Constraining Exoplanet Metallicities and Aerosols with the Contribution to ARIEL Spectroscopy of Exoplanets (CASE)
Bibliographic record
Abstract
Launching in 2028, ESA's 0.64 m 2 Atmospheric Remote-sensing Exoplanet Large-survey (ARIEL) survey of ∼1000 transiting exoplanets will build on the legacies of NASA's Kepler and Transiting Exoplanet Survey Satellite ( TESS ), and complement the James Webb Space Telescope ( JWST ) by placing its high-precision exoplanet observations into a large, statistically significant planetary population context. With continuous 0.5–7.8 μ m coverage from both FGS (0.5–0.6, 0.6–0.81, and 0.81–1.1 μ m photometry; 1.1–1.95 μ m spectroscopy) and AIRS (1.95–7.80 μ m spectroscopy), ARIEL will determine atmospheric compositions and probe planetary formation histories during its 3.5 yr mission. NASA's proposed Contribution to ARIEL Spectroscopy of Exoplanets (CASE) would be a subsystem of ARIEL's Fine Guidance Sensor (FGS) instrument consisting of two visible-to-infrared detectors, associated readout electronics, and thermal control hardware. FGS, to be built by the Polish Academy of Sciences Space Research Centre, will provide both fine guiding and visible to near-infrared photometry and spectroscopy, providing powerful diagnostics of atmospheric aerosol contribution and planetary albedo, which play a crucial role in establishing planetary energy balance. The CASE team presents here an independent study of the capabilities of ARIEL to measure exoplanetary metallicities, which probe the conditions of planet formation, and FGS to measure scattering spectral slopes, which indicate if an exoplanet has atmospheric aerosols (clouds and hazes), and geometric albedos, which help establish planetary climate. Our simulations assume that ARIEL's performance will be 1.3 × the photon-noise limit. This value is motivated by current transiting exoplanet observations: Spitzer /IRAC and Hubble /WFC3 have empirically achieved 1.15 × the photon-noise limit. One could expect similar performance from ARIEL, JWST , and other proposed future missions such as HabEx, LUVOIR, and Origins. Our design reference mission simulations show that ARIEL could measure the mass–metallicity relationship of its 1000-planet single-visit sample to >7.5 σ and that FGS could distinguish between clear, cloudy, and hazy skies and constrain an exoplanet's atmospheric aerosol composition to ≳5 σ for hundreds of targets, providing statistically transformative science for exoplanet 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".