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Record W3103692903 · doi:10.25916/sut.26242661.v1

COMPARING THE WHITE DWARF COOLING SEQUENCES IN 47 Tuc AND NGC 6397

2024· article· en· W3103692903 on OpenAlexaff
Harvey B. Richer, Ryan Goldsbury, Jeremy Heyl, Jarrod R. Hurley, Aaron Dotter, Jason S. Kalirai, Kristin A. Woodley, Gregory G. Fahlman, R. Michael Rich, Michael M. Shara

Bibliographic record

VenueFigshare · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsHerzberg Institute of AstrophysicsUniversity of British Columbia
FundersAustralian Research Council
KeywordsPhysicsAstrophysicsWhite dwarfGlobular clusterAstronomyBlue dwarfProper motionMetallicityHubble space telescopeMagnitude (astronomy)Stars

Abstract

fetched live from OpenAlex

Using deep Hubble Space Telescope imaging, color-magnitude diagrams are constructed for the globular clusters 47 Tuc and NGC 6397. As expected, because of its lower metal abundance, the main sequence of NGC 6397 lies well to the blue of that of 47 Tuc. A comparison of the white dwarf cooling sequences of the two clusters, however, demonstrates that these sequences are indistinguishable over most of their loci - a consequence of the settling out of heavy elements in the dense white dwarf atmosphere and the near equality of their masses. Lower quality data on M4 continues this trend to a third cluster whose metallicity is intermediate between these two. While the path of the white dwarfs in the color-magnitude diagram is nearly identical in 47 Tuc and NGC 6397, the numbers of white dwarfs along the path are not. This results from the relatively rapid relaxation in NGC 6397 compared to 47 Tuc and provides a cautionary note that simply counting objects in star clusters in random locations as a method of testing stellar evolutionary theory is likely dangerous unless dynamical considerations are included.

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 score0.046
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.044
GPT teacher head0.257
Teacher spread0.213 · 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

Citations21
Published2024
Admission routes1
Has abstractyes

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