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Record W2810325998 · doi:10.3847/1538-4357/aad09b

The WFC3 Galactic Bulge Treasury Program: Relative Ages of Bulge Stars of High and Low Metallicity<sup>∗</sup>

2018· article· en· W2810325998 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBulgeGlobular clusterMilky WayStarsGalaxyMetallicityPhotometry (optics)Luminosity

Abstract

fetched live from OpenAlex

Abstract The Hubble Space Telescope /WFC3 multiband photometry spanning from the UV to the near-IR of four fields in the Galactic bulge, together with that for six template globular and open clusters, are used to photometrically tag the metallicity [Fe/H] of stars in these fields after proper-motion rejecting most foreground disk contaminants. Color–magnitude diagrams and luminosity functions (LF) are then constructed, in particular for the most metal-rich and most metal-poor stars in each field. We do not find any significant difference between the I -band and H -band LFs, hence turnoff luminosity and age of the metal-rich and metal-poor components therefore appear essentially coeval. In particular, we find that no more than ∼3% of the metal-rich component can be ∼5 Gyr old, or younger. Conversely, theoretical LFs match well to the observed ones for an age of ∼10 Gyr. Assuming this age is representative for the bulk of bulge stars, we then recall the observed properties of star-forming galaxies at 10 Gyr lookback time, i.e., at z ∼ 2, and speculate about bulge formation in that context. We argue that bar formation and buckling instabilities leading to the observed boxy/peanut, X-shaped bulge may have arisen late in the history of the Milky Way Galaxy, once its gas fraction had decreased compared to the high values typical of high-redshift galaxies. This paper follows the public release of the photometric and astrometric catalogs of the measured stars in the four fields.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.271
Teacher spread0.260 · 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 designOther design
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

Citations55
Published2018
Admission routes1
Has abstractyes

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