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Record W1591095850 · doi:10.1093/mnras/stu1565

Signs of a faint disc population at polluted white dwarfs

2014· article· en· W1591095850 on OpenAlexaff
C. Bergfors, Jay Farihi, P. Dufour, M. Rocchetto

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
FundersScience and Technology Facilities Council
KeywordsPhysicsWhite dwarfInfrared excessPlanetesimalAstrophysicsBlack dwarfCircumstellar dustAstronomyPlanetary systemBrown dwarfInfraredPopulationStarsPlanet

Abstract

fetched live from OpenAlex

Observations of atmospheric metals and dust discs around white dwarfs provide important clues to the fate of terrestrial planetary systems around intermediate-mass stars. We present Spitzer Infrared Array Camera observations of 15 metal polluted white dwarfs to investigate the occurrence and physical properties of circumstellar dust created by the disruption of planetary bodies. We find subtle infrared excess emission consistent with warm dust around KUV 15519+1730 and HS 2132+0941, and weaker excess around the DZ white dwarf G245-58, which, if real, makes it the coolest white dwarf known to exhibit a 3.6 μm excess and the first DZ star with a bright disc. All together our data corroborate a picture where (1) discs at metal-enriched white dwarfs are commonplace and most escape detection in the infrared (possibly as narrow rings), (2) the discs are long lived, having lifetimes on the order of 106 yr or longer and (3) the frequency of bright, infrared detectable discs decreases with age, on a time-scale of roughly 500 Myr, suggesting large planetesimal disruptions decline on this same time-scale.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.516

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 head0.007
GPT teacher head0.196
Teacher spread0.189 · 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

Citations103
Published2014
Admission routes1
Has abstractyes

Explore more

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