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Record W3138035007 · doi:10.1051/0004-6361/202039708

Evidence for disequilibrium chemistry from vertical mixing in hot Jupiter atmospheres

2021· article· en· W3138035007 on OpenAlexaff
Claire Baxter, Jean-Michel Désert, Shang‐Min Tsai, Kamen Todorov, Jacob L. Bean, Drake Deming, Vivien Parmentier, Jonathan J. Fortney, Michael R. Line, Daniel Thorngren, Raymond T. Pierrehumbert, Adam Burrows, Adam P. Showman

Bibliographic record

VenueAstronomy and Astrophysics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
FundersEuropean CommissionSpace Telescope Science InstituteCalifornia Institute of TechnologyJet Propulsion LaboratoryNational Aeronautics and Space Administration
KeywordsPlanetExoplanetAstrophysicsPhotometry (optics)PhysicsHot JupiterAstrobiologyConvective mixingMethaneMixing ratioAtmospheric sciencesConvectionChemistryStarsThermodynamics

Abstract

fetched live from OpenAlex

Aims. We present a large atmospheric study of 49 gas giant exoplanets using infrared transmission photometry with Spitzer/IRAC at 3.6 and 4.5 μm. Methods. We uniformly analyze 70 photometric light curves of 33 transiting planets using our custom pipeline, which implements pixel level decorrelation. Augmenting our sample with 16 previously published exoplanets leads to a total of 49. We use this survey to understand how infrared photometry traces changes in atmospheric chemical properties as a function of planetary temperature. We compare our measurements to a grid of 1D radiative-convective equilibrium forward atmospheric models which include disequilibrium chemistry. We explore various strengths of vertical mixing (Kzz = 0–1012 cm2 s−1) as well as two chemical compositions (1x and 30x solar). Results. We find that, on average, Spitzer probes a difference of 0.5 atmospheric scale heights between 3.6 and 4.5 μm, which is measured at 7.5σ level of significance. Changes in the opacities in the two Spitzer bandpasses are expected with increasing temperature due to the transition from methane-dominated to carbon-monoxide-dominated atmospheres at chemical equilibrium. Comparing the data with our model grids, we find that the coolest planets show a lack of methane compared to expectations, which has also been reported by previous studies of individual objects. We show that the sample of coolest planets rule out 1x solar composition with >3σ confidence while supporting low vertical mixing (Kzz = 108 cm2 s−1). On the other hand, we find that the hot planets are best explained by models with 1x solar metallicity and high vertical mixing (Kzz = 1012 cm2 s−1). We interpret this as the lofting of CH4 to the upper atmospheric layers. Changing the interior temperature changes the expectation for equilibrium chemistry in deep layers, hence the expectation of disequilibrium chemistry higher up. We also find a significant scatter in the transmission signatures of the mid-temperate and ultra-hot planets, likely due to increased atmospheric diversity, without the need to invoke higher metallicities. Additionally, we compare Spitzer transmission with emission in the same bandpasses for the same planets and find no evidence for any correlation. Although more advanced modelling would test our conclusions further, our simple generic model grid points towards different amounts of vertical mixing occurring across the temperature range of hot Jupiters. This finding also agrees with the observed scatter with increasing planetary magnitude seen in Spitzer/IRAC color-magnitude diagrams for planets and brown dwarfs.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.019
GPT teacher head0.238
Teacher spread0.218 · 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

Citations48
Published2021
Admission routes1
Has abstractyes

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