Performance of Longitudinally Resolved Spectral Retrieval for Spectroscopic Phase Curves.
Bibliographic record
Abstract
Poster presented at Exoclimes VI. Contact: jared.splinter@mail.mcgill.ca Abstract: Previous implementations of 1D exoplanet spectral retrieval use the disk integrated retrieval method, which assumes the planet’s atmosphere is spherically symmetric. phase curves on a 2D framework has shown to provide more accurate and self-consistent results. Unfortunately, this comes with an increased trade-off of increased model complexity and increased parameter space that can make results more difficult to interpret and validate. As planets are intrinsically 3D in nature, we would prefer a retrieval approach that is not too simple to miss important aspects of the atmosphere, while also not being not too complicated to make fitting and explanation more difficult. Exoplanet phase observations can be fitted with a Fourier series that can be analytically converted to longitudinal maps. By treating each wavelength independently, this will produce a longitudinal map of an exoplanet at each wavelength. This in turn, creates longitudinally resolved spectra capable of conducting retrieval on spectra from longitudinal slices of the planet which can be fit to pre-existing 1D spectral retrieval codes. This work aims to compare the performance of longitudinally resolved and disk integrated spectral retrieval methods on synthetic spectra of HD 189733b and establish a recommendation for future exoplanet observations with JWST and ARIEL.
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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 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".