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Record W4400732404 · doi:10.3847/2041-8213/ad5e63

A Deficit of Massive White Dwarfs in Gaia Astrometric Binaries

2024· article· en· W4400732404 on OpenAlexfundno aff
Na’ama Hallakoun, S. Shahaf, T. Mazeh, Silvia Toonen, Sagi Ben-Ami

Bibliographic record

VenueThe Astrophysical Journal Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersAzrieli FoundationIsrael Science FoundationWeizmann Institute of ScienceEuropean Space Agency
KeywordsWhite dwarfPhysicsAstronomyStarsAstrophysics

Abstract

fetched live from OpenAlex

Abstract The third data release of Gaia introduced a large catalog of astrometric binaries, out of which about 3200 are likely main-sequence stars with a white dwarf (WD) companion. These binaries are typically found with orbital separations of ∼1 au, a separation range that was largely unexplored due to observational challenges. Such systems are likely to have undergone a phase of stable mass transfer while the WD progenitor was on the asymptotic giant branch. Here we study the WD mass distribution of a volume-complete sample of binaries with K/M dwarf primaries and orbital separations of ∼1 au. We find that the number of massive WDs relative to the total number of WDs in these systems is smaller by an order of magnitude compared to their occurrence among single WDs in the field. One possible reason can be an implicit selection of the WD mass range if these are indeed post-stable-mass-transfer systems. Another reason can be the lack of merger products in our sample compared to the field, due to the relatively tight orbital separations of these systems. In addition, we find that about 14% of these systems have distant tertiary companions within 1 pc.

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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations19
Published2024
Admission routes1
Has abstractyes

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