Identifying new opportunities for exoplanet characterisation at high spectral resolution
Bibliographic record
Abstract
Context. Recently, there have been a series of detections of molecules in the atmospheres of extrasolar planets using high spectral resolution (R ~ 100 000) observations, mostly using the CRyogenic high-resolution InfraRed Echelle Spectrograph (CRIRES) on the Very Large Telescope. These measurements are able to resolve molecular bands into individual absorption lines. Observing many lines simultaneously as their Doppler shift changes with time allows the detection of specific molecules in the atmosphere of the exoplanet. Aims. We aim to identify new ways of increasing the planet signal in these kinds of high-resolution observations. First of all, we wish to determine what wavelength settings can best be used to target certain molecules. Furthermore, we want to simulate exoplanet spectra of the day-side and night-side to see whether night-side observations are feasible at high spectral resolution. Methods. We performed simulations of high-resolution CRIRES observations of a planet’s thermal emission and transit between 1 and 5 μm and performed a cross-correlation analysis on these results to assess how well the planet signal can be extracted. These simulations take into account telluric absorption, sky emission, realistic noise levels, and planet-to-star contrasts. We also simulated day-side and night-side spectra at high spectral resolution for planets with and without a day-side temperature inversion, based on the cases of HD 189733b and HD 209458b. Results. Several small wavelength regions in the L-band promise to yield cross-correlation signals from the thermal emission of hot Jupiters of H2O, CH4, CO2, C2H2, and HCN that can exceed those of the current detections by up to a factor of 2–3 for the same integration time. For transit observations, the H-band is also attractive, with the H, K, and L-bands giving cross-correlation signals of similar strength. High-resolution night-side spectra of hot Jupiters can give cross-correlation signals as high as the day-side, or even higher. Conclusions. We show that there are many new possibilities for high-resolution observations of exoplanet atmospheres that have expected planet signals at least as high as those already detected. Hence, high-resolution observations at well-chosen wavelengths and at different phases can improve our knowledge about hot Jupiter atmospheres significantly, already with currently available instrumentation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".