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Record W4406188187 · doi:10.3847/1538-4357/ad9902

Hot Spot Offset Variability from Magnetohydrodynamical Thermoresistive Instability in Hot Jupiters

2025· article· en· W4406188187 on OpenAlexfundno aff
Raphaël Hardy, Paul Charbonneau, A. Cumming

Bibliographic record

VenueThe Astrophysical Journal · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsHot JupiterInstabilityAstronomyAstrophysicsOffset (computer science)Plasma instabilityStellar windHot spot (computer programming)AstrobiologyMechanicsStarsExoplanet

Abstract

fetched live from OpenAlex

Abstract Hot Jupiter (HJ) atmospheres are possibly subject to a thermoresistive instability (TRI). Such an instability may develop as the ohmic heating increases the electrical conductivity in a positive feedback loop, which ultimately leads to a runaway of the atmospheric temperature. We extend our previous axisymmetric one-dimensional radial model, by representing the temperature and magnetic diffusivity as a first-order Fourier expansion in longitude. This allows us to predict the hot spot offset during the rapid unfolding of the TRI and following Alfvénic oscillations. The instability is periodically triggered and damped within ≈10–40 days, depending on the magnetic field strength, with months of slow buildup between recurring bursts. We show a few representative simulations undergoing TRI, in which the peak flux offset varies between approximately ±60∘ on a timescale of a few days with potentially observable brightness variations. Therefore, this TRI could be an observable feature of HJs, given the right timing of observation and transit and the right planetary parameters.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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 designSimulation or modeling
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

Citations2
Published2025
Admission routes1
Has abstractyes

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