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Record W4412120685 · doi:10.5194/epsc-dps2025-644

A Homogeneous Study of Exoplanetary Atmospheres Using High-Resolution Transit Spectroscopy

2025· preprint· en· W4412120685 on OpenAlexaboutno aff
Adrien Masson, Sandrine Vinatier, Bruno Bézard

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsnot available
Fundersnot available
KeywordsHomogeneousTransit (satellite)ExoplanetSpectroscopyHigh resolutionAstrobiologyPhysicsMaterials scienceAstronomyGeologyRemote sensingEngineeringStarsThermodynamicsTransport engineering

Abstract

fetched live from OpenAlex

Thousands of exoplanets have been confirmed in the last two decades, and yet observational constraints on their compositions have only been obtained for a few hundred of them so far. Current detection methods only give access to the radius and mass of a planet and therefore to its bulk density, which generally induces large degeneracies in terms of composition and structure. Characterization of exoplanet atmospheres has therefore emerged as a challenge for the exoplanet scientific community. Constraining the composition, dynamics, and overall structure of an exoplanet atmosphere allows us to infer its formation and evolution history and to put our Solar system in a broader context through comparative planetary science. Transmission spectroscopy has emerged as a powerful method to characterize exoplanetary atmospheres: when an exoplanet transits its host star, part of the stellar flux passes through the exoplanet’s atmosphere, imprinting the signature of its molecular components on the observed flux. Observing these transmission spectra with ground-based high-resolution spectrometers allows us to detect the species present in the atmosphere and probe the dynamics of the atmosphere by resolving the individual lines, all while being less sensitive to clouds and hazes' opacities than space-based observations at medium resolution. However, the analysis of ground-based observations requires state-of-the-art data reduction and processing methods to correct for the Earth's atmosphere and the background host star contributions and to extract the faint planetary signal whose individual line amplitudes are orders of magnitude weaker than the noise. I will present the work, codes, and methodology developed during my PhD to characterize the atmosphere of fifteen exoplanets observed with the SPIRou instrument, a near-infrared spectropolarimeter at the Canada-France-Hawaii Telescope. This presentation will focus on our search for the metastable He triplet signature at 1083.3 nm (in vacuum), a near-infrared probe for atmospheric escape, and for molecular signatures to constrain the atmospheric composition and structure of 15 targets ranging from super-Earth and sub-Neptunes to hot Jupiters (Fig. 1.). I will present our results in terms of mass loss rate and escape temperature constraints obtained with Parker-wind modelisation of atmospheric escape (Fig. 2.). I will then discuss our results regarding the presence and abundances of molecules such as H2O, CO, and CH4, obtained with Cross Correlation Function and Nested Sampling methods coupled with a 1D radiative-convective equilibrium code and a high-resolution radiative transfer model (Fig. 3.). Applying the same reduction pipeline on a set of targets observed with the same instrument further allowed us to provide homogeneously retrieved constraints on these targets, paving the way toward a statistical understanding of exoplanets in terms of atmospheric composition and structure. Fig. 1. List of the fifteen targets studied in this work Fig. 2. Detection of the metastable He triplet lines (black) in HAT-P-11 b and fitting with a Parker wind escape model (red) Fig. 3. Cross Correlation Function map in velocity space showing the detection of H2O in WASP-127 b

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 score0.002
Threshold uncertainty score0.003

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 communication0.0010.001
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 head0.252
Teacher spread0.234 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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