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Record W2900589647 · doi:10.3847/2041-8213/aaf16b

1D Kinematics from Stars and Ionized Gas at z ∼ 0.8 from the LEGA-C Spectroscopic Survey of Massive Galaxies

2018· article· en· W2900589647 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsYork University
FundersScience and Technology Facilities Council
KeywordsGalaxySpectrographStarsSpectral lineVelocity dispersionKinematicsIonizationStellar kinematics

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.010
GPT teacher head0.213
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2018
Admission routes1
Has abstractyes

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