Observer les atmosphères d’exoplanètes avec l'instrument SPIRou au Téléscope Canada-France-Hawaï
Notice bibliographique
Résumé
Over the past three decades, the detection of more than 5500 exoplanets has revealed their vast diversity in mass, radius, and equilibrium temperature. This in turn sparked interest in further understanding these planets, from their potential to harbor life to how they formed and migrated to their current orbital locations. Atmospheric characterisation has proven to be a key tool for this, providing a window into the specific properties of an exoplanet. Thanks to their inflated atmospheres and close proximity to their stars, hot and ultra-hot Jupiters are the best targets to refine the tools used to analyse exoplanet atmospheric spectral data to extract information about their atmospheric properties. Their day/night temperature dichotomy, fast rotation, and strong atmospheric dynamics however make the atmospheres of these planets intrinsically 3-D, complicating the retrieval of the bulk abundances for these atmospheres required to infer the properties of the planets themselves. This thesis mainly focused on the study of the ultra-hot Jupiter WASP-76 b, in particular what could be learned about it using data acquired by the SPIRou spectrograph. This was in large part motivated by the asymmetry found for this planet's atmosphere in optical data. By looking at it with infrared data, we probe different pressure layers, giving a different insight into it's properties. For this, I helped develop a data analysis pipeline within the ATMOSPHERIX programme that was optimised for analysing transmission spectra obtained with SPIRou. The pipeline cleans the spectral data to bring out the atmospheric signal, creates synthetic spectra to analyse it, and can both validate the detection of an atmosphere and retrieve the most likely values for the atmospheres parameters, such as temperature and composition. Applying it to SPIRou-acquired data of WASP-76 b, I was able to perform an in-depth study of this planet's atmospheric properties. In particular, I was able to detect H dollar_2 dollar O and CO and analyse the dynamics associated to each, offering possible reasons for the atmosphere's asymmetry. To better understand the 3-D nature of hot and ultra-hot Jupiters, I started working with a team that studies atmospheric models. I used simulated transmission spectra from 18 models with different orbital periods, to investigate how 3-D effects influence measurements of observables using 1-D synthetic spectra. Specifically, I analysed the relation between shifts measured for observables of a planet and the planet's rotation and atmospheric winds. Degeneracies in atmospheric studies can complicate retrieval results and the inferred formation and migration scenarios of hot and ultra-hot Jupiters. A proposed solution to resolve them is to combine datasets. We have started to expand the ATMOSPHERIX pipeline's capabilities to perform combined retrievals on different datasets, which I have tested on data obtained of WASP-76 b. I present preliminary results obtained for combining the previously used SPIRou-acquired data with low-resolution data acquired by HST and Spitzer in one retrieval, and with high-resolution optical data acquired by MAROON-X in another. While the results show a need to improve our combined retrieval algorithms, they also show potential. Overall, I helped develop the ATMOSPHERIX pipeline, a promising tool for the exoplanet atmospheric characterisation community, and highlighted the use of SPIRou-acquired data in atmospheric studies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».