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Debris discs at centimetre wavelengths: planetesimal populations in young extrasolar Kuiper belts

2012· article· en· W1930848903 on OpenAlexaff
J. S. Greaves, Antonio Hales, Brian Mason, Brenda C. Matthews

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society Letters · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsNational Research Council CanadaHerzberg Institute of Astrophysics
FundersScience and Technology Facilities Council
KeywordsPlanetesimalAstrobiologyPhysicsDebrisWavelengthAstronomyGeologyAstrophysicsSolar SystemOpticsMeteorology

Abstract

fetched live from OpenAlex

ABSTRACT Two luminous debris discs around 100 Myr old have been searched for 1 cm dust emission, to a depth three times greater than in any such previous study. Very low limits were set towards both HD 377 and HD 104860 (noise levels of 12–14 Jy), extending the spectral range from 70 m to 3 mm where cool dust has previously been sought. The almost-identical fluxes of the two systems were merged into an average spectral energy distribution, which was then fitted using a distribution of grain sizes. The canonical infinite collisional cascade, with numbers of particles n(D) ∝ D−3.5 for diameter D, overpredicts the 1-cm flux, which should have been detected at over 3σ for the merged system. Preferred solutions have truncated grain populations with largest particles around 1.5–4 mm in diameter, and slightly flatter distributions, up to n(D) ∝ D−3.1. The lack of cm-sized and larger particles is reminiscent of the similar deficit inferred around comets from spacecraft encounters. Such departures from simple power-law distributions of particles have been predicted in recent models where some size regimes are more readily broken up than others. A deficit of cm-scale particles can explain the fits we obtain, and reduces the total masses inferred for the comet belts of these stars.

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 score0.104
Threshold uncertainty score0.680

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 head0.011
GPT teacher head0.205
Teacher spread0.194 · 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

Citations31
Published2012
Admission routes1
Has abstractyes

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