1D Kinematics from Stars and Ionized Gas at z ∼ 0.8 from the LEGA-C Spectroscopic Survey of Massive Galaxies
Bibliographic record
Abstract
Abstract We present a comparison of the observed, spatially integrated stellar and ionized gas velocity dispersions of ∼1000 massive ( ) galaxies in the Large Early Galaxy Astrophysics Census survey at 0.6 ≲ z ≲ 1.0. The high S/N ∼ 20 Å −1 afforded by 20 hr Very Large Telescope/Visible Multi-Object Spectrograph spectra allows for joint modeling of the stellar continuum and emission lines in all galaxies, spanning the full range of galaxy colors and morphologies. These observed integrated velocity dispersions (denoted as and ) are related to the intrinsic velocity dispersions of ionized gas or stars, but also include rotational motions through beam smearing and spectral extraction. We find good average agreement between observed velocity dispersions, with . This result does not depend strongly on stellar population, structural properties, or alignment with respect to the slit. However, in all regimes we find significant scatter between and , with an overall scatter of 0.13 dex of which 0.05 dex is due to observational uncertainties. For an individual galaxy, the scatter between and translates to an additional uncertainty of ∼0.24 dex on dynamical mass derived from , on top of measurement errors and uncertainties from Virial constant or size estimates. We measure the z ∼ 0.8 stellar mass Faber–Jackson relation and demonstrate that emission line widths can be used to measure scaling relations. However, these relations will exhibit increased scatter and slopes that are artificially steepened by selecting on subsets of galaxies with progressively brighter emission lines.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".