Constraining the atmospheric elements in hot Jupiters with Ariel
Bibliographic record
Abstract
ABSTRACT One of the main objectives of the European Space Agency’s Ariel telescope (launch 2029) is to understand the formation and evolution processes of a large sample of planets in our Galaxy. Important indicators of such processes in giant planets are the elemental compositions of their atmospheres. Here we investigate the capability of Ariel to constrain four key atmospheric markers: metallicity, C/O, S/O, and N/O, for three well-known, representative hot-Jupiter atmospheres observed with transit spectroscopy, i.e. HD 209458b, HD 189733b, and WASP-121b. We have performed retrieval simulations for these targets to verify how the planetary formation markers listed above would be recovered by Ariel when observed as part of the Ariel Tier 3 survey. We have considered eight simplified different atmospheric scenarios with a cloud-free isothermal atmosphere. Additionally, extra cases were tested to illustrate the effect of C/O and metallicity in recovering the N/O. From our retrieval results, we conclude that Ariel is able to recover the majority of planetary formation markers. The contributions from CO and CO2 are dominant for the C/O in the solar scenario. In a C-rich case, C2H2, HCN, and CH4 may provide additional spectral signatures that can be captured by Ariel. In our simulations, H2S is the main tracer for the S/O in hot-Jupiter atmospheres. In the super-solar metallicity cases and the cases with C/O > 1, the increased abundance of HCN is easily detectable and the main contributor to N/O, while other N-bearing species contribute little to the N/O in the investigated atmospheres.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".