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Record W4362589080 · doi:10.3847/1538-4357/acbfaa

Distillation of <sup>56</sup>Fe in Ultramassive O–Ne White Dwarfs

2023· article· en· W4362589080 on OpenAlexaff
M. E. Caplan, Simon Blouin, Ian F. Freeman

Bibliographic record

VenueThe Astrophysical Journal · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of Victoria
FundersOffice of International Science and EngineeringDivision of Physics
KeywordsWhite dwarfPhysicsAstrophysicsSupernovaNucleosynthesisAsteroseismologyStars

Abstract

fetched live from OpenAlex

Abstract When white dwarfs freeze, the plasma mixtures inside them undergo separation processes that can produce radical changes in the composition profile of the star. The abundance of neutron-rich elements, such as 22 Ne or 56 Fe, determines whether or not the first crystals are more or less dense than the surrounding fluid and thus whether they sink or float. These processes have now been studied for C–O–Ne and C–O–Fe mixtures, finding that distillation and precipitation processes are possible in white dwarfs. In this work, we calculate the phase diagram of more complicated O–Ne–Fe mixtures and make predictions for the internal structure of the separated white dwarf. There are two possible outcomes determined by a complicated interplay between the Ne abundance, the 22 Ne fraction, and the 56 Fe abundance. Either Fe distills to form an inner core because the first O–Ne solids are buoyant, or an O–Ne inner core forms and Fe accumulates in the liquid until Fe distillation begins and forms an Fe shell. In the case of an Fe shell, a Rayleigh–Taylor instability may arise and overturn the core. In either case, Fe distillation may only produce a cooling delay of order 0.1 Gyr, as these processes occur early at high white dwarf luminosities. Fe inner cores and shells may be detectable through asteroseismology and could enhance the yield of neutron-rich elements such as 55 Mn and 58 Ni in supernovae.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.331

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.221
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 teacher head, 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

Citations10
Published2023
Admission routes1
Has abstractyes

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