Solar Occultation FTIR Spectrometry at Mars for Trace Gas Detection: A Sensitivity Study
Bibliographic record
Abstract
Abstract A sensitivity study has been performed to estimate detection limits of various atmospheric trace gases achievable by a Mars‐orbiting solar occultation Fourier transform infrared (FTIR) spectrometer. This was accomplished by first computing realistic limb transmittance spectra based on a model (T, P, VMR, and dust profiles) of the Mars atmosphere and adding appropriate noise and systematic errors based on assumed instrument design/configuration/performance. We then performed spectral fits to the resulting synthetic spectra to derive slant column abundances and their uncertainties. A profile retrieval was performed to infer limits of detection. This methodology was applied to a Mars‐orbiting FTIR solar occultation spectrometer covering the 850–4,300 cm−1 spectral region at 0.025‐cm−1 resolution. We conclude that most gases can be retrieved with a single‐occultation sensitivity of 20–100 ppt. But this sensitivity varies considerably with the dust loading, especially for gases whose strongest absorption bands are toward higher wavenumbers where scattering is large. We conclude that for CH4, the ν4 band centered at 1,305 cm−1, despite being more than 2 times weaker than the ν3 band centered at 3,015 cm−1, offers better sensitivity due to its close spectral proximity to the dust extinction minimum. We also conclude that for the purpose of CH4 detection, a high‐resolution (0.025 cm−1) broadband instrument would have a substantial advantage over a medium‐resolution (0.15 cm−1) instrument, despite the latter having a much larger signal‐to‐noise ratio.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".