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Record W1976360590 · doi:10.1086/499522

Evolution and Environment of Early-Type Galaxies

2006· article· en· W1976360590 on OpenAlexaff
Mariangela Bernardi, R. C. Nichol, Ravi K. Sheth, C. J. Miller, J. Brinkmann

Bibliographic record

VenueThe Astronomical Journal · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsAstrophysicsPhysicsRedshiftGalaxyMetallicityPopulationLuminosityStar formationVelocity dispersionLine (geometry)AstronomyBalmer seriesSpectral lineEmission spectrumDemographyGeometry

Abstract

fetched live from OpenAlex

Chemical abundance indicators are studied using composite spectra, which we provide in tabular form. Tables of line strengths measured from these spectra and parameters derived from these line strengths are also provided. From these we find that at fixed luminosity, early-type galaxies in low-density environments are slightly bluer, with stronger O II emission and stronger Hδ and Hγ Balmer absorption lines, indicative of star formation in the not very distant past. These galaxies also tend to have systematically weaker D4000 indices. The Lick indices and α-element abundance indicators correlate weakly but significantly with environment. For example, at fixed velocity dispersion, Mg is weaker in early-type galaxies in low-density environments by 30% of the rms scatter across the full sample, whereas most Fe indicators show no significant environmental dependence. The galaxies in our sample span a redshift range that corresponds to look-back times of ∼1 Gyr. We see clear evidence for evolution of line-index strengths over this time. Since the low-redshift population is almost certainly a passively aged version of the more distant population, age is likely the main driver for any observed evolution. We use the observed redshift evolution as a model-independent clock to identify indicators that are more sensitive to age than to other effects such as metallicity. In principle, for a passively evolving population, comparison of the trends with redshift and environment constrain how strongly the luminosity-weighted ages and metallicities depend on environment. We develop a method for doing this that does not depend on the details of stellar population synthesis models. Our analysis suggests that the galaxies that populate the densest regions in our sample are older by ∼1 Gyr than objects of the same luminosity in the least dense regions, and that metallicity differences are negligible. We also use single-burst stellar population synthesis models, which allow for nonsolar α-element abundance ratios, to interpret our data. The combination of Hβ, Mg b , and ⟨Fe⟩ lines suggests that age, metallicity, and α-enhancement all increase with velocity dispersion. The objects at lower redshifts are older but have the same metallicities and α-enhancements as their counterparts of the same σ at higher redshifts, as expected if the low-redshift sample is a passively aged version of the sample at higher redshifts. In addition, objects in dense environments are less than 1 Gyr older and α-enhanced by ∼0.02 relative to their counterparts of the same velocity dispersion in less dense regions, but the metallicities show no dependence on environment. This suggests that in dense regions, the stars in early-type galaxies formed at slightly earlier times and on a slightly shorter timescale than in less dense regions. Using Hγ F instead of Hβ leads to slightly younger ages but the same qualitative differences between environments. In particular, we find no evidence that objects in low-density regions are more metal-rich.

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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.005
GPT teacher head0.182
Teacher spread0.177 · 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

Citations185
Published2006
Admission routes1
Has abstractyes

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