Are there Spectral Features in the MIRI/LRS Transmission Spectrum of K2-18b?
Bibliographic record
Abstract
Determining the composition of an exoplanet atmosphere relies on the presence of detectable spectral features. The strongest spectral features, including DMS, look approximately Gaussian. Here, I perform a suite of Gaussian feature analyses to find any statistically significant spectral features in the recently published MIRI/LRS spectrum of K2-18b (N. Madhusudhan et al. 2025). In N. Madhusudhan et al. 2025, they claim a 3.4-$σ$ detection of spectral features compared to a flat line. In 5 out of 6 tests, I find the data preferred a flat line over a Gaussian model, with a $χ^{2}_ν$ of 1.06. When centering the Gaussian where the absorptions for DMS and DMDS peak, I find ln(B) = 1.21 in favour of the Gaussian model, with a $χ^{2}_ν$ of 0.99. With only $\sim$2-$σ$ in favour of Gaussian features, I conclude no strong statistical evidence for spectral features.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".