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Record W4304182909 · doi:10.1051/0004-6361/202243956

Exoplanet weather and climate regimes with clouds and thermal ionospheres

2022· article· en· W4304182909 on OpenAlexfundno aff
Ch. Helling, Dominic Samra, David Lewis, Robb Calder, Georgina Hirst, P. Woitke, Robin Baeyens, L. Carone, Oliver Herbort, K. L. Chubb

Bibliographic record

VenueAstronomy and Astrophysics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
FundersHorizon 2020 Framework ProgrammeKU LeuvenLaidlaw FoundationRoyal SocietyUniversity of St AndrewsScience and Technology Facilities CouncilEuropean Commission
KeywordsExoplanetPhysicsPlanetGas giantAstrophysicsContext (archaeology)AstronomyAstrobiology

Abstract

fetched live from OpenAlex

Context. Gaseous exoplanets are the targets that enable us to explore fundamentally our understanding of planetary physics and chemistry. With observational efforts moving from the discovery into the characterisation mode, systematic campaigns that cover large ranges of global stellar and planetary parameters will be needed to disentangle the diversity of exoplanets and their atmospheres that all are affected by their formation and evolutionary paths. Ideally, the spectral range includes the high-energy (ionisation) and the low-energy (phase-transitions) processes as they carry complementary information of the same object. Aims. We aim to uncover cloud formation trends and globally changing chemical regimes into which gas-giant exoplanets may fall due to the host star’s effect on the thermodynamic structure of their atmospheres. We aim to examine the emergence of an ionosphere as indicator for potentially asymmetric magnetic field effects on these atmospheres. We aim to provide input for exoplanet missions such as JWST, PLATO, and Ariel, as well as potential UV missions ARAGO, PolStar, or POLLUX on LUVOIR. Methods. Pre-calculated 3D GCMs for M, K, G, F host stars are the input for our kinetic cloud model for the formation of nucleation seeds, the growth to macroscopic cloud particles and their evaporation, gravitational settling, element conservation and gas chemistry. Results. Gaseous exoplanets fall broadly into three classes: i) cool planets with homogeneous cloud coverage, ii) intermediate temperature planets with asymmetric dayside cloud coverage, and iii) ultra-hot planets without clouds on the dayside. In class ii), the dayside cloud patterns are shaped by the wind flow and irradiation. Surface gravity and planetary rotation have little effect. For a given effective temperature, planets around K dwarfs are rotating faster compared to G dwarfs leading to larger cloud inhomogeneities in the fast rotating case. Extended atmosphere profiles suggest the formation of mineral haze in form of metal-oxide clusters (e.g. (TiO 2 ) N ). Conclusions. The dayside cloud coverage is the tell-tale sign for the different planetary regimes and their resulting weather and climate appearance. Class (i) is representative of planets with a very homogeneous cloud particle size and material compositions across the globe (e.g., HATS-6b, NGTS-1b), classes (ii, e.g., WASP-43b, HD 209458b) and (iii, e.g., WASP-121b, WP 0137b) have a large day-night divergence of the cloud properties. The C/O ratio is, hence, homogeneously affected in class (i), but asymmetrically in class (ii) and (iii). The atmospheres of class (i) and (ii) planets are little affected by thermal ionisation, but class (iii) planets exhibit a deep ionosphere on the dayside. Magnetic coupling will therefore affect different planets differently and will be more efficient on the more extended, cloud-free dayside. How the ionosphere connects atmospheric mass loss at the top of the atmosphere with deep atmospheric layers need to be investigated to coherently interpret high resolution observations of ultra-hot planets.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.181
Teacher spread0.176 · 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

Citations34
Published2022
Admission routes1
Has abstractyes

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