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Record W2786912932 · doi:10.3847/1538-3881/aaabbd

Cloud Atlas: Rotational Modulations in the L/T Transition Brown Dwarf Companion HN Peg B

2018· article· en· W2786912932 on OpenAlexaff
Yifan Zhou, Dániel Apai, Stanimir Metchev, Ben W. P. Lew, Glenn Schneider, Mark S. Marley, Theodora Karalidi, Elena Manjavacas, L. R. Bedin, Nicolas B. Cowan, Paulo A. Miles-Páez, Patrick Lowrance, Jacqueline Radigan, Adam J. Burgasser

Bibliographic record

VenueThe Astronomical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsMcGill UniversityWestern University
Fundersnot available
KeywordsPhysicsBrown dwarfAmplitudeAstrophysicsWavelengthLight curveWide Field Camera 3OpticsPlanetHubble space telescopeStars

Abstract

fetched live from OpenAlex

Abstract Time-resolved observations of brown dwarfs’ rotational modulations provide powerful insights into the properties of condensate clouds in ultra-cool atmospheres. Multi-wavelength light curves reveal cloud vertical structures, condensate particle sizes, and cloud morphology, which directly constrain condensate cloud and atmospheric circulation models. We report results from Hubble Space Telescope /Wide Field Camera 3 near-infrared G141 taken in six consecutive orbits observations of HN Peg B, an L/T transition brown dwarf companion to a G0V type star. The best-fit sine wave to the 1.1–1.7 μ m broadband light curve has an amplitude of 1.206% ± 0.025% and period of 15.4 ± 0.5 hr. The modulation amplitude has no detectable wavelength dependence except in the 1.4 μ m water absorption band, indicating that the characteristic condensate particle sizes are large (>1 μ m). We detect significantly (4.4 σ ) lower modulation amplitude in the 1.4 μ m water absorption band and find that HN Peg B’s spectral modulation resembles those of early T type brown dwarfs. We also describe a new empirical interpolation method to remove spectral contamination from the bright host star. This method may be applied in other high-contrast time-resolved observations with WFC3.

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 score0.019
Threshold uncertainty score0.568

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)0.0010.000

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 head0.016
GPT teacher head0.240
Teacher spread0.224 · 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

Citations35
Published2018
Admission routes1
Has abstractyes

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