Impact of oxygen fugacity on the atmospheric structure and emission spectra of ultra-hot rocky exoplanets
Bibliographic record
Abstract
Context. Atmospheres above lava-ocean planets (LOPs) hold clues related to the properties of their interiors, based on the expectation that the two reservoirs are in chemical equilibrium. Furthermore, such atmospheres are observable with current-generation space- and ground-based telescopes. While efforts have been made to understand how emission spectra are related to the composition of the lava ocean, the influence of oxygen fugacity has yet to be examined in a self-consistent way. Aims. Here, we investigate the sensitivity of atmospheric emission spectra of LOPs to key geochemical parameters, namely, temperature (T), composition (X), and oxygen fugacity (fO2). We also consider the precision involved in recovering these spectra from observations of hot, rocky exoplanets. Methods. We considered ‘mineral’ atmospheres produced in equilibrium with silicate liquids. We treated fO2 as an independent variable, together with T and X, to compute equilibrium partial pressures (p) of stable gas species at the liquid-gas interface. Above this boundary, the atmospheric speciation and the pressure–temperature structure are computed self-consistently to yield emission spectra. We explored a wide array of plausible compositions, oxygen fugacities (between 6 log10 units below and above the iron-wüstite buffer, IW), and irradiation temperatures (2000, 2500, 3000, and 3500 K) relevant to LOPs. Results. We find that SiO(g), Fe(g) and Mg(g) are the major species below ~IW, ceding to O2(g) and O(g) in more oxidised atmospheres. The transition between the two regimes demarcates a minimum in total pressure (P). Because p scales linearly with X, emission spectra are only modest functions of composition. By contrast, fO2 can vary over orders of magnitude, thereby causing commensurate changes in p. Atmospheres outgassed from reducing melts exhibit intense SiO emission, creating a temperature inversion in the upper atmosphere. Conversely, oxidised atmospheres have lower pSiO and lack thermal inversions, with their resulting emission spectra mimicking that of a black-body. Consequently, the intensity of SiO emission relative to the background, generated by MgO(g), can be used to quantify the fO2 of the atmosphere. Depending on the emission spectroscopy metric of the target, deriving the fO2 of known nearby LOPs is possible with a few secondary occultations observed by JWST.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".