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Record W1991484299 · doi:10.1088/0004-637x/721/1/738

THE DUAL ORIGIN OF STELLAR HALOS. II. CHEMICAL ABUNDANCES AS TRACERS OF FORMATION HISTORY

2010· article· en· W1991484299 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMilky WayStarsGalactic haloGalaxyMetallicityHaloGalaxy formation and evolutionStar formation

Abstract

fetched live from OpenAlex

Fully cosmological, high-resolution N-body+smooth particle hydrodynamic simulations are used to investigate the chemical abundance trends of stars in simulated stellar halos as a function of their origin. These simulations employ a physically motivated supernova feedback recipe, as well as metal enrichment, metal cooling, and metal diffusion. As presented in an earlier paper, the simulated galaxies in this study are surrounded by stellar halos whose inner regions contain both stars accreted from satellite galaxies and stars formed in situ in the central regions of the main galaxies and later displaced by mergers into their inner halos. The abundance patterns ([Fe/H] and [O/Fe]) of halo stars located within 10 kpc of a solar-like observer are analyzed. We find that for galaxies which have not experienced a recent major merger, in situ stars at the high [Fe/H] end of the metallicity distribution function are more [α/Fe]-rich than accreted stars at similar [Fe/H]. This dichotomy in the [O/Fe] of halo stars at a given [Fe/H] results from the different potential wells within which in situ and accreted halo stars form. These results qualitatively match recent observations of local Milky Way halo stars. It may thus be possible for observers to uncover the relative contribution of different physical processes to the formation of stellar halos by observing such trends in the halo populations of the Milky Way and other local L^* galaxies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

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

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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations107
Published2010
Admission routes1
Has abstractyes

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