Observer les atmosphères d’exoplanètes avec l'instrument SPIRou au Téléscope Canada-France-Hawaï
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".