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Record W4386162475 · doi:10.3847/2041-8213/acec4b

Ultracool Dwarfs Observed with the Spitzer Infrared Spectrograph: Equatorial Latitudes in L Dwarf Atmospheres Are Cloudier

2023· article· en· W4386162475 on OpenAlexaff
Genaro Suárez, Johanna M. Vos, Stanimir Metchev, Jacqueline K. Faherty, Kelle L. Cruz

Bibliographic record

VenueThe Astrophysical Journal Letters · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsWestern University
Fundersnot available
KeywordsOpacityBrown dwarfPhysicsAstrophysicsEquatorInfraredAstronomyLatitudeMassive compact halo objectSpitzer Space TelescopeStars

Abstract

fetched live from OpenAlex

Abstract We report direct observational evidence for a latitudinal dependence of dust cloud opacity in ultracool dwarfs, indicating that equatorial latitudes are cloudier than polar latitudes. These results are based on a strong positive correlation between the viewing geometry and the mid-infrared silicate absorption strength in mid-L dwarfs using mid-infrared spectra from the Spitzer Space Telescope and spin axis inclination measurements from available information in the literature. We confirmed that the infrared color anomalies of L dwarfs positively correlate with dust cloud opacity and viewing geometry, where redder objects are inclined equator-on and exhibit more opaque dust clouds, while dwarfs viewed at higher latitudes and with more transparent clouds are bluer. These results show the relevance of viewing geometry to explain the appearance of brown dwarfs and provide insight into the spectral diversity observed in substellar and planetary atmospheres. We also find a hint that dust clouds at similar latitudes may have higher opacity in low-surface gravity dwarfs than in higher-gravity objects.

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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.015
GPT teacher head0.216
Teacher spread0.201 · 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

Citations35
Published2023
Admission routes1
Has abstractyes

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