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Record W4412545151 · doi:10.3847/2041-8213/ade30f

JWST Coronagraphic Images of 14 Her c: A Cold Giant Planet in a Dynamically Hot Multiplanet System

2025· article· en· W4412545151 on OpenAlexaff
Daniella C. Bardalez Gagliuffi, William O. Balmer, Laurent Pueyo, Timothy D. Brandt, Mark R. Giovinazzi, Sarah Millholland, Tiger Lu, Malena Rice, James Mang, Caroline Morley, Brianna Lacy, J. H. Girard, Elisabeth C. Matthews, Aarynn L. Carter, Brendan P. Bowler, Jacqueline K. Faherty, C. Fontanive, Emily Rickman

Bibliographic record

VenueThe Astrophysical Journal Letters · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsMontreal Clinical Research Institute
FundersSpace Telescope Science Institute
KeywordsPlanetAstrobiologyGiant planetExoplanetPhysicsAstronomyPlanetary system

Abstract

fetched live from OpenAlex

Abstract Most observed multiplanet systems are coplanar, in a dynamically “cold” configuration of concentric orbits like our own solar system. With the James Webb Space Telescope, we have detected 14 Her c, the first mature and cold exoplanet directly imaged in a dynamically “hot” multiplanet system. With large eccentricities and a nonzero mutual inclination, the present-day architecture of this system points to a turbulent past and ongoing angular momentum exchange between the planetary orbits of 14 Her b and c. The temperature of 14 Her c rivals both the coldest imaged exoplanet and the coldest known brown dwarf. Moreover, its photometry at 4.4 μ m is consistent with the presence of carbon disequilibrium chemistry and water-ice clouds in its atmosphere. 14 Her c presents a unique laboratory to study giant planet formation, the dynamical evolution of multiplanet system architectures, and atmospheric composition and dynamics in extremely cold worlds.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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

Citations20
Published2025
Admission routes1
Has abstractyes

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