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Record W2275900960 · doi:10.3847/2041-8205/822/1/l2

COSMOLOGISTS IN SEARCH OF PLANET NINE: THE CASE FOR CMB EXPERIMENTS

2016· article· en· W2275900960 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Letters · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsPlanetMillimeterConfusionAsteroidCosmic microwave backgroundUranusEclipticPlanck

Abstract

fetched live from OpenAlex

ABSTRACT Cosmology experiments at millimeter wavelengths can detect Planet Nine if it is the size of Neptune, has an effective temperature of 40 K, and is 700 au from the Sun. It would appear as a ∼30 mJy source at 1 mm with an annual parallax of ∼5 arcmin. The challenge is to distinguish it from the approximately 4000 foreground asteroids brighter than 30 mJy. Fortunately, these asteroids are known to the Minor Planet Center and can be identified because they move across a resolution element in a matter of hours, orders of magnitude faster than Planet Nine. If Planet Nine is smaller, colder, and/or more distant than expected, then it could be as faint as 1 mJy at 1 mm. There are roughly 10 6 asteroids this bright and many are unknown, making current cosmology experiments confusion limited for moving sources. Nonetheless, it may still be possible to find the proverbial needle in the haystack using a matched filter. This would require millimeter telescopes with high angular resolution and high sensitivity in order to alleviate confusion and to enable the identification of moving sources with relatively short time baselines. Regardless of its millimeter flux density, searching for Planet Nine would require frequent radio measurements for large swaths of the sky, including the ecliptic and Galactic plane. Even if Planet Nine had already been detected by other means, measuring its millimeter flux would constrain its internal energy budget, and therefore help resolve the mystery of Uranus and Neptune, which have vastly different internal heat.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.317

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.024
GPT teacher head0.305
Teacher spread0.281 · 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 designBench or experimental
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

Citations21
Published2016
Admission routes1
Has abstractyes

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