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Record W4416775297 · doi:10.1126/sciadv.adv3324

Large-amplitude variability driven by giant dust storms on a planetary-mass companion

2025· article· en· W4416775297 on OpenAlexaff
Xianyu Tan, Xi Zhang, Mark S. Marley, Yifan Zhou, Ben W. P. Lew, Brittany Miles, Natasha E. Batalha, Beth Biller, G. Chauvin, Sasha Hinkley, Kielan K. W. Hoch, Elena Manjavacas, Stanimir Metchev, Simon Petrus, Emily Rickman, Andrew Skemer, Genaro Suárez, Ben J. Sutlieff, Johanna M. Vos, Niall Whiteford

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsWestern University
FundersNASA HeadquartersNational Natural Science Foundation of ChinaShanghai Jiao Tong UniversityRoyal SocietyNational Science Foundation
KeywordsAtmosphere (unit)StormWavelengthExoplanetFlux (metallurgy)Dust stormBrown dwarf

Abstract

fetched live from OpenAlex

Large-amplitude variations are commonly observed in the atmospheres of directly imaged exoplanets and brown dwarfs. VHS 1256B, the most variable known planet-mass object, exhibits a near-infrared flux change of nearly 40%, with red color and silicate features revealed in recent JWST spectra, challenging current theories. Using a general circulation model, we demonstrate that VHS 1256B's atmosphere is dominated by planetary-scale dust storms persisting for tens of days, with large patchy clouds propagating with equatorial waves. This weather pattern, distinct from the banded structures seen on solar system giants, simultaneously explains the observed spectra and critical features in the rotational light curves, including the large amplitude, irregular evolution, and wavelength dependence, as well as the variability trends observed in near-infrared color-magnitude diagrams of dusty substellar atmospheres.

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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 head0.008
GPT teacher head0.257
Teacher spread0.248 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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