MétaCan
Menu
Back to cohort

New Praesepe white dwarfs and the initial mass-final mass relation

2006· preprint· en· W6929312486 on OpenAlexaff

Bibliographic record

VenuearXiv (Cornell University) · 2006
Typepreprint
Languageen
FieldComputer Science
TopicMathematics, Computing, and Information Processing
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsWhite dwarfExtrapolationContext (archaeology)Cluster (spacecraft)SupernovaRange (aeronautics)PleiadesOpen cluster

Abstract

fetched live from OpenAlex

We report the spectroscopic confirmation of four further white dwarf members of Praesepe. This brings the total number of confirmed white dwarf members to eleven making this the second largest collection of these objects in an open cluster identified to date. This number is consistent with the high mass end of the initial mass function of Praesepe being Salpeter in form. Furthermore, it suggests that the bulk of Praesepe white dwarfs did not gain a substantial recoil kick velocity from possible asymmetries in their loss of mass during the asymptotic giant branch phase of evolution. By comparing our estimates of the effective temperatures and the surface gravities of WD0833+194, WD0840+190, WD0840+205 and WD0843+184 to modern theoretical evolutionary tracks we have derived their masses to be in the range 0.72-0.76Msun and their cooling ages \~300Myrs. For an assumed cluster age of 625+/-50Myrs the infered progenitor masses are between 3.3-3.5Msun. Examining these new data in the context of the initial mass-final mass relation we find that it can be adequately represented by a linear function (a0=0.289+/-0.051, a1=0.133+/-0.015) over the initial mass range 2.7Msun to 6Msun. Assuming an extrapolation of this relation to larger initial masses is valid and adopting a maximum white dwarf mass of 1.3Msun, our results support a minimum mass for core-collapse supernovae progenitors in the range ~6.8-8.6Msun.

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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.071
GPT teacher head0.200
Teacher spread0.129 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

Same venuearXiv (Cornell University)Same topicMathematics, Computing, and Information ProcessingFrench-language works237,207