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Record W4394737172 · doi:10.1093/mnras/stae986

Polluting white dwarfs with Oort cloud comets

2024· article· en· W4394737172 on OpenAlexafffund
Dang Pham, Hanno Rein

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsWhite dwarfAstrophysicsInterstellar cometAstronomyAccretion (finance)Galactic tideCometPrecessionBrown dwarfStarsGalactic haloGalaxyHalo

Abstract

fetched live from OpenAlex

ABSTRACT Observations point to old white dwarfs (WDs) accreting metals at a relatively constant rate over 8 Gyr. Exo-Oort clouds around WDs have been proposed as potential reservoirs of materials, with galactic tide as a mechanism to deliver distant comets to the WD’s Roche limit. In this work, we characterize the dynamics of comets around a WD with a companion having semimajor axes on the orders of 10–100 au. We develop simulation techniques capable of integrating a large number (108) of objects over a 1 Gyr time-scale. Our simulations include galactic tide and are capable of resolving close interactions with a massive companion. Through simulations, we study the accretion rate of exo-Oort cloud comets into a WD’s Roche limit. We also characterize the dynamics of precession and scattering induced on a comet by a massive companion. We find that (i) WD pollution by an exo-Oort cloud can be sustained over a Gyr time-scale, (ii) an exo-Oort cloud with structure like our own Solar system’s is capable of delivering materials into an isolated WD with pollution rate ∼108 g s−1, (iii) adding a planetary-mass companion reduces the pollution rate to ∼107 g s−1, and (iv) if the companion is stellar mass, with Mp ≳ 0.1 M⊙, the pollution rate reduces to ∼3 × 105 g s−1 due to a combination of precession induced on a comet by the companion, a strong scattering barrier, and low likelihood of direct collisions of comets with the companion.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.482

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 head0.006
GPT teacher head0.191
Teacher spread0.186 · 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 designSimulation or modeling
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

Citations9
Published2024
Admission routes2
Has abstractyes

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