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Record W2586861863 · doi:10.3847/2041-8213/836/1/l7

The Chemical Composition of an Extrasolar Kuiper-Belt-Object*

2017· article· en· W2586861863 on OpenAlexaff
Siyi Xu, B. Zuckerman, P. Dufour, Edward Young, B. Klein, M. Jura

Bibliographic record

VenueThe Astrophysical Journal Letters · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPhysicsWhite dwarfExoplanetSolar SystemAstronomyCometDwarf planetAstrobiologyPlanetary systemAstrophysicsAccretion (finance)PlanetPlanetary massStars

Abstract

fetched live from OpenAlex

Abstract The Kuiper Belt of our solar system is a source of short-period comets that may have delivered water and other volatiles to Earth and the other terrestrial planets. However, the distribution of water and other volatiles in extrasolar planetary systems is largely unknown. We report the discovery of an accretion of a Kuiper-Belt-Object analog onto the atmosphere of the white dwarf WD 1425+540. The heavy elements C, N, O, Mg, Si, S, Ca, Fe, and Ni are detected, with nitrogen observed for the first time in extrasolar planetary debris. The nitrogen mass fraction is ∼2%, comparable to that in comet Halley and higher than in any other known solar system object. The lower limit to the accreted mass is ∼10 22 g, which is about one hundred thousand times the typical mass of a short-period comet. In addition, WD 1425+540 has a wide binary companion, which could facilitate perturbing a Kuiper-Belt-Object analog into the white dwarf’s tidal radius. This finding shows that analogs to objects in our Kuiper Belt exist around other stars and could be responsible for the delivery of volatiles to terrestrial planets beyond the solar system.

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)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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

Citations132
Published2017
Admission routes1
Has abstractyes

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