 ≤ z < 1. Compared with z ∼ 0, these galaxies have enhanced star formation rates, with stellar feedback likely affecting the dynamics of the gas. We use LEGA-C DR3, the highest-redshift data set that provides sufficiently deep measurements of a K s -band limited sample. For M dyn,*, we use Jeans anisotropic multi-Gaussian expansion models. For M dyn,eml, we first fit a custom model of a rotating exponential disk with uniform dispersion, whose light is projected through a slit and corrected for beam smearing. We then apply an asymmetric drift correction based on assumptions common in the literature to the fitted kinematic components to obtain the circular velocity, assuming hydrostatic equilibrium. Within the half-light radius, M dyn,eml is on average lower than M dyn,*, with a mean offset of –0.15 ± 0.016 dex and galaxy-to-galaxy scatter of 0.19 dex, reflecting the combined random uncertainty. While data of higher spatial resolution are needed to understand this small offset, it supports the assumption that the galaxy-wide ionized gas kinematics do not predominantly originate from disruptive events such as star formation–driven outflows. However, a similar agreement can be obtained without modeling from the integrated emission line dispersions for axis ratios q < 0.8. This suggests that our current understanding of gas kinematics is not sufficient to efficiently apply asymmetric drift corrections to improve dynamical mass estimates compared with observations lacking the signal-to-noise ratio required for spatially extended dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".