Simulated<i>JWST</i>/NIRISS Transit Spectroscopy of Anticipated Tess Planets Compared to Select Discoveries from Space-based and Ground-based Surveys
Bibliographic record
Abstract
The Transiting Exoplanet Survey Satellite (TESS) will embark in 2018 on a 2 year wide-field survey mission, discovering over a thousand terrestrial, super-Earth and sub-Neptune-sized exoplanets ( ) potentially suitable for follow-up observations using the James Webb Space Telescope ( JWST ). This work aims to understand the suitability of anticipated TESS planet discoveries for atmospheric characterization by JWST 's Near InfraRed Imager and Slitless Spectrograph (NIRISS) by employing a simulation tool to estimate the signal-to-noise (S/N) achievable in transmission spectroscopy. We applied this tool to Monte Carlo predictions of the TESS expected planet yield and then compared the S/N for anticipated TESS discoveries to our estimates of S/N for 18 known exoplanets. We analyzed the sensitivity of our results to planetary composition, cloud cover, and presence of an observational noise floor. We find that several hundred anticipated TESS discoveries with radii will produce S/N higher than currently known exoplanets in this radius regime, such as K2-3b or K2-3c. In the terrestrial planet regime, we find that only a few anticipated TESS discoveries will result in higher S/N than currently known exoplanets, such as the TRAPPIST-1 planets, GJ1132b, and LHS1140b. However, we emphasize that this outcome is based upon Kepler -derived occurrence rates, and that co-planar compact multi-planet systems (e.g., TRAPPIST-1) may be under-represented in the predicted TESS planet yield. Finally, we apply our calculations to estimate the required magnitude of a JWST follow-up program devoted to mapping the transition region between hydrogen-dominated and high molecular weight atmospheres. We find that a modest observing program of between 60 and 100 hr of charged JWST time can define the nature of that transition (e.g., step function versus a power law).
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".