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Record W2020060144 · doi:10.1086/427551

The Dearth of Massive, Helium-rich White Dwarfs in Young Open Star Clusters

2004· article· en· W2020060144 on OpenAlexaff
Jasonjot Singh Kalirai, Harvey B. Richer, Brad M. S. Hansen, D. Reitzel, R. Michael Rich

Bibliographic record

VenueThe Astrophysical Journal · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysicsOpen clusterHeliumWhite dwarfAstrophysicsCluster (spacecraft)Star clusterStellar classificationPopulationSpectral lineHydrogenAstronomyStarsAtomic physicsMedicine

Abstract

fetched live from OpenAlex

Spectra have been obtained of 21 white dwarfs (WDs) in the direction of the young, rich open star cluster NGC 2099. This represents an appreciable fraction (>30%) of the cluster's total WD population. The mean derived mass of the sample is 0.8 M ☉ —about 0.2 M ☉ larger than the mean seen among field WDs. A surprising result is that all of the NGC 2099 WDs have hydrogen-rich atmospheres (DAs); none exhibit helium-rich ones (DBs) or any other spectral class. The number ratio in the field at the temperatures of the NGC 2099 WDs is DA/DB ~ 3.5. While the probability of seeing no DB WDs in NGC 2099 solely by chance is ~2%, if we include WDs in other open clusters of similar age it then becomes highly unlikely that the dearth of DB WDs in young open clusters is just a statistical fluctuation. We explore possible reasons for the lack of DBs in these clusters and conclude that the most promising scenario for the DA/DB number ratio discrepancy in young clusters is that hot, high-mass WDs do not develop large enough helium convection zones to allow helium to be brought to the surface and turn a hydrogen-rich WD into a helium-rich one.

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)0.002
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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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

Citations27
Published2004
Admission routes1
Has abstractyes

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